Engineering Geophysics
Engineering Geophysics connects onshore geotechnical engineering challenges to the
geophysical methods that may be applied to solve them. Unknown geological conditions are
a risk in construction projects, and geophysical information can help to identify those risks.
The book answers questions on how, why, and when the individual and combined methods
provide the results requested. Flowcharts guide the reader to geophysical methods that can
be applied for various engineering challenges, and the solutions are illustrated with practical
case histories.
The book is intended mainly for geotechnical engineers and geologists but also for
geophysicists or managers in need of an overview or brushup on geophysical methods and
their practical applications. In addition, it can be used by educational institutions in courses
both for geotechnical engineers and geologists.
Engineering Geophysics
Edited by
Anna Bondo Medhus and Lone Klinkby
Cover image:Anna Bondo Medhus and Lone Klinkby
First published 2023
by CRC Press/Balkema
Schipholweg 107C, 2316 XC Leiden,The Netherlands
e-mail: enquiries@taylorandfrancis.com
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CRC Press/Balkema is an imprint of the Taylor & Francis Group, an informa business
© 2023 selection and editorial matter,Anna Bondo Medhus and Lone Klinkby; individual
chapters, the contributors
The right of Anna Bondo Medhus and Lone Klinkby to be identifed as the authors of the
editorial material, and of the authors for their individual chapters, has been asserted in
accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced or utilised in any
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information herein, no responsibility is assumed by the publishers nor the author for any
damage to the property or persons as a result of operation or use of this publication and/
or the information contained herein.
Library of Congress Cataloging-in-Publication Data
Names: Medhus,Anna Bondo, editor. | Klinkby, Lone, editor.
Title: Engineering geophysics / edited by Anna Bondo Medhus, Lone Klinkby.
Description: Boca Raton : CRC Press, [2023] | Includes bibliographical
references and index.
Identifers: LCCN 2022022719 (print) | LCCN 2022022720 (ebook) |
ISBN 9781032026886 (hbk) | ISBN 9781032026985 (pbk) |
ISBN 9781003184676 (ebk)
Subjects: LCSH: Engineering geology. | Geophysics.
Classifcation: LCC TA705 .E526 2023 (print) | LCC TA705 (ebook) |
DDC 624.1/51—dc23/eng/20220908
LC record available at https://lccn.loc.gov/2022022719
LC ebook record available at https://lccn.loc.gov/2022022720
ISBN: 978-1-032-02688-6 (hbk)
ISBN: 978-1-032-02698-5 (pbk)
ISBN: 978-1-003-18467-6 (ebk)
DOI: 10.1201/9781003184676
Typeset in Times New Roman
by Apex CoVantage, LLC
Contents
Editors
List of contributors
Preface
Acknowledgments
1
Introduction
xi
xiii
xv
xvii
1
LONE KLINKBY AND ANNA BONDO MEDHUS
2
Scope of work
21
ANNA BONDO MEDHUS AND LONE KLINKBY
3
Relationship between geotechnical and geophysical methods
29
MICHAEL LONG
4
Gravimetric method
53
OLIVIER FRANCIS
5
Magnetometry method
59
J O A N N A H A M LY N
6
Direct current resistivity methods
67
D O U G L A S W. O L D E N B U R G , S E O G I K A N G , L I N D S E Y J . H E A G Y,
AND MICHAEL MAXWELL
7
Electromagnetic methods
77
NIELS BØIE CHRISTENSEN
8
Ground penetrating radar
87
M AT T E O R O S S I
9
Refection seismic methods
RICHARD D. MILLER
95
vi
Contents
10 Seismic refraction methods
107
C O L I N A . Z E LT
11 Surface wave methods
117
J U L I A N I VA N O V
12 Case: mapping potential unexploded ordnance (UXO)
129
J O A N N A H A M LY N
13 Case: geophysical investigation to delineate landfll
133
J O A N N A H A M LY N
14 Case: 3D GPR in the inner yard at Frederiksborg Castle
137
OLE FRITS NIELSEN AND SØREN WEDEL NIELSEN
15 Case: mapping of utilities when developing at an old coal storage facility 141
PETER THOMSEN
16 Case: near-surface electromagnetic survey to support the design
of urban development plans
145
PETER THOMSEN
17 Case: archaeological investigation to identify a Romano-British
farmstead using magnetic gradiometry
149
J O A N N A H A M LY N
18 Case: integrated geophysical survey to locate buried structures
151
J O A N N A H A M LY N
19 Case: total feld magnetometry to locate buried foundations
155
J O A N N A H A M LY N
20 Case: utility mapping with GPR at Copenhagen Harbour
157
OLE FRITS NIELSEN AND SØREN WEDEL NIELSEN
21 Case: thickness of peat and depth to bedrock for road
construction using ground penetrating radar
161
J A A N A G U S TA F S S O N
22 Case: multidisciplinary geophysical investigation for a new
railway track in Norway
R O G E R W I S É N , A N D E R S A L M H O LT, A N D F R E D R I K O L S E N
165
Contents
23 Case: road maintenance and ground frost
vii
171
J A A N A G U S TA F S S O N
24 Case: depth to bedrock detection by integration of airborne EM
data with sparse geotechnical drilling data for early phase road
alignment
175
C R A I G W. C H R I S T E N S E N , G U R O H . S K U R D A L , A N D I A . P FA F F H U B E R ,
A N D M A RT I N PA N Z N E R
25 Case: delineation of aggregates gravels, sands, and silts using
electrical resistivity tomography
181
CHRISTOPHER PHILLIPS
26 Case: delineation of material type for use in ready-mix concrete
185
B A R RY B A L D I N G
27 Case: mapping railroad ballast and geology using ground
penetrating radar
187
J A A N A G U S TA F S S O N
28 Case: assessing loose soils for tower cable anchors using
electrical resistivity
191
FERN WEBB
29 Case: delineation of soft soils and bedrock depth using
integrated methods
195
MICHAEL MAXWELL
30 Case: high-defnition bedrock depth and conditions for urban
construction project site evaluation in Switzerland using seismic
refraction with combined GRM and tomographic approach
201
P I E R V I T T O R I O R A D O G N A , C H R I S T O P H E V E R G N I A U LT, A N D D AV I D D U P U Y
31 Case: paleo-channel investigation for seepage pathway potentials
205
DANIEL E. KRAMER
32 Case: near-surface electromagnetic survey to support the design
of climate adaptation in urban development plans
209
PETER THOMSEN
33 Case: geophysical investigation of slope stability using electrical
resistivity tomography, seismic refraction, and surface waves
J O A N N A H A M LY N
213
viii
Contents
34 Case: integrated geophysical investigation to map a landslip
217
J O A N N A H A M LY N
35 Case: mapping of quick clay risk by electrical resistivity
tomography (ERT)
221
TORLEIF DAHLIN
36 Case: quick clay volume delineation based on AEM resistivity,
geotechnical soundings, and lab samples
227
C R A I G W. C H R I S T E N S E N , A N D I A . P FA F F H U B E R , G U R O H . S K U R D A L ,
A N D M A RT I N PA N Z N E R
37 Case: depth to bedrock and weak zone detection for tunnel
design under water passages
233
T O R L E I F D A H L I N , M AT H I A S R O N C Z K A , A N D R O G E R W I S É N
38 Case: 3D model of depth to bedrock for a new train tunnel under
the capital of Norway
239
F R E D R I K O L S E N , A N D E R S A L M H O LT, A N D R O G E R W I S É N
39 Case: screening for ground risk ahead of tunnel design and
construction activities
243
J A M E S R U S S E L L A N D E D WA R D C O X
40 Case: geometrical complex ground model for large industrial
construction sites: ultra-high-resolution with shear waves –
qualifcation fow and application
249
P I E R V I T T O R I O R A D O G N A A N D C H R I S T O P H E V E R G N I A U LT
41 Case: identifying weakness zones and geological boundaries
across tunnel alignments using airborne electromagnetics
255
A N N E H . R A S M U S S E N , G I S E L L E M . L I N A R E S , C R A I G W. C H R I S T E N S E N ,
A N D M A RT I N PA N Z N E R
42 Case: delineation of soil type, dam leakage, underground voids,
and water fow in tunnels
261
M I C H A E L M A X W E L L , R O B E RT E S O , A N D B R O D I E K L U E
43 Case: pre-investigations for horizontal directional drilling
in Copenhagen
OLE FRITS NIELSEN, SØREN WEDEL NIELSEN, AND
S Ø R E N S T R I C K E R M AT H I A S E N
271
Contents
44 Case: mapping depth to bedrock along a planned cable route
ix
275
BO BERGMAN, DANIEL JUUL OKHOLM, AND ANDERS EDSEN
45 Case: mapping bedrock profles for cable landings using seismic
refraction and surface waves (MASW)
279
J O A N N A H A M LY N
46 Case: lake bottom investigations with ground penetrating
radar (GPR)
283
J A A N A G U S TA F S S O N
47 Case: pre-investigations for pipeline crossing of a stream
287
OLE FRITS NIELSEN, SØREN WEDEL NIELSEN, AND SØREN
S T R I C K E R M AT H I A S E N
48 Case: delineation of palaeokarst features under a proposed
tailings facility using ERT, seismic refraction, and micro-gravity
291
B A R RY B A L D I N G
49 Case: the identifcation of leaks in tailings storage facility
impoundment dam walls using ERT and IP
295
B A R RY B A L D I N G
50 Case: groundwater vulnerability assessment for new motorway
using ERT
299
TORLEIF DAHLIN, ELISABET HAMMARLUND, AND ROGER WISÉN
Index
303
Editors
Anna Bondo Medhus holds a PhD in seismology from the
Department of GeoScience, Aarhus University, 2010. In her
current position at Energinet, Anna works with pre-investigations
related to energy transmission projects and renewable energy.
Anna was a researcher and research program manager at VIA
University College (2018–2022) in the Center of Applied Research
and Development in Building, Energy and Environment (Horsens,
Denmark). She managed research projects mainly related to preinvestigation for urban development projects and in collaboration
with several partners like consultancy companies and utilities.
She developed and taught a course in geophysics for engineering
students, which provided insight on the needs of engineers and
how to teach students from other professions. From 2010 to 2018, Anna was employed in
COWI as a specialist in applied geophysics working with site investigations for various
construction and foundation projects in nationwide and international project groups.
She has led work on optimising data access and data management throughout COWI’s
geoscience, groundwater and geotechnical groups. Anna has always had a focus on the
dissemination of knowledge with a keen eye for the interest, needs and starting point of the
audience. Her art is to make difficult topics easily understandable without compromising
on facts.
Lone Klinkby holds a PhD in deep reflection seismics from the
Department of Earth Sciences at the University of Aarhus, 1998.
Currently Lone works in COWI as Project Director and Business
Development Director within Energy and Geoscience. From 2008
to 2011 she worked within Research and Development at Vattenfall
with a focus on deep CO₂ storage demonstration projects in Denmark
and Germany. Further, she participated in international scientist
projects and networks on CO₂ storage guidance and monitoring.
Former positions include research on reservoir geophysical data at
the Geological Survey of Denmark and Greenland (1998–2005).
COWI is an international consulting group within the engineering
disciplines of energy, buildings, infrastructure, and environment. Lone is part of a group
of geophysicists and geologists working across departments in close connection with
xii
Editors
COWI’s geotechnical groups. Since 2011, she has worked with applied geophysics for
projects related to deep and shallow foundations both on- and offshore. She is an active
participant in international project groups, and her work includes leading combined projects
on pre-investigations and for establishing decision foundations for the transition toward
renewable energy. Lone works in the interface between professions and strive for increasing
interpersonal understanding for the benefit of the projects.
Contributors
Anders Almholt, Impakt Geofysik AB
Barry Balding, WSP Golder Associates Ltd., now at SLR Consulting Ltd
Bo Bergman, WSP
Craig W. Christensen, Emerald Geomodelling
Niels Bøie Christensen, Aarhus University
Edward Cox, FUGRO
Torleif Dahlin, Engineering Geology, Lund University
David Dupuy, Geo2x
Anders Edsen, WSP
Robert Eso, WSP Golder Associates Ltd. and UBC-GIF
Olivier Francis, University of Luxembourg
Jaana Gustafsson, Guideline Geo AB
Joanna Hamlyn, TerraDat UK Ltd
Elisabet Hammarlund, Tyréns AB
Lindsey J. Heagy, Department of Earth Ocean and Atmospheric Sciences at the University
of British Columbia
Julian Ivanov, Kansas Geological Survey, University of Kansas
Seogi Kang, Stanford University, Department of Geophysics
Lone Klinkby, COWI Denmark
Brodie Klue, WSP Golder Associates Ltd.
Daniel E. Kramer, Petralogix Engineering Inc.
Giselle M. Linares, Emerald Geomodelling
Michael Long, University College Dublin, School of Civil Engineering
Michael Maxwell, WSP Golder Associates Ltd. and UBC-GIF
xiv
Contributors
Anna Bondo Medhus, Energinet
Richard D. Miller, Kansas Geological Survey, University of Kansas
Ole Frits Nielsen, COWI Denmark
Søren Wedel Nielsen, COWI Denmark
Daniel Juul Okholm, WSP
Douglas W. Oldenburg, Department of Earth Ocean and Atmospheric Sciences at the University of British Columbia
Fredrik Olsen, Impakt Geofysik AB
Martin Panzner, Emerald Geomodelling
Andi A. Pfaffhuber, Emerald Geomodelling
Christopher Phillips, WSP Golder Associates Ltd.
Pier Vittorio Radogna, Geo2x
Anne H. Rasmussen, Emerald Geomodelling
Mathias Ronczka, Leibnitz Institute of Applied Geophysics
Matteo Rossi, Engineering Geology, Lund University
James Russell, FUGRO
Guro H. Skurdal, Emerald Geomodelling
Søren Stricker Mathiasen, Energinet
Peter Thomsen, Rambøll A/S
Christophe Vergniault, EDF-DI-TEGG
Fern Webb, WSP Golder Associates Ltd.
Roger Wisén, Impakt Geofysik AB
Colin A. Zelt, Rice University, Department of Earth, Environmental and Planetary Sciences
Preface
The challenges accepted by the engineering society are enormous and constantly expanded
by mega trends in the society. The need for solutions for renewable energy, for adaptation to
climate changes, and for sustainable infrastructure and other constructions call for innovative thinking and increased cooperation between professions. This book presents how the
geophysical mapping of soil layers can be part of the solutions, when applied with all the
skills and care that the contributors to this book represent.
The challenges and solutions presented in this book are generally some that the editors
have met and discussed during many years of working with pre-investigations for construction and installation projects. Working closely with geotechnical and design engineers has
convinced us that we need to know what questions are important before jumping to conclusions. A geophysical approach of using methods we ourselves are most attached to or the
method for which we have the tools in the basement does not always provide the best solutions for the projects.
Working with engineering projects, the questions and assumptions on geophysics we meet
are challenging, intriguing, and very interesting. Often, we are met with requests because
borehole investigations are not available or possible due to site conditions. Subsurface site
conditions challenge projects, and, as a last resort, someone decides to ask the geophysicist
for suggestions. Geophysical methods, unfortunately, seldom flourish as a problem solvers.
Geophysical methods are best used and cheapest to apply early in project life as a screening
tool for soil conditions and potential hazards, thereby providing a solid basis for decisions
in the earlier phases of a project. We have also met a fair number of engineers who are very
sceptical or who even oppose the use of geophysical methods in their projects. This presumably bad reputation of the geophysical methods is most often related to misunderstandings
regarding the resolution power of geophysical methods. Projects where geophysical methods have been used inappropriately or where unrealistic promises for results have been made
are not unheard of either.
While working on this book, we have been so fortunate as to discuss and learn from specialists and practitioners around the world. The efforts put forth toward solving engineering
and related challenges are truly amazing and multifaceted. We are embarrassingly aware that
this book by no means covers all the topics, methods, or solutions that the concept of “engineering geophysics” can encircle. Further, this book has its home base in the Scandinavian
tradition for engineering as well as geophysics. Viewpoints and solutions other than those
described in this book can easily be found in the literature, practical operations, and other
geographies.
xvi
Preface
This book aims to increase the cooperation among geotechnical engineers, geologists,
and geophysicists by providing information on geophysical methods and by addressing both
“how” and “where” to use them and also “when”. Eventually, we hope that engineering geophysics will be considered as a natural part of the site investigations for all onshore engineering projects. For this to succeed, the methods must be applied with due respect to the physics
behind them. Geophysical methods are non-unique, meaning that what is measured in the
geophysical data can be caused by more than one reality. Therefore, inversion techniques are
thoroughly introduced as part of the introductory chapter.
The book offers solid overviews of the individual geophysical methods, and a full chapter
is devoted to the relationship between the geophysical and geotechnical methods. Finally,
a generous number of case histories illustrate the practical solutions applied for different
purposes and in different geographies.
We hope you will enjoy the reading
Best regards,
Lone and Anna
www.engineering-geophysics.com/
Acknowledgements
This book was no book without all the contributors and coauthors. We are very grateful to
all of them for the hours, enthusiasm and years of experience condensed into the contributions we have received. We cannot express our gratitude enough for their contributions to
this project.
We also wish to thank the clients and customers for allowing the publication of their data
and results for this book.
The following reviewers provided valuable comments raising the quality of sections in
this book:
Carsten Steen Sørensen and David Ross Alvin for respectively their reviews and early
discussions on the geotechnical section
Tim Niebauer for the section “Gravimetry Method”
Adam D. Booth for the section “Magnetometry Method”
Torleif Dahlin for the section “Direct Current Resistivity Method”
Esben Auken for the section “Electromagnetic Methods”
Lars Nielsen for the section “Ground Penetrating Radar”
Søren Wedel Nielsen for support on reviewing the section “Seismic Reflection”
Hansruedi Maurer for the section “Seismic Refraction Methods”
Roger Wisén for the section “Surface Wave Methods”
Niels Bøie Christensen for the cases employing electromagnetic methods
Matteo Rossi for cases using ground penetrating radar
Søren Wedel Nielsen for the cases using magnetometry
For the remaining cases, we split the roles of editor and reviewer between us.
Many professionals have given us input and support for this project, we thank them all.
Special thanks are given to Torben Lund Skovhus for input on book editing in the early
phase of this project; to Christopher Jack for valuable input on the section related to engineering geology; to Maria Riis and Lea Bondo Medhus for the production of nice figures;
to Lisbeth Lyngkjær Pedersen for handling the formatting of references. Grateful thanks to
Doug and his team for taking on a second challenge and writing the section on inversion; to
Joanna Hamlyn for always stepping in when needed; to Michael Long for patiently providing us with insight into the world of geotechnical engineering.
Our families have supported us through the process and never complained about the endless talk about the project, meetings at dinnertime and too many late-night working hours.
Thanks to our husbands Anders and Jesper for creative support. We are grateful to Jesper for
xviii
Acknowledgements
sharing our enthusiasm and for building the homepage (www.engineering-geophysics.com/)
for the book project. Thanks also for all the chocolate and coffee served and for taking over
many household chores. Thanks to our children Lea, Elin, Vitus, Jeppe, Emma, and Ninna
for accepting less available mothers and for being who they are.
This book is inspired by all the colleagues in geology, geophysics, and geotechnical engineering whom we have met since 2010 in different working constellations whether at COWI,
VIA University College or clients, partners and organizations. We dedicate this book to the
geotechnical engineers, geophysicists, and geologists we are working with on an everyday
basis. They amaze us and inspire us with their energy for challenges and their striving for
quality.
Chapter 1
Introduction
Lone Klinkby and Anna Bondo Medhus
This book presents how onshore engineering challenges may be addressed through application of geophysical methods. Geophysical methods measure different physical properties of the earth. The methods presented here are primarily performed from the earth’s
surface. By sending a signal into the ground and measuring how the earth is responding,
information is gathered and can be interpreted with due respect to the physics involved.
There are many different geophysical methods, and their physics differs considerably.
The engineering soil properties of strength and stiffness are not simply related to any of the
methods. Still both geotechnical engineers, geologists and geophysicists aim to describe
the subsurface distribution of soils. Thus, working together, the disciplines can provide a
more complete understanding of the subsurface soils and a stronger foundation for construction works.
This book aims to bring the disciplines closer together, providing an overview of the
methods and applications for engineering disciplines. For geologists working in engineering
groups, the book can be used to understand the different methods and suggest the applications where relevant. Further, at the end of his or her formal education, a geophysicist is
often mostly an expert within one of the groups of methods, and this book intends to assist
in keeping the general overview.
Geophysicists, geologists, and geotechnical engineers use different wording and abbreviations, sometimes even when describing exactly the same conditions. This is a challenge as
misunderstandings prevailing in a project will easily cause a lack of overview of potential
hazards and related risks. This book offers a better understanding for geotechnical engineers
of the geophysical vocabulary as well as increased insight for geologists and geophysicists
of the needs and perspectives of geotechnical engineering.
Professionals identifying themselves as geophysicists may have different backgrounds
from engineering schools or universities with different focus, e.g., geology or physics. Here,
we informally define engineering geophysics as the use of geophysical methods to solve
engineering challenges for foundation design.
1.1
FRAMING AND LIMITATIONS
This book is intended for engineers planning site investigations and construction projects
onshore and for geologists involved in such projects. Furthermore, it can be used by geophysicists, geologists, or managers in need of an overview of the best geophysical methods
to apply within a specific engineering discipline.
DOI: 10.1201/9781003184676-1
2
Engineering Geophysics
The applications and methods described in this book are by no means exhausting
or fulfilling. The book focuses on methods practically available to the industry. Thus
prototypes developed at universities that are not yet commercially available are not
covered; neither are methods superseded by more recent methods and methods only
rarely applied. Similarly, methods that might theoretically be applied but would never
be selected because much cheaper alternatives are available are not included either. The
focus of the book is to suggest a guideline including reasonable, doable, and reliable
investigations.
The book does not cover offshore surveying methods including cable tracking and remote
sensing. The related topic of mining and environmental geophysics and resource mapping
are not covered in the book either; other books cover these topics well (see Section 1.2). The
essential topics of borehole geophysics and seismic site characterisation are not covered well
in this book, despite the high relevance for geotechnical engineering. A few references can
be found in Section 1.2.4.
The cases described in this book cover wide geography, from California, the United States,
via the United Kingdom to Central Europe, but with the majority found in a Scandinavian
setting. As geography matters for geology and as geological setting matters for the application of geophysical methods, the book is biased toward the Scandinavian solutions.
1.1.1
How to use this book
The introductory chapter of the book covers general topics common to all geophysical disciplines and provides a framing of the book with an outlook toward interfacing areas. Central
in the introduction is a section on geophysical inversion. Inversion techniques are part of
the basis for applied geophysics and must be considered the territory of the geophysicist.
The section here offers a thorough overview and increased understanding of how results are
obtained throughout the book.
The second chapter invites the reader to start from their type of construction project and
visit the flowchart representing that discipline. The flowchart lists topics related to the discipline where geophysical methods may potentially aid in resolving or reducing project risks.
Each listed topic leads to one or more suggested geophysical methods that can reduce uncertainties. Some boxes in the flowchart relate to the expected geology, others to specific targets. Some of the geophysical methods listed are supplied with comments suggesting when
they should be applied or circumstances under which other methods should be selected. All
methods have a reference to a theory chapter with a thorough overview. References to one
or more cases are given, thereby illustrating the method’s capabilities suggested in practical operation. In this way the flowcharts are a guide to the parts of the book of particular
relevance to a specific engineering discipline.
The third chapter comes in two main parts. The first part contains an overview of geotechnical parameters intended to provide geophysicists and geologists with increased understanding of the focus and needs of the geotechnical engineer. The second part offers perspectives
on some of the most promising relations between geotechnical and geophysical parameters
in the onshore environment. This section is the most academic part of the book. It is included
for the interested reader and in order to highlight the potential for further and more direct
collaboration in the future.
The method chapters (Chapters 4–11) comprise short introductions to the fundamental
theory, planning considerations, opportunities, limitations, and resolution of the various
Introduction
3
geophysical methods suggested throughout the book. The sections provide an understanding enabling the reader to decide whether the method under consideration fits the intended
purpose. These are the sections referred to in the flowcharts for the reader curious about the
theory of a method. To assure the quality of these sections, they are mainly written by academic profiles within the discipline.
The case histories (Chapters 12–50) are referenced in the flowcharts and in the method
sections. The cases include practical survey and planning considerations along with examples of interpretations and results. The cases even provide examples of where the methods
were less successful. The case section aims to cover the most common applications of the
different methods for the engineering disciplines included in the book. The examples are
intentionally selected as typical rather than as the most successful applications, thereby providing the reader with realistic expectations for the outcome of a survey.
1.1.2
Reservations
Please note that this book’s works and views should not be considered instructions and are
subject to different opinions from specialists and practitioners other than the editors and
contributors. Only the editors are responsible for the book in its entire form and therefore for
any inconsistencies between chapters and sections. Careful review has been performed of
the individual chapters, but the authors have had the final say on the contents.
Further, although all care is taken to ensure integrity and the quality of this publication
and the information herein, no responsibility is assumed by the publishers or the authors for
any damage to property or persons as a result of operation or use of this publication and/or
the information contained herein.
1.2
INTERFACES TO RELATED DISCIPLINES
1.2.1
Engineering geology
Engineers and geologists have a long-shared history in working with the subsurface, and
the field of engineering geology is the embodiment of this history. Engineering geology
can be defined as the scientific discipline concerned with applying geological knowledge to
engineering challenges. As an important complement to this, engineering geophysics is an
essential part of the engineering geologist’s toolbox.
In many engineering companies, you will find a geologist or two in various departments
and an even larger number in the tunnelling department. They can often be recognised by the
rock collection on their shelf!
Engineering geologists are skilled in assessing the soil and rock conditions of a site and
advising on the engineering implications and challenges posed by the ground conditions.
One reason for the higher number of geologists on tunnel projects is the higher level of
geological risk typically involved in these projects. These risks are not easily mitigated
by simply adding more boreholes to the ground investigation: what is needed first is a
robust engineering geological model to guide the investigation. Linear projects such as
tunnels need a detailed understanding of geological history, combined with mapping of
aerial imagery and surface outcrops (where available), ideally together with geophysical
investigation for increased three-dimensional understanding, ground characterization and
parameterization.
4
Engineering Geophysics
A central tool for engineers, geologists, and geophysicists alike is the use of models
of the subsurface. Parry (2014) defines the engineering geological model as an approximation of the geological conditions created for the purpose of solving an engineering
problem and with the intent to be tested by some form of investigation and thus updated.
In this context, geophysical site investigations are a very valuable way to test hypotheses,
add data, and reduce uncertainties in the engineering geological model. The geophysical
surveys should ideally be carried out before or at least in parallel with the geotechnical
investigations.
Fundamentally, engineering geologists work to add geological understanding to a wide
range of engineering challenges, with inputs including ground characterization, ground
investigation, geohazard assessment (e.g., landslides, earthquakes, sinkholes) and groundwater studies. Detailed consideration of engineering geology is outside the scope of this
book, and Waltham (2009) is recommended as a useful overall introduction.
1.2.2
Environmental engineering
Environmental engineering and the related use of geophysical methods is a well covered
topic in several other books and is not included here. Suggested readings in Chapter 7
include the topics of environmental geophysics. Geophysical methods applied for mapping
contaminants as well as groundwater resources can also be used for the engineering challenges covered in this book and often go hand in hand in several ways. First of all, variation
in hydraulic conductivity relevant to evaluating fluid flow often correlates very well with
variation in lithologies. Second, groundwater and surface water issues are closely related to
the scope of works covered in this book.
The water content of the soil influence and to some extent control the design parameters.
Climate change has increased awareness of the need to consider water management as part
of construction projects and even of catastrophic events, accentuating the role of groundwater and surface water. Furthermore, projects may be subject to requirements related to
groundwater protection, or it may be necessary to assess potential hazards associated with
contaminant transport near a planned construction site. In addition, the construction project
itself may require some dewatering to keep construction pits dry (see the cases in Chapters 31, 32, and 50).
Water content is a fundamental indicator of the likely behaviour of the soil and is routinely
part of geotechnical investigations. Geophysics are useful for distinguishing between aquifers (soils conducting water, and from which groundwater can be extracted) and aquitards
and aquicludes (soils permitting only a very slow or no flow of water). In some geological
settings geophysical methods can be used to map the water table, and geoelectric or electromagnetic methods can distinguish between salty and freshwater bodies. The low resistivity
of the salt water is very characteristic. When a planned construction site is contaminated,
mitigation measures are often required. It is therefore of interest to map the extent and potential migration paths of the contaminants. Depending on contaminant type and its mobility,
different topics may be discussed. Gaseous compounds may rise through porous or fractured
materials, while soluble compounds are likely to be transported along with the groundwater.
In the latter case, the groundwater flow must be addressed. In addition to the potential for
mapping groundwater transport, recent geophysical developments reveal the potential to
map conductive groundwater when contaminants introduce ions in otherwise fresh groundwater resources.
Introduction
1.2.3
5
Resource mapping
Mining and mapping of raw materials and groundwater are not covered in this book, though
they are closely related to many of the topics. Shallow geophysical methods have been used
for groundwater or minerals resource investigations for many, many years. Other books
cover these topics, both their development and their application. However, mining projects
are also construction projects during pit development, after exploitation is finalised, and as
new land use is initiated. Tunnels, voids, and tailing dams related to mining are engineering
challenges where geophysical methods can be applied for various purposes. A few examples
are included in the book. See the cases in Chapters 42, 48, and 49.
Construction works require various raw materials, typically coarse materials, sand, and
gravel (aggregates). Some of these may be found locally, which is a cost-effective solution. But for such potentials to be realized, the volume of available relevant deposits must
be determined. Geophysical mapping for aggregates may be initiated in close relation to
construction projects or by authorities or developers of gravel pits. For that reason, a few
examples are included in the book. See the cases in Chapters 25 and 26.
Hydrogeophysics groups exist in both engineering companies and in the academic world
and are becoming increasingly important as drinking water resources worldwide are under
pressure due to contamination or droughts. The application to the engineering challenges
of methods inherited from developments in hydrogeophysics have increased and owe some
of their success to these developers. Suggested readings in Chapter 7 include the topics of
groundwater geophysics.
1.2.4
Suggested readings for omitted topics
Two major areas related to engineering geophysics are not covered by this book, though
they are mentioned in certain chapters or cases. Borehole geophysics utilises the same
physics as surface measurement and is highly useful in establishing the velocity or electrical properties of soils. For the latter, see the case in Chapter 42. Seismic site characterization is needed in areas of the world where hazards associated with earthquakes
are closely integrated with design criteria. For the use of surface seismic methods, see
Chapter 11 and Sitharam et al. (2018). Seismic site characterization is well covered in the
comprehensive work by Yilmaz (2015), which also covers the use of seismic methods in
boreholes.
1.3
DEFINING SCOPE OF WORK
When a geologist defines a ground model, understanding the geological history and processes leading to the deposited units is key. For this, understanding geophysical data is
a valuable input. Understanding geological history implies establishing the chronology
of processes and sediments forming a landscape that eventually becomes the subsurface.
Based on this 3D understanding of the subsurface, predictions between data points become
feasible. When a geotechnical engineer defines a ground model, the purpose is to describe
the properties of the 3D layers, e.g., strength and stiffness, in a manner suitable for further calculations needed for construction-related decisions. This is based on a geological
understanding of the area and related potential hazards to determine the relevance of different layers.
6
Engineering Geophysics
1.3.1
Screening or problem solving
When considering geophysical surveys, it is essential to understand the questions asked.
These questions often depend on the current phase of a project.
During early-stage planning phases, lateral covering information may be a priority. The
opportunity here is to reduce construction costs by avoiding the most challenging soils and
wisely choosing the locations for geotechnical positions and construction works. Thus a
screening is needed, and the coverage often has higher priority than resolution. At this stage,
questions may relate to the general understanding of the site geology, e.g., detection of challenging layers like soft soils unable to support structures, detection of bedrock variation, or
major structural features introducing lateral variation like major faults, buried channels, or
karstic landscapes.
In later predesign phases, locations are typically fixed, and opportunities are related more
directly to foundation design. For this purpose, depending on the geometry and size of the
planned construction, more localized surveys to address site-specific potential hazards or
risks may be relevant. At this stage, high resolution may be a priority rather than lateral
coverage. A more detailed definition of geological conditions may be of interest, along with
addressing specific questions related to unexploded ordnance (UXO) clearance, more exact
location of anthropogenic structures, and other small-scale features.
Almost all investigations can be listed as addressing some sort of potential hazard or risk.
Knowing soil conditions permits for less conservatism in design. Knowing the properties
only in point locations requires adding a larger safety margin to structural design, accounting
for the risk of the unknown. By adding the 3D mapping of the geology, including an interpretation of depositional environments, improves the knowledge of the expected variation
within a unit regarding thickness and lithological variation. That information is valuable for
design work when evaluating risks and assessing the potential need for additional boreholes
and their suggested location. Thus the 3D understanding can save money both through a better targeted geotechnical campaign and by reducing the risk and thereby requiring a smaller
safety margin in the design phase.
1.3.2
Conceptual understanding of the area
Knowing what to look for is valuable. A conceptual model should be the starting point in
defining potential hazards and assessing whether related severity or costs should lead to
mitigation. A conceptual model is a general understanding of an area and can be more or less
formalized. Even when the geology is relatively unknown, a general geological history of an
area is often available and contains relevant information on the main lithologies or structures
expected. If presented as a profile, it shows the main lithologies and geological structures
present in the area and provides a basis for decisions even though this specific profile may
never be found anywhere on the site.
Generally, any prior knowledge of geology and its surface environment is valuable when
planning geophysical surveys. It is easier to assess whether geophysical methods will benefit a project when having a conceptual understanding of an area. Decisions on the specific
geophysical methods to apply can often be made from studies of regional geological maps
or nearby boreholes.
Figure 1.1 shows profiles representing conceptual geological understandings of 2 different areas: area (a) and area (b). In area (a), the layers are assumed to be nearly horizontal
Introduction
7
(a)
(b)
Sand
Waterlevel
Clay
Screen
Figure 1.1 (a) Upper: Conceptual understanding of an area; e.g., based on a regional geological history
or map. Lower: Boreholes representative from the area. (b) Upper: Conceptual understanding for a different area. Lower: Boreholes representative for the area.
and relatively homogeneous over the area. The lack of complexity might make geophysics
irrelevant with respect to the geology in this case.
In area (b), a very complex geology is expected, and geophysical measurements may be
relevant to tie borehole information, even with several boreholes available. When expecting
large lateral variation in lithologies, performing the geophysical survey early in the project is
recommended. This may reduce costs by locating later geotechnical investigations to target
the lithologies at the most optimal locations.
Assuming the geological understanding includes borehole information from locations representative for the areas, as shown in Figure 1.1 for areas (a) and (b), this information could
8
Engineering Geophysics
further support conclusions. In area (a), boreholes show consistent and uniform layers at similar
depth. Hydraulic heads (water levels) in boreholes screened at similar elevations are consistent
with the expected flow of water in the aquifer, which does not advocate against a lateral connection between sandy water-bearing layers. On the contrary, in area (b), boreholes available
show different layering in different boreholes. Furthermore, the hydraulic heads in boreholes
screened at a similar elevation differ significantly, indicating disconnected or non-horizontal
layering (flat surface and no nearby bodies of water). Note that that expected hydraulic heads
should be evaluated by a hydrologist and often require pump tests, which are outside the scope
of this book.
The surface environment for planned construction works is potentially the biggest issue
for the project. In rural, mountainous or forested areas, land access for geotechnical investigations may be troublesome, whereas geophysical mapping can be performed with lighter
equipment. In a developed area, the main issues may relate to space requirements or previous installations and remoulding of the area rather than geological variation. Knowledge of
former use, e.g., historical aerial photographs of the site, old city plans, or records of existing
pipes and cables, may inform on potential hazards or risks related to anthropogenic features,
existing facilities, or contaminations.
1.3.3
Survey planning
The conceptual model helps determine which geophysical method is most likely to be successful and provides essential information for decisions on the survey setup. Based on the
conceptual model, it is possible to carry out forward calculations on relevant synthetic models and invert them to understand whether it is possible to resolve a target using a specific
geophysical method and setup (see Section 1.4). Theoretically, this is how survey planning
should be performed. From a practical perspective, time or cost does not always allow that.
Therefore, using more than one method is often a successful solution. An experienced geophysicist often knows what type of surveys are likely to be useful in a specific geology even
without detailed pre-modelling.
Knowledge is needed of the scale of features and targets, along with some initial understanding of the direction of any linear features. As discussed in the various method sections,
such information may determine the choice of survey line plans, instrument configuration,
and settings to balance the resolution needed against the survey speed, etc. Specifically, if 2D
surveys are planned and linear features are expected across the area, survey lines should be
arranged perpendicularly to the anticipated strike of features for the best resolution. In case
linear structures are expected but the strike is unknown, consider an area covering survey, 2D
with perpendicular survey lines or 3D, in order to reduce the risk of overlooking significant
features striking parallel to survey lines. The surface environment is equally important to
understanding likely noise sources for the data, planning equipment access, and the necessary
workforce.
For all geophysical methods, geographical positioning is critical. It must be measured during acquisition with utmost care as well as handled correctly during all later processes toward
the final result and interpretations. At the site, modern surveying methods are widely used but
also sometimes challenged due to the lack of coverage of navigation systems or the survey
environment. Forests or mountains may restrict connection of the GPS to a sufficient number
of satellites. Equally critical is the consistent use of a single agreed-on coordinate system and
datum for the positioning in the project. The same datum/level must be used for data acquired
Introduction
9
by the survey itself and for integrating other information toward a final result. Interpretations
and models can be taken according to elevation but also according to ground level, which
then becomes the datum. Either way, the elevation of the ground surface must be determined.
1.3.4
Ground truthing and integrated methods
In general, more information is preferred but comes with increased cost. Since geophysical methods themselves at the moment cannot translate directly to geology or geotechnical
properties, ground truthing is needed. The advantage of collecting geophysical data first is
often that geotechnical locations can be chosen to resolve and adequately describe different
soil optimally. The geotechnical boreholes act as a calibration for the geophysical data. Correlation between the data types permits a translation of geophysical data to lithologies with
specific properties.
Regardless of the method applied, the observed geophysical results often allow many
equally likely soil type interpretations and possible model results. Either because of uncertainties, non-uniqueness, and equivalences (see Section 1.4) or simply because certain lithologies have similar physical properties, geophysical data need ground truthing, preferentially
with geotechnical investigations.
In an interdisciplinary collaboration, the aligning of expectations is key. The level of
detail is very different between geotechnical and geophysical information. A borehole or
CPT can provide relevant information at depth with great detail at a centimetre scale in
one geographical position. However, the information is not necessarily easy to interpolate
between boreholes. Compared with this, the geophysical data offer a lower level of detail,
often at meter scale, distributed laterally along lines or in grids. So the geophysicist offers a
blurred picture of the subsurface but with a much better opportunity to predict the subsurface
between data points. The combination of detailed direct information from the geotechnical campaign and the broad but less detailed overview from the geophysical investigation
strengthens the output of both.
Also, combining several geophysical methods generally enhances the chance of success.
Each method maps different physical properties and has its own set of limitations. When
used in combination, these limitations can be conquered. Further, several methods sensitive
to different parameters may supplement one another and provide a higher resolution when
used in combination. Often, time and cost influence the use of integrated methods. Using
several methods one after the other takes considerably more time and higher cost than mobilizing a geophysical team to use several methods during the same field campaign. The need
for ground truthing and the use of integrated methods are well illustrated in many of the case
histories of this book.
1.3.5
Processing
Processing is in the expert domain. Processing steps differ depending on the geophysical
method. Nevertheless, the first step is a data quality assessment. For some methods, noise
reduction is possible by filtering out irrelevant signals or the signal-to-noise ratio may be
enhanced by stacking. For other methods, unreliable data points must be identified and
removed. If disturbed or unreliable data points or noise are included, it may affect and hamper even the good and reliable parts of a data set.
10
Engineering Geophysics
Understanding data and the related uncertainty of data is an essential step for understanding and providing information on the reliability and resolution of the results. Furthermore,
when inversion is applied (see Section 1.4), knowledge of the uncertainty related to input
data aids in setting up a meaningful inversion. This book does not cover detailed processing
steps except for information considered relevant by method or case authors and included in
subsequent chapters. We refer to the literature referenced in the relevant method sections for
further details on specific methods.
1.3.6
Machine learning
Machine learning methods are rapidly developing and hold a huge potential for geophysics
and geotechnics, for interpretation of data or models, and for data processing purposes, as
well as for the description of relations between parameters. Details of machine learning are
considered beyond the scope of this book, but a short introduction is provided, acknowledging that the applications are expected to gain increasing use in the years to come.
Machine learning is a way to search for correlations in the data. Application of the techniques only makes sense if it can reasonably be expected that a relation exists in the data
analysed. There are different ways to apply machine learning methods to classify or identify
patterns or describe relations. In the application of inversion described in following sections,
the relation between model parameters and data is known and used to search for unknown
model parameters based on collected data. In machine learning applications, on the contrary,
the relationship is often the unknown, and by learning from observed data related to known
models, features, or classifications, the relation is found.
Machine learning methods for interpretation purposes are challenged when relations are
only locally valid because a large number of data points are required for a reliable description even of local relationships. Thus a greater potential is expected if global relations or
patterns can be defined. An example could be predicting noise patterns or disturbances, like
a coupling in EM or ERT data, which would produce a similar pattern in any lithology. The
same goes for detection purposes, e.g., searching for specific hyperbola patterns in GPR or
seismic data for detection of buried structures or faults.
Machine learning also has a potential in combining various types of information or vast
amounts of data that have an objective relation but are too extensive for the human brain to
accommodate and keep track of all at once.
1.4
GEOPHYSICAL INVERSION FOR ENGINEERS
Douglas W. Oldenburg, Seogi Kang, and Lindsey J. Heagy
1.4.1
Introduction
Solutions to engineering problems require the ability to determine earth structure beneath
the surface. Borehole information provides detailed knowledge at a specific location, but
extrapolating that information away from the borehole or interpolating structures or earth
compositions between boreholes is challenging because the earth structure is highly variable
in all three dimensions. Geophysics provides a complementary approach to obtaining that
spatially distributed information.
Introduction
11
The method sections in this book illustrate how geophysics can be used to solve a variety of engineering problems. The basic procedure is to convert surface measurements into
images of the subsurface. This is often done through the process of inversion. In this section, we introduce the fundamentals of inversion, show how inversion is implemented, and
discuss the issue of non-uniqueness and how to assess the reliability of final models derived
from inversion. We begin with a high-level description of the geophysical survey and the
inverse problem.
1.4.2
Geophysical survey
A generic geophysical experiment is displayed in Figure 1.2a. Energy from a source is input
to the ground, and it propagates through the earth in a way that is governed by physical
equations. The source can be provided by nature, such as for gravity and magnetics surveys,
or it can be human-made, such as those used in seismic, controlled-source electromagnetics
and ground penetrating radar surveys. The parameters in the governing equations are the
physical properties of the earth (e.g., density, susceptibility, electrical conductivity, magnetic
permeability, elastic parameters), and the distribution of these parameters determines how
the energy flows. Sensors, usually at the surface, record potentials, fields, and/or fluxes.
These are the “observations” or “data”. In a general geophysical experiment, we are given
knowledge about the source, the equations governing the physics, and the observations. The
goal of the inversion is to determine the distribution of the physical properties that gave rise
to the data. This is illustrated in Figure 1.2b.
The physical properties of the earth are highly variable at different scale lengths, and the
geophysical data provide only a finite number of constraints on them. The inverse problem is
therefore inherently non-unique. Historically, to perform inversion, a simplified model, such
as a layered earth composed of two or three layers, is assumed. Then parameters, e.g., layer
thicknesses and physical property values, are found using a numerical optimisation algorithm. In this formulation, the number of geophysical data exceeds the number of parameters, and this allows the uncertainty of the parameters to be estimated (e.g., Aster et al.
2013). Although this methodology can provide useful information, it is inadequate when
Figure 1.2 (a) Generic geophysical experiment. (b) Role of inversion for processing geophysical data to
estimate a physical property model of the subsurface.
12
Engineering Geophysics
imaging the complex details or smaller-scale structures that are important when addressing
engineering problems.
Rather than working with a few parameters, we formulate the inverse problem in terms of
finding a general distribution of physical properties. We discretise the earth into small cells,
or voxels, which are smaller in size than the resolving power of the geophysical data. The
cells could be 1D, 2D, or 3D, and we seek to find the value of the physical property associated with each cell. The number of cells is much larger than the number of data. Thus the
inverse problem is non-unique. Other information is required in order to get a useful solution
to address engineering problems. There are many good resources for how the inversion problem can be formulated, but much of the work presented here is based on the tutorial paper
from Oldenburg and Li (2005).
Unfortunately, the solution to an inverse problem is not a “turn-key” operation. It
involves numerous steps, and the most successful implementations are achieved through
an iterative workflow procedure, as shown in Figure 1.3, in which collaboration between
the engineer and geophysicist is essential. We note that many of the practical problems
addressed in this handbook use these techniques, and here we outline the important components for setting up and solving the inverse problem. Throughout, we provide definitions of terms that engineers encounter when talking to the geophysicist. We conclude
Figure 1.3 Inversion workfow.The “Evaluate results” box requires interaction with the engineer.
Introduction
13
with comments about resolution and uncertainty. An example of inverting direct current
(DC) resistivity data to recover the 1D vertical resistivity of the earth is used to tie the
concepts together.
1.4.3
Setting up the inverse problem
As indicated in the workflow (Figure 1.3), there are three main components in setting
up the problem. Assembling the geophysical data and being able to carry out forward
modelling are the responsibility of the geophysicist. By forward modelling, we refer to
the ability to simulate observations for a given earth model. It is traditional to write the
data as
F[m] = d
Here, F is the mathematical operator that includes the physics of the problem and the details
of the survey, m is the “model”, usually a vector representation of the physical properties,
and d represents the simulated observations. The solution to the forward problem is obtained
using numerical modelling software.
Assembling prior information requires input from the engineer, geologist, and geophysicist. The information can include borehole logs, geologic sketches of the area and what is
sought, and information from other geophysical surveys. This also requires having estimates
for the physical property values of the geologic or engineering units.
1.4.4
Model parameters for inversion
The earth is a 3D medium, but in the inverse problem, the earth can have 1D, 2D, or 3D cells
of different sizes and shapes. Examples of cell geometries are shown in Figure 1.4.
Figure 1.4 Sampling of different mesh types used for numerical simulations.
14
Engineering Geophysics
1.4.5
Solving the inverse problem
Irrespective of how the earth is parameterised, the inverse problem is non-unique. That is,
many mathematical solutions acceptably fit the observations, and they can be very different
and have a structure that, if interpreted geologically, would lead to incorrect information for
the engineering problem. The inverse problem needs to be “regularised”, and this is done by
including the prior information referred to in Figure 1.3. The inverse problem is then solved
by finding the model m that minimises:
˜ (m) = {data misfit } + ° {model norm}
= ˜d (m) + °˜m (m)
(1.1)
such that the simulated data are adequately fit.
There are three essential elements in this equation, and we define them and discuss their
relevance next.
1.4.5.1
Data misfit fd
This is a measure of how closely the predicted data (using a trial model m) match the observations. Most practitioners use the least squares criterion:
˛ d − d obs ˆ˘2
˙˙ j
j ˘
fd =
˘˘
˙˙
ε
˘ˇ
˙
j
j=1 ˝
N
(1.2)
The normalising constant ε j is the estimated uncertainty for the jth datum. If the noise on the
data is Gaussian with standard deviation epsilon, then this becomes a chi-squared statistic
2
whose expected value E[c ] ~ N . When describing how well the data are fit, sometimes the
2
value of fd is reported, or it can also be described in terms of the c -factor, which is fd /N ,
2
or a root-mean-square (RMS) value, which is fd / N . If the c -factor or RMS value is
~1, then the error on the data fit matches the expected uncertainty. Characterising the misfit by a single statistic is extremely valuable when interpreting whether a proposed model
might be a good candidate for interpretation. However, it is also valuable to view plots of
the data misfits to ensure that there are no regions with data points that are systematically
being underfit. In such regions, we may consider the model to be less reliable, or there may
be outliers. We note that Eq. 1.2 is an L2-norm misfit, but other metrics such as an L1-norm
can be used when outliers are present.
1.4.5.2
Model norm fm
”Model norm” encompasses the prior information assembled in the setup. It includes a reference model mref that pertains to the area of interest. For some problems, the reference model
might be detailed and complicated, while for other problems, where little is known, mref
might be a uniform earth and represented by a single number.
The model norm is generally composed of a combination of terms, each of which plays
a role in determining the character of the final model. The goals include making the model
close to the reference mref , enforcing a smooth transition of model parameters in various directions, and the ability to adjust where prior information is reliable or somewhat
Introduction
15
uncertain. Typically, the mathematical form for these terms involves L2-norms, but changing the metrics to L1- or L0-norm can produce models that have sharp interfaces or are
compact. Designing a suitable regularisation is essential. However, when the relevant prior
information is not available, simple regularisations that yield smoothly varying solutions can
be defaults and can provide a good first estimate of the earth model.
1.4.5.3
Regularisation parameter β
The regularisation parameter, often called the trade-off parameter, controls the relative
importance of the two terms in Eq. 1.2. β is a constant and takes any value in the range [0,¥ ] .
If it is very small, there will be little regularisation, and the model will have excessive and
unrealistic structures. Essentially, we are overfitting the data; that is, we are fitting the noise
in our data and thereby allowing noise to create structures in the model results. At the other
extreme, if β is very large, we are attempting to minimise the model norm term only without
allowing the data to play a significant role. The resultant model will equal the reference
model, which will not provide a good fit to the data. In practice, Eq. 1.1 is minimised for a
suite of β-values, and a Tikhonov curve, as shown in Figure 1.5, is produced. Choosing a
solution with a misfit fd equal to some target misfit, fd* (i.e., the expected value from Eq. 1.2
or, equivalently, an RMS of ~1) is a good start on the interpretation. Other solutions around
this value are often worthwhile to explore.
1.4.6
Numerical solution
The inverse problem is solved numerically using iterative techniques, and, at each stage,
large matrices need to be solved many times. Modern laptops or workstations can readily
handle 1D problems, 2D problems for DC resistivity and seismic tomography, as well as
3D problems for potential fields (gravity and magnetics). However, 3D problems in electromagnetics or full waveform inversion of seismic or GPR data need HPC or cloud resources.
Figure 1.5 Tikhonov curve.
16
Engineering Geophysics
After a numerical solution is obtained, the results must be evaluated to see whether the
geophysical data are acceptably fit and whether the model can answer the engineering questions. If the answer is “yes”, then the inversion is complete. If the answer is “no”, then
parameters in the inversion (overall misfit, estimated uncertainties, details of the model
norm, etc.) may need updating, and the inversion can be carried out again (Figure 1.3). Interaction between the engineer and geophysicist can be valuable at this stage.
1.4.7
Example inversion
To illustrate the important aspects of inversion, we present an example using a DC resistivity survey (Chapter 6). The earth model shown in Figure 1.6a has 4 layers. A Schlumberger
sounding with a current-electrode-separation (AB) extending from 1 m to 1000 m was carried out. Five percent Gaussian noise was added, and the apparent resistivities are shown in
Figure 1.6a. There are 31 data points. We divide the earth into 30 layers with a uniform thickness of 2.5 m, and carry out the inversion where the model norm is designed to generate a
smoothly varying model (i.e., the smallness term, including a reference model, is removed).
The result is plotted in Figure 1.6d and shows a smoothed representation of the true earth
with the major features reasonably well recovered. Figures 1.6c and 1.6e show the results
of underfitting and overfitting the data, respectively. The model norm can be altered such
that sharp interfaces are obtained in the model; this is shown in Figure 1.6h. In Figure 1.6g
we show a result obtained by correctly parameterising the earth as a 4-layer model without
regularisation. The result is reasonably good. However, if we use only two layers, an incorrect parameterisation, and no regularisation, then we get the result in Figure 1.6f.
In summary, this example shows that if the parameterisation of the earth model is exactly
representative of the subsurface, then solving the inverse problem through traditional techniques can produce good results. In general, however, we do not have such information.
Therefore, posing the inverse problem as highly underdetermined and incorporating other
information into a model constitute a much more stable way to proceed. This formulation
also allows us to address issues of resolution, non-uniqueness, and uncertainty.
1.4.8
Non-uniqueness, uncertainty, and resolution
We discuss these items within the context of the DC problem and the results shown in Figure 1.6. The concept of non-uniqueness is illustrated in Figure 1.6d, g, and h, where we show
three different models that fit the data very closely to the desired global misfit value of 31.
If we include Figure 1.6e, which overfits the data, it is clear that the value of the physical
property for each cell is highly variable, especially at depth where the sensitivity of the data
is small.
The concept of non-uniqueness and uncertainty is meaningful only when discussing a
spatial average of a physical property. Both recovered resistivity models in Figures 1.6d and
1.6h show that there is a conductive layer near the surface. While the top boundary of the
conductive layer is accurately imaged in both models, the layer thickness and resistivity are
inaccurate. Further, many other resistivity models can fit the data. Thus, for a single cell,
the level of non-uniqueness and uncertainty is extremely high. For the imaged conductive
layer, meaningful information about the uncertainty can be obtained when we average the
recovered resistivity values over the depth interval: 5–20 m, which contains the conductive
layer. The average resistivity values of the two models in Figure 1.6d and h are, respectively,
Introduction
Figure 1.6 Inversion examples with a direct current resistivity experiment.
17
18
Engineering Geophysics
13 and 12 Ωm. This provides us with confidence that there is a conductive layer and that its
average resistivity value is about 12.5 Ωm for a given depth interval.
Also, for some problems, there can be fundamental reasons for non-uniqueness. The geophysical data depend on the product of a physical property and geometrical dimensions. This
relationship is sometimes referred to as equivalence. Table 1.1 summarises some of these.
Confidence in a feature in the recovered model can be enhanced by generating other models that also fit the data and are geologically feasible. This can be done by altering the parameters of the model norm and carrying out the inversions. If all the resultant models show the
feature in question (e.g., a void, conductive layer, etc. in a particular region of the earth),
then this increases the confidence in its existence. However, if even one of the results does
not have the feature, then confidence is eroded.
This idea can be carried a step further. Hypothesis testing, where one attempts to construct
a model that does not have a feature (e.g., a void, conductive layer, etc.) in a particular region
of the earth, can also be carried out. If no geologically reasonable model can be found, then
confidence in the existence of the feature is increased.
Prior information can also be used to build confidence in an inversion result. For example,
if a transition that is observed in the inversion corresponds to a geologic interface interpreted
from well logs at a particular location, then this provides confidence in extending interface
interpretation away from the borehole. In addition to the avenues for including prior information in the inversion that we discussed in Section 1.4.6.1 on model norms, more advanced
inversion methods make use of techniques from machine learning to include physical property data and/or geologic information so that the model that is obtained favours all available
information while fitting the geophysical data.
The methods for obtaining a model that we have discussed thus far take a deterministic
approach to the inversion. That is, for a given choice of model norm and definition of data
uncertainties, a single model is obtained. Another class of methods for solving the inverse
problem takes a Bayesian approach and seeks to find a distribution of models that fit the data
(Tarantola 2005). This provides an avenue for quantifying uncertainties. However, these
approaches are much more computationally intensive and, as a result, are typically restricted
to simple geometries such as a 1D layered earth model and small 2D problems.
Our inversion methodology can also be used to obtain insight into the resolving power of a
survey or the depth of penetration. We note that these quantities cannot be defined through a
simple equation; they are problem dependent. The depth that a survey can “see” depends on
the physics of the problem and the survey. In DC resistivity, the further apart the electrodes
are, the deeper we can see. In EM, lower frequencies see deeper than higher frequencies.
Also, the depth to which a target can be seen depends on the noise in the data. Greater depth
of penetration and resolution can be achieved by having observations with lower noise.
Table 1.1 Equivalence underlying non-uniquenesses for different types of geophysical survey
Geophysical survey
Equivalence underlying non-uniqueness
Gravity
Magnetic
DC resistivity
Electromagnetics
Product of density and volume of a confned body
Product of susceptibility and volume of a confned body
Product of resistivity and thickness of a layer
Product of conductivity and thickness of a layer
Introduction
19
Depth of penetration depends on the background geology. For instance, in DC resistivity
surveys, a thin layer of highly resistive or conductive material shields the region beneath,
and any feature below the layer is invisible. Therefore, the rule of thumb that the depth of
penetration is a fraction of the maximum electrode spacing, which works for a uniform earth,
is not applicable. The maximum depth of penetration is the depth of the thin layer irrespective of how far apart the electrodes are. There is an analogous conclusion for EM surveys in
that a highly conducting layer shields structures below.
It is possible to assess whether a specific model is likely to be resolvable by using the
following steps: (1) create a synthetic model; (2) carry out a forward simulation of the data;
(3) add representative noise; (4) invert the data. This provides important insight about the
resolving power of the geophysical survey and a sense of what the image of the subsurface
might look like. This procedure can be used either before or after the survey. It is highly
recommended to conduct this process before the survey, as this helps prevent the misuse of a
geophysical survey to address an engineering problem. When done before, the geophysicist
refers to this step as survey design.
1.5
CONCLUSIONS
Geotechnical engineers are faced with solving progressively more complicated problems
that require an understanding of the subsurface. Geophysical data can provide insight into
variations in subsurface physical properties, which can be informative for addressing questions about the subsurface. Inversion is a procedure by which we estimate a model that is
consistent with the geophysical data and available prior information.
This chapter introduced the basic elements needed to understand how modern inversions
are conducted. Obtaining a high-quality model and interpreting that model in the context
of the engineering question at hand is best achieved as a collaborative process between the
geophysicist and the engineer. When interpreting a model obtained through inversion, it is
important to consider:
•
•
•
•
•
•
How the subsurface engineering features of interest relate to the physical properties that
a given geophysical survey is sensitive to.
Whether the geophysical survey design is adequate to answer the engineering question.
The limitations of the physics (e.g., how deep a survey can “see”) and inherent nonuniquenesses of a given survey type.
The data coverage and how well the recovered model reproduces the data.
The expected influence of inversion parameters such as the choice of the model norm on
the recovered model.
What prior information is available and how this can be used to improve an inversion
model or aid in the geologic interpretation.
REFERENCES
Aster, R. C., B. Borchers, and C. H. Thurber. 2013. Parameter Estimation and Inverse Problems.
Amsterdam: Elsevier. https://doi.org/10.1016/C2009-0-61134-X
Oldenburg, D. W., and Y. Li. 2005. Inversion for applied geophysics: A tutorial. In Investigations in
Geophysics 13, Near-Surface Geophysics, ed. D. K. Butler, pp. 1–85. Tulsa, OK: Society of Exploration Geophysicists. https://doi.org/10.1190/1.9781560801719.ch5
20
Engineering Geophysics
Parry, S., F. J. Baynes, M. G. Culshaw, et al. 2014. Engineering geological models: An introduction: IAEG commission 25. Bulletin of Engineering Geology and the Environment 73(3):689–706.
https://doi.org/10.1007/s10064-014-0576-x
Sitharam, T. G., N. James, and S. Kolathayar. 2018. Seismic site characterization. In Comprehensive
Seismic Zonation Schemes for Regions at Different Scales. Heidelberg: Springer Nature. https://doi.
org/10.1007/978-3-319-89659-5_4
Tarantola, A. 2005. Inverse Problem Theory and Methods for Model Parameter Estimation. Philadelphia, PA: Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9780898717921
Waltham, T. 2009. Foundations of Engineering Geology. Boca Raton: CRC Press, Taylor and Francis.
ISBN 9780415469609
Yilmaz, O. 2015. Investigations in Geophysics No. 17, Engineering Seismology with Applications to
Geotechnical Engineering. Tulsa: Society of Exploration Geophysicists.
Chapter 2
Scope of work
Anna Bondo Medhus and Lone Klinkby
The flowcharts presented in this section aim to connect engineering disciplines to the
geophysical methods most likely relevant for the different fields of expertise. The guide
leads from conceptual understanding and the need for information to methods that may fit
the purpose. In the flowcharts, “chapter” is omitted for readability; thus the numbers refer
to chapter numbers. Some boxes in the flowchart relate to the expected geology, others to
specific targets. Each listed topic leads to one or more suggested geophysical method. The
methods listed refer to the relevant method sections that provide useful information on the
physics of the method but also on the limitations and considerations for acquisition. One
or more cases are referenced to illustrate the method’s capabilities suggested in practical
operation.
The cases present realistic examples of what may be expected when using the method
suggested in a particular geological setting or for a specific purpose. In this way, the flowcharts aim to provide guidance towards parts of the book of specific relevance to the engineering challenges at hand. Some cases describing a solution for one engineering discipline
may well illustrate both purpose and resolution capability for other engineering disciplines.
Therefore, where a flowchart includes a case focusing on a different engineering discipline
that presents solutions to the needs addressed, the case is marked with an asterisk (*) following the chapter number.
The mapping of anthropogenic features is relevant for most engineering disciplines, but
to maintain readability, a separate flowchart on “Anthropogenic Targets” is provided and
referenced where relevant.
The flowcharts summarise suggested solutions based on cases received from several contributors. Thus they do not cover what may theoretically be the options as much as what we
practically experience in the applications. In Section 2.1, further considerations on framing
and limitations are given, along with important reservations.
DOI: 10.1201/9781003184676-2
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Engineering Geophysics
2.1
ANTHROPOGENIC FEATURES
Magnetometry
Metallic objects, like cars,
may disturb the data
Method chapter 5
cases : 12, 13, 17, 18, 19
UXO
Magnetic target
case : 12
Electromagnetics
Conductors like buried
cables will disturb the data
Landfll
case : 13
Method chapter 7
cases : 12, 13, 15, 18
Localised
structures
Archaeology
and buried
constructions
cases : 14, 15, 17, 18, 19
Pipes and ground
anchors
cases : 20
Magnetic target
Ground
penetrating radar
Non-conducting soils only
Method chapter 8
cases : 14, 20
Gravimetry
Steep topography must be
accounted for
Method chapter 4
cases : 18
Figure 2.1 Flowchart summarising suggested solutions for mapping of anthropogenic features based on
cases received from several contributors.
Scope of work
2.2
23
ROADS AND RAILS
Seismic refraction
Heavy machinery or
waterfalls may disturb data
Method chapter 10
cases : 22, 31*, 37*
Mapping needs
Soft soil
cases : 21, 22, 23
Bedrock
cases : 21, 22, 24, 16* 37*
Electromagnetics
Conductors like buried
cables will disturb the data
Method chapter 7
Related topics
cases : 24, 16*, 32*
Water management
Conceptual
model
cases : 50, 31*, 32*
Aggregates
cases : 25, 26
Maintenance
rail condition
case : 27
Road condition
DC resistivity (ERT)
Conductors like buried
cables will disturb the data
Method chapter 6
cases : 22, 25, 26, 50, 37*
case : 23
Depth, frost
case : 23
Ground penerating
radar
Non-conducting soils only
Method chapter 8
cases : 21, 22, 23, 27
See Section 2.1 on anthropogenic features
for detection of buried structure, archaeology and UXO
Figure 2.2 Flowchart summarising suggested solutions for mapping in roads and rails projects based on
cases received from several contributors.
24
2.3
Engineering Geophysics
SHALLOW FOUNDATIONS
Gravimetry
Mainly for major density
contrasts
Method chapter 4
cases : 48
Refraction
Heavy machinery or
waterfalls may disturb data
Mapping needs
Method chapter 10
Soft/ loose soil
cases : 29, 30, 31, 33*,
37* 48*
Bedrock depth
and status
DC resistivity (ERT)
cases : 28, 29, 21*
cases : 29, 30, 37,
16*, 21*, 33*, 48*
Conductors like buried
cables will disturb the data
Method chapter 6
cases : 25, 26, 28, 29, 33*,
37*, 48*, 50*
Conceptual
model
Related topics
Water management
case : 31, 32, 50*
Ground
penetrating radar
Non-conducting soils only
Method chapter 8
cases : 29, 21*
Aggregate mapping
cases : 25, 26
Surface waves
(MASW)
Method chapter 11
cases : 29, 33*
Electromagnetics
Conductors like buried
cables will disturb the data
Method chapter 7
See Section 2.1 on anthropogenic features
for detection of buried structure, archaeology and UXO
cases : 32, 16*
Figure 2.3 Flowchart summarising suggested solutions for mapping in shallow foundation projects
based on cases received from several contributors.
Scope of work
25
2.4 TUNNELS AND DEEP FOUNDATIONS
Seismic reflection
Heavy machinery or
waterfalls may disturb data
Method chapter 9
cases : 39, 40
Seismic refraction
Heavy machinery or
waterfalls may disturb data
Conceptual
model
Depth to bedrock
and detecting
weak zones
cases : 37, 38, 39, 40,
41, 42
Method chapter 9
cases : 37, 38
Electromagnetics
Conductors like buried
cables will disturb the data
Method chapter 7
cases : 41
DC resistivity (ERT)
Conductors like buried
cables will disturb the data
Method chapter 6
See Section 2.1 on anthropogenic features
for detection of buried structure, archaeology and UXO
cases : 37, 42
See Section 2.3 on shallow foundations for shallow interests
Figure 2.4 Flowchart summarising suggested solutions for mapping in tunnels and deep foundation
projects based on cases received from several contributors.
26
Engineering Geophysics
2.5
PIPES AND CABLES
Seismic refraction
Heavy machinery or
waterfalls may disturb data
Method chapter 10
cases : 43, 45, 22*
Electromagnetics
Conductors like buried
cables will disturb the data
Method chapter 7
cases : 44, 16*
Mapping needs
Soft or isolating soil
case : 21*, 22*
Conceptual
model
Bedrock
case : 43, 44, 45, 21*,
16*
DC resistivity (ERT)
Conductors like buried
cables will disturb the data
Method chapter 6
cases : 47, 22*
Crossings
case : 46, 47
Ground
penetrating radar
Non-conducting soils only
Method chapter 8
cases : 46, 21*, 22*
Surface waves
(MASW)
Method chapter 11
cases : 45
See Section 2.1 on anthropogenic features
for detection of buried structure, archaeology and UXO
Figure 2.5 Flowchart summarising suggested solutions for mapping in pipes and cables projects based
on cases received from several contributors.
Scope of work
2.6
27
LANDSLIDES
Seismic refraction
Heavy machinery or
waterfalls may disturb data
Method chapter 10
cases : 33, 34
Electromagnetics
Conductors like buried
cables will disturb the data
Mapping needs
Conceptual
model Risk
Assessment
Method chapter 7
cases : 34, 36, 16*
Depth to bedrock or
potential slip
surface
cases : 33, 34, 16*
Quick clay
cases : 35, 36
DC resistivity (ERT)
Conductors like buried
cables will disturb the data
Method chapter 6
cases : 33, 34, 35
Surface waves
(MASW)
Method chapter 11
cases : 33, 34
See Section 2.1 on anthropogenic features
for detection of buried structure, archaeology and UXO
Figure 2.6 Flowchart summarising suggested solutions for areas with risk of landslides based on cases
received from several contributors.
28
2.7
Engineering Geophysics
DAMS AND SIMILAR STRUCTURES
Seismic refraction
Heavy machinery or
waterfalls may disturb data
Method chapter 10
cases : 48
Mapping needs
Conceptual
model
Foundation:
case : 48, see also:
shallow foundations
Gravimetry
Method chapter 7
cases : 48
Leakage
case : 42, 49
DC resistivity (ERT)
Conductors like buried
cables will disturb the data
Method chapter 6
cases : 42, 48, 49 (incl. IP)
See Section 2.1 on anthropogenic features
for detection of buried structure, archaeology and UXO
Figure 2.7 Flowchart summarising suggested solutions for mapping in projects related to dams and
similar structures based on cases received from several contributors.
Chapter 3
Relationship between geotechnical
and geophysical methods
Michael Long
3.1
INTRODUCTION
This chapter focuses on the relationship between geotechnical and geophysical data.
It aims to help geotechnical engineers understand what geophysical information can
provide and gives geophysicists an appreciation of geotechnical requirements. Often
the focus of geotechnical engineers is on defining the ground model, the location of the
groundwater table, and geotechnical properties such as strength and stiffness. Geophysicists frequently concentrate on mapping lithological variations and hazards. However,
the work of both groups is complementary and ultimately is aimed at risk reduction for
structures.
The first part of the chapter is mainly intended for the geologist or geophysicist to be
familiarised with important geotechnical properties, parameters, and terms. The second part
of the chapter provides an overview of the relationship between physical parameters measured from geophysical methods and geotechnical methods and their relationship. Recent
research is referenced and presented.
3.2
GEOTECHNICAL PROPERTIES
The geotechnical properties that can be assessed can be divided into three main categories:
1
2
3
Basic properties
Strength
Stiffness
In Europe, the requirements for the geotechnical design are presented in Eurocode 7 (CEN
2004, 2007). This requires all structures which are in contact with the ground to be checked
for the following states:
•
•
Ultimate limit state (ULS): Will there be a failure of the ground? For this assessment, the
strength of the soils must be evaluated.
Serviceability limit state (SLS): Will the movement of the structure be such as to cause
it to be difficult to use; i.e., will there be excessive cracking, or will the distortions make
it difficult to open windows or doors etc.? See Figure 3.1. This assessment relates to the
deformation properties of the soils, which refers primarily to the soil stiffness.
DOI: 10.1201/9781003184676-3
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Engineering Geophysics
Figure 3.1 Building in Ribe in Denmark.The foundations have not failed, but the distortion of the building is likely to have resulted in sloping foors and have made operations such as opening
windows and doors diffcult.
Data obtained from geophysical investigations can assist in the determination of basic
properties, strength, and stiffness. These are discussed in turn in the following sections.
3.2.1
Basic properties
A basic property is one that describes the fundamental properties of the soil in its in situ
condition. Some of the most relevant basic properties are described as follows:
3.2.1.1
Density/unit weight
Density (ρ) or unit weight γ (weight per unit volume) are key properties for all geotechnical analyses as they form the basis for the calculation of the stress in the ground, which
is the starting point of all geotechnical design. Density can be measured by in situ testing such as simple sand or water replacement tests, nuclear density testing, or correlation
with some in situ tests such as CPT or the standard penetration test (SPT). Perhaps more
commonly, density is measured in the laboratory by simple measurements on a soil sample. Density measurements for soils typically vary within a range between values close to
1 Mg/m3 (i.e., similar to water) for peat to 2.2 Mg/m3 for a glacial lodgement till (close to
rock or concrete). Many soils have densities in the range 1.7–1.9 Mg/m3.
Geotechnical and geophysical methods
3.2.1.2
31
Porosity/void ratio
The amount of pore space in the soil relative to the quantity of solid particles is an important factor governing the behaviour of the soil in engineering applications. In soil mechanics terms, porosity is the ratio of the volume of voids to the total volume of a soil sample,
whereas void ratio is the ratio of the volume of voids to that of solid particles.
3.2.1.3
Water content
Similar to porosity and density, water content (w) is a fundamental indicator of the likely
behaviour of the soil. Many engineers use empirical rules of thumb based on water content to help them understand soil behaviour. For example, peat that is highly compressible and weak can have a water content of some 1000%, but highly competent glacial
lodgement till might have a water content some two orders of magnitude lower at about
10%. The famous Norwegian Professor Nilmar Janbu used a water content value of 30%
as a first estimate in subdividing normally consolidated soils from lightly overconsolidated soils (Janbu 1970).
3.2.1.4
Relative density
The relative density (Dr) of a soil is an expression of the in situ void ratio of a soil in relation
to the maximum and minimum void ratio. Dr is an important parameter used in assessing the
compaction state of coarse-grained soils. It can be related to the strength of soils, and it is
an important consideration, for example, in the assessment of ground improvement schemes
and liquefaction risk determination.
3.2.1.5
In situ stress
Vertical effective stress (σv0′) can be determined directly from the unit weight of the different
soil layers and the position of the groundwater table. In this case, the geophysical information primarily aids in describing the geometry of layers between boreholes and evaluating
the expected homogeneity of layers and highlighting zones potentially expected to differ
from the rest of a layer. Horizontal effective stress (sh0′) is directly related to sv0′ by the
coefficient of in situ earth pressure at rest (K0):
′
s′h0 = K 0sv0
(3.1)
K0 is a key parameter in the design of underground structures such as retaining walls and
tunnels.
3.2.2
Strength parameters
Knowing the strength of the underground to avoid failure of constructions is obviously of
interest. The most important parameters of this topic are:
•
•
The undrained shear strength (for cohesive soils, e.g., clay and silt) and
Effective shear strength (friction angle primarily for non-cohesive soils, e.g., sands and
gravel) and cohesion and friction angle (clays).
32
Engineering Geophysics
Figure 3.2 Mohr circle and Mohr–Coulomb representation of stress state and failure in the ground.
Several models are available to allow estimates of the strength of the ground to be made
for input into geotechnical calculations. However, by far the most common method in soil
mechanics applications is to use the Mohr–Coulomb failure criterion. This is expressed
graphically in Figure 3.2. Prior to construction, there are typically some vertical and horizontal principal effective stresses in the ground (σ1′ and σ3′). Generally, the construction
increases σ1′. The increase in σ1′ cannot continue indefinitely, and the Mohr circle, which
represents the stress state, is “capped” by a failure line beyond which the stresses cannot
increase. If the increase continue, failure occurs.
Mathematically the shear strength (τ f ) of the ground is obtained from the following
formula:
t f = c, + sn , tano ,
(3.2)
τf is thus obtained from a combination of friction and cohesion. In simple terms, cohesion
(c`) refers to the interlocking of clayey plate-like particles. By definition, non-cohesive
materials such as sand and gravel do not contain any significant quantities of clay-like particles, and for practical purposes, the cohesion is taken to be zero in these materials. Their
strength is then characterised in terms of the friction, which is represented mathematically
by the friction angle ϕ′.
3.2.2.1
Pore water pressure
In the undisturbed natural state, the water molecules located between the mineral soil grains
are under pressure corresponding to their depth below the groundwater table. These pressures are called pore water pressures. Any loading action on the soil causes an increase in the
pressure in these water molecules. Unloading has the opposite effect. This process happens
in all soils, but the most important consideration is how fast drainage can occur to allow
these excess or reduced pore water pressures to return to the initial values.
Geotechnical and geophysical methods
3.2.2.2
33
Friction angle ϕ′
Friction angle can typically vary between 25° and 50°. Many soils have friction angles
between 33° and 36°. A clean rounded sand has a friction angle close to 30°.
Non-cohesive (granular) materials have high permeability, so the process of pore water
pressure change on loading or unloading happens rapidly. For practical purposes, it may be
considered to happen instantaneously. This form of loading of the soil is termed drained
loading, with subsequent design calculations being performed with the in situ pore water
pressures and the frictional strength of the soil (friction angle). This type of soil is easier to
deal with since no significant change is expected over time once the construction work has
ended. With the effective cohesion set to zero, the friction angle is the relevant parameter for
granular materials.
The friction angle may be determined in laboratory tests such as the direct shear box
test and the triaxial test or in situ by correlation with CPT, SPT, or dilatometer (DMT)
data.
3.2.2.3
Undrained shear strength (s u)
Cohesive soils such as clays and silts have low permeability. The excess pore water
pressures can take a long time to reduce following a loading. Thus loading of these
soils is often “undrained”, and the relevant strength parameter is the undrained shear
strength (su). It should be remembered that long-term ULS calculations (using ϕ′ and
c′) also need to be carried out for cohesive soils since settlements may happen as pore
water pressures are slowly decreasing, but the short term/undrained condition can be the
critical one.
Although su is one of the most used and useful properties in soil mechanics, helping to
assess potential failure, it is a difficult parameter.
Some possible test methods for su in the laboratory include the triaxial test, the direct
simple shear test (DSS), or the laboratory vane test. In the field, larger-scale in situ vane
testing can be performed, or su can be obtained by correlation from CPT, SPT, or DMT
measurements.
There is no one unique value for su for a particular soil element. It depends on, among
many factors, anisotropy, test type, effective stress, stress history, rate of loading, and temperature effects. The influence of anisotropy/test type is shown for an embankment on soft
ground in Figure 3.3. Very similar conditions exist for other geotechnical problems such as
an infinite slope stability problem or a standard strip or pad foundation. The soil along the
potential sliding surface is partly in compression (represented by anisotropically consolidated undrained triaxial testing [CAUC]), partly in direct simple shear (DSS test) and partly
in extension (CAUE).
An example of some laboratory and field vane test results for Onsøy clay from Southern
Norway is shown in Figure 3.4 (Lunne et al. 2003). The tests were carried out on highquality Sherbrooke block samples. Note that the strength in the test performed on a sample
taken at a location exposed to compression (CAUC) is significantly greater than that from
a simple shear location (DSS), which in turn is greater than the results of the sample taken
from a location subjected to extension (CAUE) tests.
The results of the field vane tests at Onsøy are very similar to those from the DSS tests.
Similar results have been found for clays from other countries, e.g., Sweden (Larsson and
34
Engineering Geophysics
Figure 3.3 Infuence of anisotropy and test type on su for the problem of an embankment on soft
ground.The soil experiences compression below the embankment (CAUC), direct simple
shear (DSS) along the main near-horizontal part of the sliding surface and extension (CAUE)
as the failure surface emerges at ground level. (Image adapted from Bjerrum [1973].)
Figure 3.4 Laboratory and feld strength test results for Onsøy clay.
Source: Data taken from Lunne et al. (2003)
Geotechnical and geophysical methods
35
Mulabdić 1991) and Finland (D’Ignazio et al. 2017). Design engineers then often take su
from DSS or field vane to represent the value of su for a given problem and consider it to be
the “mobilised” su (Leroueil et al. 1990).
3.2.3
Stiffness
Several parameters may be relevant in relation to stiffness. Among the most important are:
•
•
The small strain shear modulus (Gmax).
Parameters obtained from 1D consolidation (oedometer) testing, such as the modulus at
in situ stress (M0), the pre-consolidation stress (pc′), the minimum stiffness around the
pre-consolidation stress (ML), and the modulus number m.
These classical 1D compression parameters are often used in Scandinavian practice (Janbu
1963, 1969) and are illustrated in the plot of 1D compression stiffness against stress in Figure 3.5. Janbu (1963) used the resistance concept to interpret one-dimensional consolidation
in an oedometer test. He defined the tangent modulus (or the constrained modulus), M, as
the ratio of the change in stress (Δσ′) to the change in strain (Δε) for a particular load in
increment, i.e.:
M=
°˜′
°°
(3.3)
Figure 3.5 Norwegian Geotechnical Institute (NGI) interpretation of classical Janbu tangent modulus
versus stress model.
Source: L’Heureux and Long (2017)
36
3.3
Engineering Geophysics
PERSPECTIVES ON PARAMETER RELATIONS
3.3.1
Basic properties
A summary of which basic properties can be obtained from geophysical investigations is
given in Table 3.1.
3.3.1.1
Density and unit weight
An important relationship between geophysical parameters and basic soil properties is that
between shear wave velocity (Vs) and unit weight (γ). Various authors such as Mayne et al.
(2009) and Moon and Ku (2016) have developed relationships between γ and Vs using a
worldwide database of soil tests. They have shown how the resulting relationship depends,
amongst other factors, on soil type, in situ stress, and plasticity. Different equations are
suggested for different soil types. The fit between the measured and predicted data can be
improved by introducing additional parameters such as the plasticity index (Ip) into the relationships. Although these relationships are very useful, it is perhaps more worthwhile to
develop local correlations. An example of such a correlation for Norwegian clays is shown
in Figure 3.6. The data were obtained from L’Heureux and Long (2017).
3.3.1.2
Porosity
By studying the electrical resistivity within sandstones, Archie (1942) established that the
resistivity of a clean, water-bearing rock formation is proportional to the resistivity of the pore
fluid with which it is (fully or partially) saturated. This is defined by the generalised equation:
rb = ar f ϕ −m S w−n
(3.4)
Table 3.1 Use of geophysics to determine soil state parameters.
Geotechnical
parameter
Geophysical
parameter
Example references
Comment
Density (ρ) and
unit weight (γ)
Shear wave
velocity (Vs)
Mayne et al. (2009) and
Moon and Ku (2016)
Locally derived correlations
preferred
Porosity (void
ratio)
Resistivity
Archie (1942)
Relationship complicated by
many overlapping factors
Water content
Dielectric
constant
Hunt et al. (2005)
Huisman et al. (2003)
Shallow surveys
State parameter
for sands (Ψ)
Vs
Guo and Yang (2017),
Wride (Fear) et al.
(2000)
Mostly used in research
studies; not considered
further here
Relative density of
sands (Dr)
Vs
Biryaltseva et al. (2016)
Authors suggest promise in
the relationship, but more
work is necessary.
Coeffcient of in
situ earth pressure
at rest (K0)
Vs
Hatanaka and Uchida
(1999)
Ku and Mayne (2015)
Ideally, need measurements
of Vs in directions of
propagation and polarisation
Geotechnical and geophysical methods
37
Figure 3.6 Relationship between unit weight and Vs for Norwegian clays.
Source: Data from L’Heureux and Long (2017)
where:
ρb and ρf = resistivity of the bulk material and the fluid respectively,
a = constant,
ϕ = porosity,
m = cementation factor,
Sw = water saturation within pores (if soil is saturated, then Sw = 1), and
n = the saturation exponent.
Archie’s law implies that a larger porosity produces a lower bulk resistivity for a given pore
fluid, and the equation has often been used to attempt to directly link resistivity and porosity.
Equation 3.4 does not consider, for example, the influence of the conducting mineral grains.
Such grains, if electrically connected, lower the bulk resistivity of a rock or sediment from
that predicted by Archie’s law (O’Connor et al. 2016). Therefore, Archie’s law has been
modified over the decades, with various values put forward for its component parameters
(Shah and Singh 2005). In addition, it has been found that factors such as bulk density and
stone content have a strong influence on measured resistivity (Fukue et al. 1999; Rey et al.
2006). Similarly, for compacted soils, resistivity is a function of the degree of compaction
and the water content at the start of the compaction process (Hassan and Toll 2015).
38
Engineering Geophysics
From a practical geotechnical engineering point of view, the resistivity cone probe –
RCPTU (i.e., additional module added to the standard CPTU for resistivity measurement) –
has been proposed for in situ determination of the relationships between resistivity and
porosity (Daniel et al. 1999; Kim et al. 2011; Campanella and Weemees 1990).
Given the complexity of the relationships and the influence of various parameters, it
seems impossible at this stage to suggest simple generalised relationships between resistivity and porosity (or resistivity and void ratio) and that development of local correlations are
necessary for practical application.
3.3.1.3
Water content
It is first important to distinguish between water content in the engineering context (w =
gravimetric water content = mass of water divided by the mass of solid particles) and volumetric water content (VWC = mass of water divided by total mass), which is often used, for
example, in agricultural or soil science applications. The two parameters are linked by the
soil dry density (ρd):
VWC = wrd
(3.5)
Using geophysical techniques, VWC can be determined using frequency and time domain
reflectometry (FDR and TDR), neutron probe, and capacitance probe techniques, as well as
GPR methods (Hunt et al. 2005).
In GPR work, the time needed for the electromagnetic wave to travel between a radar
transmitter and receiver is measured and used to estimate the dielectric permittivity of the
soil. The dielectric permittivity estimates can then be converted to VWC using either generalised or site-specific correlation (Topp et al. 1980). A variety of GPR methods have been
used to estimate VWC (Huisman et al. 2001; Huisman et al. 2003).
The TDR technique involves a similar measurement, except in this case via simple small
probes where the waveform is transmitted through short steel rods, such as in the commercially available Theta probe (Gaskin and Miller 1996) or the Meter Group Teros 10 probe
(www.metergroup.com). These forms of measurement have been used successfully in studies of long-term slope deterioration in Northern Ireland and the UK (Stirling et al. 2020;
Lynch 2019).
The neutron probe measures the number of backscattered neutron particles in the soil
surrounding the probe. This number is proportional to the number of hydrogen ions within
the tested volume, and therefore the readings can be calibrated with VWC (Greacen et al.
1981). Hunt et al. (2005) provide some discussion on the relative accuracy of the neutron
probe, capacitance probe, and TDR measurements of VWC with a focus on agricultural
test sites.
3.3.1.4
Relative density of sands (D r )
In geotechnical engineering practice, it is very difficult to obtain undisturbed samples of
sand. Therefore, to obtain estimates of parameters such as the relative density, in situ test
techniques (for example, the CPTU) are commonly used (Baldi et al. 1986). Empirical
CPTU–Dr correlations were developed based on large calibration chamber tests. However, it is well recognised that there are some uncertainties in these methods and that the
Geotechnical and geophysical methods
39
estimates can lead to conservative and uneconomic foundation design. To address some
of these uncertainties, efforts have been made to correlate Dr with Vs. Biryaltseva et al.
(2016) describe some work at a sand site just south of Cuxhaven in Germany. The results
of the work seem promising, but the authors state that further research is necessary before
any definitive correlations can be established and that local relationships may often be
needed.
3.3.1.5
Coefficient of in situ earth pressure at rest (K 0)
The parameter relates the in situ horizontal effective stress (σh0′) to the vertical effective
stress (σv0′):
,
,
o h0
= K 0o v0
(3.6)
Despite its practical importance, K0 is one of the most difficult geotechnical engineering
parameters to assess. Various studies have shown that Vs is related to the effective stress in
the ground (σ′) as follows:
° s` ˙n
Vs = Cs ˝˝˝ ˇˇˇ
˛˝ Pa ˆˇ
(3.7)
where the coefficient Cs depends on soil anisotropy, soil fabric, packing type, degree of
fissuring, over-consolidation, bonding, ageing and other factors; Pa is atmospheric pressure; and n is a stress-dependent exponent (Hardin 1978). Logically, then, Vs is related
to the in situ horizontal and vertical effective stresses and is thus related to K0. Various
equations have been derived linking K0 to Vs (Cai et al. 2011; Cho et al. 2014). Perhaps
the most widely used equation is that of Hatanaka and Uchida (1999), who suggested the
Vs-equal method. A significant benefit of this expression is that only one input value of Vs
is required.
However, as pointed out by various authors, the value of Vs depends on the direction of
propagation and polarisation of the shear wave. Therefore, only uniform isotropic soils have
a single value of Vs. (Ku and Mayne 2013, 2015) proposed equations relating K0 to pairs of
shear wave velocity measurements, e.g., those from downhole tests (Vs-VH) cross-hole tests
(Vs-HV or Vs-HH).
3.3.2
Strength from V s measurements
3.3.2.1
Correlations between s u and V s : some examples
In the opinion of the author, it is not acceptable to develop empirical correlations
between any two parameters without there being a sound theoretical reason for attempting the correlation. As well as the test type, the anisotropy and mode of failure previously discussed, su, of clay depends on a variety of parameters such as the water
content/void ratio, plasticity, current effective stress and stress history (Ladd and Foott
1974). Shear wave velocity is dependent on a very similar set of parameters (Hardin
1978). Thus it is logical to attempt to develop an empirical relationship between these
two parameters.
40
Engineering Geophysics
An overview of some relationships between Vs and su for clays used in Scandinavia and
internationally is presented by L’Heureux and Long (2017). The authors stress the importance of recognising the origin of the data and, in particular, how the values of su were
obtained. Many of the relationships follow the same format, i.e.:
Vs = a sub
(3.8)
The examples given herein are based on correlations between su derived for either the field
vane (FVT) or the laboratory shear (DSS) test following the preceding discussion. The relationship between su,DSS and Vs for Norwegian clays is shown in Figure 3.7a (L’Heureux and
Long 2017). These authors found that the fit between Vs and su,DSS was stronger than the
similar relationships with triaxial compression and extension results. Perhaps this is not
surprising given that the mode of soil deformation is the same in the two sets of tests. It was
found that:
su , DSS = 0.027Vs1.39 with R2 = 0.87
(3.9)
The relationship between su,FVT and Vs for Norwegian clays is shown in Figure 3.7b (Long
and D’Ignazio 2020). Although there is more scatter than for the DSS tests, the relationship
between the two sets of parameters is clear. The best-fit power function relationship between
the two sets of data has an R2 value of 0.66. Also shown in Figure 3.7b is the best-fit trend
line for the DSS test results from Figure 3.7a. There is a reasonably good fit between the DSS
trend line and the field vane data. This confirms the good relationship between these two test
types, as has been found elsewhere.
Figure 3.8a illustrates the relationship between su,FVT and Vs of Swedish and Finnish clays.
The mean trend of the data is consistent with Eq. 3.9 for su,DSS of Norwegian clays. Moreover,
from the regression analysis, it was found that:
su,FVT = 0.062Vs1.23 with R2 = 0.69
(3.10)
For comparison, the Norwegian FVT data from Figure 3.7b are added to Figure 3.8b. There
seems to be a reasonably good fit between the DSS trend line for Norwegian clays and the
field vane data. This would suggest that the positive relationship between su,DSS and su,FVT is
also valid for clays from Sweden and Finland, as suggested by Figure 3.8a.
It is possible also to compare the resulting empirical correlations obtained from the same
laboratory test on different clays. For example, L’Heureux and Long (2017) derived an
expression for the relationship between su from triaxial compression (CAUC) tests carried
out on high-quality Norwegian clay samples. Further, Long et al. (2017) derived an empirical correlation for similar tests carried out on high-quality samples of Swedish clays. It was
found from these two studies that, although the same form of equations applied, the relationships were not identical, and it is necessary to develop local correlations and not rely on
correlations developed for soils elsewhere.
A very good illustration of this important point lies in a comparison between Vs profiles
for soft to medium stiff southern Norwegian clays and stiff to very stiff London clay from the
UK. Intuitively one would expect the London clay Vs values to be much higher than those of
Geotechnical and geophysical methods
Figure 3.7 (a) suDSS versus Vs for Norwegian clays (L’Heureux and Long 2017). (b) suFVT versus Vs for Norwegian clays (Long and D’Ignazio 2020).
41
42
Engineering Geophysics
Figure 3.8 suFVT versus Vs for (a) Swedish and Finnish clays and (b) Swedish and Finnish clays compared
to Norwegian clays (Long and D’Ignazio 2020).
the Norwegian soils. However, as can be seen in Figure 3.9, the ranges of values recorded
are more or less identical. The reason for the low values in the London clay is likely to be
strongly linked to the fundamental stress anisotropy of these materials (Butcher and Powell
1996) and their fissured nature (Hight et al. 2003). However, it is clear that correlations
derived for one location or for one specific soil type need to be applied only with great caution to another soil type.
3.3.2.2
Relationship between V s and peak friction angle
Compared to the relationship between su and Vs, much less work has been done on the
determination of effective friction angle from Vs. Yoo et al. (2018) proposed an empirical relationship between ϕ′ and Vs based on a series of drained triaxial tests on clean
sands and natural gravels. Their work uses the previously reported linear relationship
between Vs and the major principal stress at failure (σ1′; see Figure 3.2). The correlation
was improved by introducing the confining stress (σ3′) into the relationship. Cha and
Cho (2007) outlined a two-step approach to develop an empirical relationship involving measurement of Vs using bender elements in oedometer tests and parallel direct
shear tests to determine ϕ′. A non-linear correlation between Vs and the void ratio was
obtained. Yoo et al. (2018) pointed out that the disadvantage of this approach is that it
requires two parallel tests and is difficult to apply in practice as a single empirical equation cannot represent it.
Geotechnical and geophysical methods
43
Figure 3.9 Comparison of Vs-VH values for London clay and range for South Norway clays. (L’Heureux
and Long [2017].) (London clay data from Powell and Butcher [1991], Butcher and Powell
[1995], Gordon et al. [1996], Matthews et al. [1996], Menzies and Matthews [1996], Hight
et al. [2003], Hight et al. [2007], Clayton [2011], and Lessi-Cheimariou et al. [2019].)
3.3.3
Stiffness from V s measurements
3.3.3.1
Small strain shear modulus from V s measurements
The main input value in SLS calculations is soil stiffness. According to elastic theory, the
small strain shear modulus (Gmax) may be calculated from the shear wave velocity using the
following equation:
Gmax = rVs2
(3.11)
where:
Gmax is the shear modulus (in Pa),
Vs is the shear wave velocity (in m/s), and
ρ is the total mass density (in kg/m3).
Small strains are typically considered to be those of approximately 0.001% strain and less
(Atkinson and Sällfors 1991). Unfortunately, stiffness is highly non-linear and decreases
44
Engineering Geophysics
with increasing strain. The design engineer must choose a stiffness consistent with the strain
that occurs in the soil around the structure under consideration. Various techniques exist to
allow Gmax, as determined directly from Vs, to be reduced depending on the likely strains.
Perhaps the most common technique is the use of the hyperbolic stiffness degradation formula suggested by Fahey and Carter (1993) as follows:
˛ τ ˘ˆ
G
˘
= 1− f ˙˙˙
˙˝τ max ˘˘ˇ
Gmax
g
(3.12)
where τ and τmax are the applied shear stress and shear strength, respectively, and f and g are
fitting parameters. The ratio τ/τmax is analogous to the inverse of the factor of safety. Values of
f = 1 and g = 0.3 appear to give reasonable estimates for unstructured and uncemented geomaterials and provide a general fit to experimental data (Mayne et al. 2009). Published literature contains some practical examples of the use of this expression to predict the response
of footings, in terms of the settlement versus applied stress, and on various soil types such
as sands (Mayne 2000; Poulos 2021; and (Kim et al. 2015), soft clay (Mayne 2000), and soft
clayey silt (Mayne 2005).
Some details for the Class A footing response prediction on a trial structure founded on
soft silt (known locally as “sleech”) in Belfast, Northern Ireland, are shown in Figure 3.10.
Mayne (2005) used the measured Vs values and the stiffness degradation formula described
in Eq. 3.12 to obtain a Class A estimate of the footing settlement. He used the following
equation for settlement (ρ) prediction:
˜=
(
qdI GFE 1−° 2
)
2
ˆ q 2Gmax (1+ ° ) 1−˘˘˘
ˇ˘ qult Figure 3.10 Belfast footing test. (Redrafted from details in Lehane [2003] and Mayne [2005].)
(3.13)
Geotechnical and geophysical methods
45
where:
q
= applied stress,
qult = ultimate capacity of footing,
d
= footing dimension,
IGFE = correction factor based on footing shape, stiffness variation with depth,
embedment, and foundation rigidity (Mayne and Poulos 1999), and
ν
= Poisson’s ratio.
It can be seen that the predicted and measured data are in good agreement, especially
to applied stress of about 80 kPa, at which point the onset of bearing capacity failure
occurs.
3.3.3.2
Correlations between V s and 1D compression parameter
General
Many researchers have attempted to develop relationships between shear wave velocity and
1D compression parameters as obtained from oedometer testing. This includes work on preconsolidation stress (pc′), the tangent modulus at in situ vertical effective stress (M0), and the
tangent modulus at pc′ (ML). See Figure 3.5.
Pre-consolidation stress from V s
Pre-consolidation stress (pc′) estimates from shear wave velocity have been made using:
•
•
•
Theoretical relationships,
From bender element testing on oedometer samples, and
Empirical relationships.
Similar to the approach used to estimate K0 previously described, Ku and Mayne (2014)
outline a method for determining the stress history profile in soils using pairs of shear wave
velocity measurements, e.g., Vs-VH and Vs-HH. Huang et al. (2016) describe how the uncertainty associated with empirical relationships between Vs and pc′ can be improved by the
Bayesian updating approach.
If Vs is measured (using bender elements) during oedometer testing, it is possible to estimate pc′ from the resulting Vs–consolidation stress (σvc′) profile. Some examples for a peat
site at Uitdam in The Netherlands and for a soft clay site at Kwangyang-A in Korea are
shown in Figure 3.11. A break in the semi-logarithm Vs–σvc′ curve can be seen for the Uitdam
data at approximately 12 kPa and for Kwangyang-A data at 20 kPa. This is considered to
be a stress history effect, and Yoon et al. (2011) suggested that pc′ can be obtained directly
from the point of intersection of the two resulting straight lines. The values of 12 kPa and 20
kPa are consistent with the pc′ values obtained from 1D oedometer tests by Zwanenburg and
Erkens (2019) and Yoon et al. (2011), respectively.
46
Engineering Geophysics
Figure 3.11 Vs versus σvc′ for peat and soft clay. (Data from Trafford and Long [2020] and Yoon et al.
[2011].)
Good correlations between Vs and pc′ have been reported for a worldwide database of
intact clays by Mayne et al. (1998) and specifically for Norwegian clays by L’Heureux and
Long (2017), for Swedish clays by Long et al. (2017), for Finnish clays by Di Buò et al.
(2018), and for Jiangsu clays from China by Duan et al. (2019). An example of such an
approach for Eastern Canada clays is shown in Figure 3.12. The scatter in the data is inevitable given that the sampling and testing were carried out using different methods by different groups over many years. However, the overall trend is clear, and the data fit well with the
previously published correlations.
M 0 and M L from V s
Similar correlations between Vs and M0 have been proposed for Norwegian and Finnish clays
by L’Heureux and Long (2017) and Di Buò et al. (2018), respectively, and between Vs and
ML by the former authors.
Geotechnical and geophysical methods
47
Figure 3.12 pc′ versus Vs for Eastern Canada clays. (Data from Bozozuk [1972], Lo et al. [1976], Leroueil et al. [1983],Tavenas and Leroueil [1979], Leroueil et al. [1983], Lefebvre et al. [1994],
Hamouche et al. [1995], Leroueil et al. [2003], Tanaka et al. [2003], Bouchard [2020] and
Burnotte et al. [2004].)
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Leroueil, S., F. Tavenas, L. Samson, and P. Morin. 1983. Preconsolidation pressure of Champlain
clays, Part 2, Laboratory determination. Canadian Geotechnical Journal 20:803–816.
Lessi-Cheimariou, A., I. Tromans, N. Hadlow, M. Floyd, and J. Pateman. 2019. A novel technique for
deep seismic cone tests in challenging ground conditions. Proceedings of the 2019 SECED Conference – Earthquake Risk and Engineering Towards a Resilient World, Greenwich, London.
L’Heureux, J.-S., and M. Long. 2017. Relationship between shear wave velocity and geotechnical
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Lo, K. Y., M. Bozozuk, and K. T. Law. 1976. Settlement analysis of the Gloucester test fill. Canadian
Geotechnical Journal 13(4):339–354.
Long, M., and M. D’Ignazio. 2020. Shear wave velocity as a tool for characterising undrained shear
strength of Nordic clays. Proceedings of the 18th Nordic Geotechnical Meeting (NGM), Helsinki,
Finland (Conference held virtually January 2021).
Long, M., T. Wood, and J.-S. L’Heureux. 2017. Relationship between shear wave velocity and geotechnical parameters for Norwegian and Swedish sensitive clays. In Landslides in Sensitive Clays,
Advances in Natural and Technological Hazards Research 46, ed. V. Thakur, J.-S. L’Heureux, and
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Lunne, T., M. Long, and C. F. Forsberg. 2003. Characterisation and engineering properties of Onsøy
clay. Proceedings of the International Workshop on Characterisation and Engineering Properties
of Natural Soils, Singapore.
Lynch, K. N. W. 2019. The Impact of Weather on Suction and Moisture Content in a Vegetated Infrastructure Cutting Through Lodgement Till. PhD Diss., School of Natural and Built Environment,
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119(2):84–95.
Mayne, P. W. 2000. Enhanced geotechnical site characterization by seismic piezocone penetration
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the 3rd International Symposium on Deformation Characteristics of Geomaterials, Lyon, France,
September 2003.
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Chapter 4
Gravimetric method
Olivier Francis
4.1
INTRODUCTION
Surface gravity measurements reflect crustal thickness and density. They are helpful to detect
low-density anomalies (voids or cavities) and high-density anomalies (mineral deposits).
Gravity methods are non-invasive and easy to implement but could be time-consuming. By
nature, mass configurations resulting from observed anomalies are non-unique. Nevertheless,
gravity measurements are helpful in detecting anomalies for further investigation by other
methods; see the cases in chapters 18 and 48.
4.2
FUNDAMENTAL PRINCIPLE
Newton’s law of gravitation provides a mathematical expression of the attraction between
masses: two masses attract each other proportionally to the product of their masses and conversely proportional to the square of their distance. The proportional coefficient is known as
the universal gravitational constant G. It is the first constant ever introduced in physics and
is the less precisely known constant still today with a relative standard uncertainty of 2.2 ×
10−5 (Mohr et al. 2018). Gravity is the smallest interactive force in nature.
In gravimetry, the measurand is the acceleration. As the precision of the gravimeters is
around 1 ppb, the acceleration is expressed in nm/s2. However, the cgs unit “Gal” is still
widely used and tolerated: 1 Gal = 1 cm/s2 or 10 nm/s2 = 1 microGal.
Gravity measurements are not restricted to the Earth’s surface. They are performed in
boreholes, mines, and space as well. A vast amount of data are collected on moving platforms
like ships, submarines, helicopters, aircraft, as well as with artificial satellites (equipped with
accelerometers or satellite-to-satellite tracking of two satellites orbiting in tandem).
In fundamental geodesy, gravity data play a central role in the determination of the geoid
(the equipotential corresponding to the mean sea level). The geoid is a complex surface with
undulations related to density variations, stratifications, faults, and so on. The geoid tells us
in which direction a liquid is flowing. It is required information for engineering applications
like laying pipelines. Without the geoid, we could not transform GPS heights into altitude
and vice versa.
Gravitational acceleration is a vertical vector perpendicular to the geoid. Gravimeters
measure its magnitude integrating effects from the whole Earth. One measurement is not
enough to collect information on the exact distance and azimuth of external masses. It
requires a network of gravity stations whose design in terms of number and station spacings
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(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
Figure 4.1 Archetypical horizontal gravity profles produced by underground density constrasts. The
surface gravity signals are displayed in (a)–(c) and (d) – (j).The corresponding density anomalies are placed below each example: (a) a spherical void of 4 m in diameter buried in limestone 1 mbgl (meters below ground level); (e) a 2 m wide dike surrounded by clay; (f) a 4 m
offset fault between granite and clay; fnally, spherical voids of 2 m in diameter buried at: (j) 2
mbgl, (k) 3 mbgl, and (l) 4 mbgl, respectively.The dashed lines correspond to the limit of the
detectability with today gravimeters. (Figures drawn by Anna Bondo Medhus.)
must be adapted to the terrain conditions and targets. Most of these applications necessitate
only relative gravity changes from place to place, whatever the absolute value is. Relative
measurements are quicker and cheaper than absolute measurements. Gravity variations are
measured on specific areas (it could be the entire Earth’s surface) or temporal variations at
a specific site.
The objective of engineers resorting to gravimetry is the detection of density contrasts
below the Earth’s surface: geological structures like faults and different layers, cavities, mineral deposits, salt domes, lateral heterogeneities responsible for the deficiency or abundance
of mass. See some examples of models and corresponding gravity anomalies in Figure 4.1.
In general, gravity alone is not enough to determine the origin of anomalies. It has to be
Gravimetric method
55
complemented by other information or techniques, e.g., levelling, electromagnetic, seismic
refraction or reflection. Besides mineral and petroleum exploration, gravity has been widely
used in archaeology. Recently, it has been used as a monitoring tool in hydrology (aquifer
monitoring), glaciology (ice melting), and volcanology (magma motion).
Gradiometry uses the gradient of the gravity acceleration. The derivative eliminates the
large constant part and enhances the high-frequency part of the gravity signal. Thus it is
more sensitive to nearby objects. On a moving platform, the common acceleration mode is
cancelled. The accuracy is in the order of 1 Eötvös (10−9 s−2). The technology is intensively
used in airborne and satellite applications, and the technology is still undergoing a lot of
progress.
4.3
ACQUISITION
A gravity meter or gravimeter is scientific equipment designed to measure the acceleration
due to gravity (so-called g). All gravimeters measure the relative or absolute acceleration of
a test mass under the gravitational field of surrounding masses. Relative gravimeters provide only relative differences of g between sites or temporal changes at a stationary point.
Relative gravimeters are based on the same principle: to balance the force of gravity by a
stable force. It could be the restoring force produced by quartz or metallic springs. The more
precise but less mobile gravimeter superconducting gravimeter (SG) uses an ultra-stable
magnetic field produced by an electric current trapped in a superconducting niobium coil
maintained at the temperature of a few Kelvin.
Relative gravimeters data exhibit an instrumental drift due to the stretching of the springs.
The magnitude is determined by the stability of the balancing force ranging from a few
microGal per year (SG magnetic field) up to a few microGal per hour (spring). Absolute
gravity meters are ballistic instruments. The acceleration is obtained from the measurement
of a free-fall test mass (a corner cube or even atoms) whose position is measured with a laser
interferometer timed with a highly accurate atomic clock. The accuracy is about 2 microGal.
Absolute gravity measurements are more expensive than relative ones: they take more time,
the equipment is less portable, and they necessitate a larger and more stable base/pier, preferably indoor, and more electric power. It is recommended to choose base stations at absolute
gravity stations that provide the calibration factor of the relative gravimeter.
Gravimeters on moving carriers are basically the same as land gravimeters. They operate on a stabilised platform. Additional information, such as position, velocity, and altitude
provided using several GPS antennas, is needed. Borehole measurements require dedicated
equipment and experienced operators. Modern gravimeters are equipped with an acquisition
system. They provide digital data and often include geophysical corrections.
4.4
PROCESSING
For the relative gravimeter, the first step consists of estimating instrumental drift. The operator repeats acquisition at well-chosen base stations during field measurements. The drift rate
is generally assumed to be linear, which is reasonable for most surveys.
Then the philosophy is to compare actual gravity data with the gravity field produced by
an ellipsoidal Earth with homogeneous composition. First, we need to correct data for known
gravitational geophysical effects. Most textbooks or manuals on gravity measurements
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describe the corrections for the atmospheric pressure effect, gravity tides, latitude, altitude
(free-air effect), Bouguer correction, terrain/topographic correction, and a global Earth
gravity field model. This last step allows separating regional anomalies from large-scale
anomalies.
Gravity measurements on moving platforms are, in general, less precise by a factor of
100–1000. This is because they are subjected to inertial accelerations and tilt of the platform
that requires additional corrections. Therefore, they have limited accuracy.
Global models of the Earth’s gravity field derived from satellites are widely available.
Although their resolution is coarse, removing their contribution makes it possible to enhance
features of the regional or local gravity field. Thereby a more detailed residual map of socalled gravity anomalies produced by subsurface density variations was obtained.
4.5
INTERPRETATION AND MODELLING
Historically, the final products were profiles and maps of gravity anomalies from the high
and low values, location at the surface (not depth) of cavities or excess densities. However,
once digital maps became available, simple mathematical operations such as gradient or
second horizontal derivatives illuminated the boundaries of the buried masses/cavities.
The interpretation of gravity anomalies is based on a priori knowledge of rock densities
that are adjusted until the gravity profile matches the predicted gravity field (Monte Carlo
simulation). This approach is the direct or forward method and is still the most commonly
used for mineral prospection and exploration.
Another approach consists of estimating the type (positive or negative) and extension
(volume) of density variations from gravity anomalies at the Earth’s surface. This is the
indirect method that relies on inverse methods. The methodology is generally very specific
to each project. It might imply the use of very sophisticated mathematics and substantial
computational resources. Unfortunately, different body configurations can generate the same
gravity anomalies. The gravity inverse problem does not have a unique solution. It is the perfect example of an ill-posed problem. In order to overcome the issue, additional information
or constraints are needed like geological information, magnetic maps, geological drilling,
seismic reflection or refraction profiles, etc. Inverse modelling is still evolving and has a
bright future. However, it requires many resources.
4.6
RESOLUTION AND UNCERTAINTIES
For practical applications, gravity measurements are carried out on a network of stations.
The observations are adjusted to estimate the instrument drift and the gravity differences
between the stations. The final precision is about 5–10 microGal. The size of the network is
the key parameter. A small spatial network with high point density provides a better resolution and precision. The precision of a gravity survey also depends on the acquisition time at
the measured sites.
It is impossible to present general uncertainties on the final interpretation of gravity surveys or results for gravity data inversion. They are dependent on the quantity and quality of
complementary data included in the analyses and are specific for each case. Engineers are
more concerned with small-scale surveys with a size of about 10–100 m. These microgravity
Gravimetric method
57
networks provide the most precise observations. Typically, two parameters control the detectability of the object responsible for an anomaly: depth of burial and its density contrast.
To illustrate the sensitivity and what object size can be detected with the present-day
precision of gravimeters, Figure 4.1g, 4.1h, and 4.1i shows the expected gravity signal of an
empty sphere of a 1 m radius at the surface as a function of its burial depth, as illustrated in
Figure 4.1j, k, and l. The sphere can easily be detected to a depth of 4 m. For lower density
contrast between the sphere and the material around it, the signal amplitude decreases proportionally to the difference in densities.
REFERENCES
Fairhead, J. D., and M. E. Odegard. 2002. Advances in gravity survey resolution. The Leading Edge
21:36–37.
Hinderer, J., D. Crossley, and R. J. Warburton. 2007. Gravimetric methods – superconducting gravimeters. Geodesy 3:65–122. ISBN: 978-0-444-51928-3.
Mohr, P. J., D. B. Newell, B. N. Taylor, E. Tiesinga, and M. Douma. 2018. The NIST Reference on Constants, Units, and Uncertainty. Codata. https://physics.nist.gov/cgi-bin/cuu/Value?bg|search_for=G
(accessed March 29, 2022)
Niebauer, T. 2007. Gravimetric methods – absolute gravimeter: Instruments concepts and implementation, in treatise of geophysics. Geodesy 3:43–64. ISBN: 978-0-444-51928-3.
Seigel, H. O. 1995. A Guide to High Precision Land Gravimeter Surveys, 122 p., Scintrex Limited.
https://scintrexltd.com/wp-content/uploads/2017/02/Guide-High-Precision-Land-Gravimeter-Surveys.pdf (accessed March 29, 2022).
Torge, W. 1989. Gravimetry. Berlin: De Gruyter, 465 p. ISBN: 9783110107029.
Van Camp, M., O. de Viron, A. Watlet, B. Meurers, O. Francis, and C. Caudron. 2017. Geophysics
from terrestrial time-variable gravity measurements. Reviews of Geophysics 55:938–992.
Chapter 5
Magnetometry method
Joanna Hamlyn
5.1
FUNDAMENTAL PRINCIPLES
The successful application of magnetometry for geotechnical site investigation requires an
understanding of how the Earth’s magnetic field is produced, how it is perturbed by the magnetic properties of the target, and to what degree this effect is measurable. Magnetometry
can be used for archaeological investigation (see the case in chapter 17) and for locating
anthropological features that contain ferrous materials such as UXO (see the case in chapter
12) and buried structures (see the cases in chapters 12, 13, and 19). Magnetometry can also
be used to map geological features such as igneous intrusions, lava flows, and magnetite ore
bodies.
5.2
BASIC PHYSICS
5.2.1
Earth’s total magnetic field
The convection of molten iron in the Earth’s outer core generates a magnetic field known
as the Earth’s total magnetic field (ETMF). To a first approximation, the ETMF resembles
the field from a large bar magnet aligned subparallel to the geographic axis (Figure 5.1a).
Based on the 2020 World Magnetic Model (WMM), magnetic north is currently in the Arctic
Ocean, situated ~400 km from the geographic north.
The Earth’s magnetic field conforms to the same principles as the magnets we are familiar
within our day-to-day lives (from those holding bottle openers to the fridge door to the black
stripe on a credit card). All magnets are dipolar, having a positive and a negative end (or
pole) where magnetic field lines converge. Similarly, the Earth’s magnetic field is vertical
at its north and south poles and horizontal at its equator (Figure 5.1a); however, the Earth’s
magnetic field is spatially complex, and its orientation and magnitude change with latitude
and longitude. The total strength, or intensity, of the magnetic field is measured in units of
nanoteslas (nT) and is three times larger at the poles (~67,000 nT) than in equatorial regions
(~22,000 nT).
Magnetometry for geotechnical site investigation measures either the total strength or the
rate of change (gradient) of the ETMF at a given location. The total field strength is independent of the direction of the field (a scalar value), whereas the gradient is a vector in the
spatial (X and Y) and vertical (Z) directions.
The magnetic field observed at a given location is the sum of the background ETMF and
the magnetic field of small bodies in the near-surface. The magnetic field caused by the local
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Figure 5.1 Illustration of the magnetic method. (a) Schematic of ETMF, created by convection in the
Outer Core. Note: At the poles, the direction of the feld is vertical, and the density of
feld lines is greatest. (b) Demonstration of inverse square law. Example amplitudes of the
measured total feld at heights r and 2r from the causative body. (c) Example magnetic
anomaly and analysis. Dipole observed in the total feld (single-sensor data collection) and
corresponding analytical signal, reduction to pole, and application of the half-width analysis.
subsurface bodies is proportional to two intrinsic material properties called magnetic permeability and magnetic susceptibility.
•
•
Magnetic permeability (µ): A measure of the magnetisation that a material obtains in
response to any applied magnetic field
Magnetic susceptibility ( k ): Closely related to magnetic permeability, a measure of the
degree to which the magnetisation of a material strengthens or weakens the local field
Magnetometry method
61
The magnetic permeability and susceptibility of target features allow them to be detected in
site investigation. Perturbations in the ETMF derive from two different kinds of magnetism:
induced or permanent (remanent).
5.2.2
Induced magnetisation
When magnetically susceptible materials are placed within a magnetic field, the field itself
causes the material to act as a magnet, referred to as induced magnetisation. In the region
around the induced material, the total magnetic field is the sum of the undisturbed total
magnetic field plus the induced field. The magnetic susceptibility of a material determines
whether the total magnetic field is strengthened or weakened by the induced field.
5.2.3
Permanent magnetisation
Permanent magnetisation is also known as remanent magnetisation. In induced magnetisation, a susceptible material retains no permanent magnetisation once the inducing field is
removed. In contrast, magnetic materials with permanent magnetisation exhibit a magnetic
flux after removing an external magnetic field. Such materials are described as ferromagnetic and occur naturally in some igneous rocks, such as basalt, or artificially, such as in iron
alloys. Materials may possess both induced and permanent magnetisations, and the resulting
measured field is a vector addition of both fields.
Thermoremanent magnetisation (TRM) is the permanent magnetic signature acquired as
magnetic grains cool through the Curie temperature. Above the Curie temperature, the grains
align with the current magnetic field. As the material cools, the orientation becomes fixed.
Pottery, kilns and burnt structures, and ancient fireplaces have a magnetic field aligned with
their last cooling and create distortions in the present-day magnetic field. TRM provides the
basis for locating such archaeological features.
5.3
PROPERTIES RELATED TO LITHOLOGIES AND TARGETS
Magnetic materials at or below the Earth’s surface create spatial deviations from the expected
total magnetic field. Therefore, the measurement of the total magnetic field can be used to
assess the subsurface for the presence or absence of these materials. As a result, geological
or anthropological features can be mapped on various scales.
Magnetic anomalies occurring from geological sources are caused by the presence
of magnetite or similar iron-rich minerals. It should be noted that iron oxide is antiferromagnetic and so does not cause magnetic anomalies. Most rocks have a very low
magnetic susceptibility as they contain less than 1% magnetite. Mafic igneous rocks tend
to have the highest percentages of magnetite, whereas sedimentary rocks tend to contain the least. However, the magnetic susceptibility of rocks is extremely variable, with
significant overlap between different lithologies. Therefore, it is impossible to identify
a specific rock type or formation based solely on its magnetic characteristics. Common
geological causes of magnetic anomalies include igneous intrusions, lava flows, and
magnetite ore bodies.
Buried anthropological features often contain iron alloys and can create a range of distortions in the local magnetic field. A buried object or feature can be detected if it is either
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magnetic or where materials that should be uniformly magnetic have been displaced. Example targets of interest to site investigation may include burn pits, pottery/bricks, burials,
culverts, ferrous services, UXO, reinforced foundations, tunnels, and mine entries.
5.4 THEORETICAL AND PRACTICAL
LIMITATIONS AND COMPLEXITIES
5.4.1 Time variation
The basic magnetic theory outlined previously has focused on spatial variations in the magnetic field and the localised influences of magnetic materials at the surface. However, it
should be noted that the ETMF also varies temporally on multiple time scales. There is a
slow drift of the magnetic field due to convection in the outer core; more relevant for site
investigation are short-period fluctuations related to the effects of the solar wind, magnetic
storms, and micro-pulsations. Magnetic data can be corrected for these variations using a
base station, which would be required for long-duration, regional-scale geological mapping.
However, for geotechnical site investigation, base station corrections are not necessary if the
anomalies can be traversed in less than 5 min. This is often the case on local-scale sites with
high acquisition rate magnetometers.
5.4.2
Detectability
The detection of a magnetic target depends firstly on the distance between the target and
the observation point. Magnetic fields obey an inverse cube law, where the field strength of
1
magnetic dipoles decreases rapidly as the inverse cube of distance ( 3 ) (Figure 5.1b). Furtherr
more, the magnetic intensity of a dipole is significantly larger along its axis than along a line
at right angles to the dipole. Therefore, a key control on detectability is the distance between
the target and the magnetometer, as well as its geometry and orientation.
A further consideration for detectability is the mass of ferromagnetic content that the
object contains rather than the mass of the object itself.
Finally, any geophysical measurement is vulnerable to background noise. Noise consists
of any distortions in the magnetic field not caused by the intended target of the investigation.
If this noise is similar to or greater in magnitude than the response of the target, the target
may not be reliably detected. Therefore, the ability to resolve a magnetic target is highly
site dependent. Sources of background noise include buildings, roads, ferrous-made ground,
fences, pipelines, reinforced concrete, vehicles, and electrical cables. Magnetometry field
crews take great care to ensure that they demagnetise themselves prior to surveying (i.e.,
steel watches, jewellery, etc., is removed).
5.5
NON-UNIQUENESS, RESOLUTION AND UNCERTAINTY
In any potential field method, the field observed can be produced by an infinite combination of
sources and geometries; hence the interpretation of data is said to be non-unique. Therefore,
when a magnetic feature is observed, the use of additional information such as historical/geological maps and construction drawings aids in the consideration of only realistic scenarios.
Magnetometry method
63
Table 5.1 Suggested traverse spacings for common targets.These are largely controlled
by the expected spatial extents of the magnetic distortion caused by the
target feature, given that the magnetic feld obeys an inverse cube law.
Target
Traverse spacing (m)
Archaeology
UXO
Mine entries
Structures
Buried waste
Geological mapping
0.5–1
0.5–1
<2
<5
5+
5+
Magnetometry for geotechnical site investigation relies on the ability to observe the target’s effect on the ETMF to the extent required to draw interpretations on its origin. It is not
possible to look deeper into the subsurface by changing profile spacing, nor is it possible to
increase the strength of the field. The ability to resolve deeper objects relies on magnetically
quiet site conditions and the ability to measure very broad and subtle responses. To achieve
this, the survey area should always extend well beyond a target feature to observe its contrast
with the background values at the site.
The resolution of a magnetic data set is controlled by the spacing of parallel survey traverses along which the data are acquired (the X-spacing/axis) and the density of data points
along the traverse (the Y-spacing/axis). Modern magnetometers usually collect data at rates
between 10 and 200 Hz, making data resolution very high along the Y-axis. Table 5.1 suggests maximum traverse spacings for common targets; however, spacing mainly depends on
the nature of the target and site conditions.
5.6
ACQUISITION REQUIREMENTS
A magnetic survey is often useful for diverse targets if the practical limitations outlined in
Section 5.4 are absent or overcome. The data set is usually acquired on a survey grid consisting of a series of parallel traverses. North–south aligned traverses are ideal, as this creates
the largest-amplitude anomalies regardless of latitude. However, this is not always possible
given site constraints and logistical considerations.
The total magnetic field is usually measured by a single magnetometer. The most common types of magnetometer include proton precession, Overhauser, Caesium vapour, optically pumped and fluxgate magnetometers. In some cases, it is useful to employ a second
sensor (either in vertical or horizontal alignment) to measure the gradient of the ETMF. At
this point, they are referred to as gradiometers. Gradiometer surveys enhance small, shallow magnetic anomalies and can suppress broad regional changes, making them particularly
useful in geologically complex areas. They are also not affected by temporal variations in
the ambient magnetic field since it can be assumed that temporal changes affect both sensors
equally. Fluxgate magnetometers are most commonly used in this format. Even though they
are not the most accurate sensor type, fluxgate magnetometers are relatively inexpensive,
robust, precise, and sensitive. This makes them ideal for archaeological surveys, where the
main targets tend to be very subtle and very shallow.
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Recent developments include mounting multiple gradiometers on bespoke non-magnetic
carts with RTK GPS positional control. This configuration enables increased resolution and
greater productivity, which is particularly useful for UXO and archaeological investigation.
Specialist lightweight sensors can also be mounted onto drones allowing for magnetic mapping of large areas with difficult terrain.
5.7
INTERPRETATION
As all magnetic fields are dipolar, the superimposition of a dipolar anomaly on the ETMF
creates perturbations observed as localised relative increases and decreases in field strength
intensity. However, the observation of monopolar magnetic anomalies can occur when the
ends of the dipole are far apart or vertically aligned (e.g., the top and bottom of a buried
pile). As the intensity of magnetisation is a vector, the direction of magnetisation controls the
shape of the observed anomaly. For these reasons, total field magnetic anomalies are more
difficult to interpret and are often less closely related to the shape of the causative body than
with other total field techniques, such as micro-gravity.
The interpretation of the measured total magnetic field is further complicated by the horizontal displacement of the anomalies relative to the source due to the non-vertical nature of
the magnetic field. There are two common methods to reduce this complexity: by processing
data to show the “analytic signal” or by “reduction-to-pole”.
The analytic signal is used to simplify magnetic dipoles into a single positive peak over
the areas of the highest magnetic gradient (Figure 5.1c). Therefore, the maxima in the analytic signal indicate the locations of the greatest magnetic contrast. Reduction-to-pole simplifies the observed data by simulating the magnetic response to a vertical inducing field, i.e.,
as it would be recorded at the magnetic north pole (Blakely 1995). In this way, the maximum
response should be directly above the causative body.
5.7.1
Depth estimation
Considering the wavelength and amplitude of a given magnetic anomaly makes it possible to
estimate target depth. The depth of the magnetic source can be estimated from the shape of
the analytic signal using a simple half-width rule; the half-width of the anomaly is approximately equal to the depth of the causative body, providing the data used are perpendicular
to the strike of the anomaly (Figure 5.1c). If two sources exist closer together than they are
deep, interference occurs, and the depth estimate will be wrong.
Depth estimation from the total magnetic field data (rather than the analytic derivative)
can also be achieved via a half-width approach. However, some a priori knowledge about
the shape of the source (e.g., sphere, vertical, or horizontal cylinder) is required, and a vertical background field is assumed, which requires reduction to pole. Depth estimation is fully
explored in Breiner (1999).
FURTHER READINGS
This chapter has simplified and selected information directly relevant to the application
of magnetometry to geotechnical engineering. However, further reading for more detailed
explanations and wider knowledge is highly recommended. Key texts include:
Magnetometry method
65
Blakely, R. 1995. Potential Theory in Gravity and Magnetic Applications. Cambridge: Cambridge
University Press. http://doi.org/10.1017/CBO9780511549816
Breiner, S. 1999. Applications Manual for Portable Magnetometers. Sheldon Technical Report, 1.
Sunnyvale, CA 94066, Geometrics. http://doi.org/10.6067/XCV8DJ5JD6
Kearey, P., and M. Brooks. 1984. An Introduction to Geophysical Exploration. Malden, MA: Blackwell Science.
Reynolds, J. M. 1997. An Introduction to Applied and Environmental Geophysics. Chichester: John
Wiley and Sons Limited.
Roest, W., J. Verhoef, and M. Pilkington. 1992. Magnetic interpretation using 3-D analytic signal.
Geophysics 57:116–125. http://doi.org/10.1190/1.1443174
Chapter 6
Direct current resistivity methods
Douglas W. Oldenburg, Seogi Kang,
Lindsey J. Heagy, and Michael Maxwell
6.1
INTRODUCTION: SURVEY
A DC resistivity survey, sometimes referred to as ERT (electrical resistance tomography) or
ERI (electrical resistivity imaging), is a geophysical technique used to estimate the electrical
resistivity distribution of earth. The method can provide important information for geotechnical problems, groundwater hydrology, and mineral exploration.
The electrical resistivity of earth or human-made materials quantifies how easily an electric current passes through it. As illustrated in Figure 6.1, the resistivity of materials varies
by many orders of magnitude. Resistivity is expressed as 𝝆 with units 𝛀m, and its reciprocal,
conductivity, is expressed as 𝞂 with units S/m (siemens per meter). The words are often used
interchangeably, for example, a low-resistivity soil can be referred to as a conductor, while
a high-resistivity bedrock can be referred to as a resistor.
The geologic units in Figure 6.1 have resistivities that span seven orders of magnitude,
and soils encountered in geotechnical problems can vary by three orders of magnitude. Each
geologic unit is portrayed with a range of resistivity values; for example, tills, clays, sands,
and gravels can vary by more than a factor of 10 in resistivity. This arises because the resistivity depends on many factors: rock type, porosity, connectivity of the pores, the resistivity
of the fluids in the rock, and the metallic content of the rock matrix. Therefore, knowing the
resistivity does not uniquely identify the rock unit, soil type, or fluid type. For instance, the
figure shows there are eight possible classifications for a unit that has a resistivity of 50 𝛀m.
Before deciding whether to use a DC resistivity survey, an engineer should have a general understanding of how resistivity is related to geological units and geotechnical requirements. Resistivity often correlates well with grain size: cohesive soils with small grain size,
e.g., clays, have a lower resistivity than non-cohesive soils like well drained sand and gravel
deposits, which have larger grain sizes. Knowing the strength of topsoils is important in
construction projects. An increasing clay content generally reduces the soil strength. Hence,
soft soil may be identified by low resistivities (see the case in Chapter 22). Loose, dry, and
unconsolidated soils have higher resistivity than wet and compact soils (see the case in
Chapter 28). Correlation to boreholes can be used to identify and characterise aggregate
deposits (see the cases in Chapters 25 and 26). Bedrock is typically detected by being more
resistive than the overburden soils (or weathered bedrock) (see the case in Chapter 29). Conductive features within bedrock formations are usually associated with clay- or fluid-filled
fractures (see the cases in Chapter 37, 42, and 47). Transitions may aid in detection of potential slip surfaces (see the cases in Chapters 33, 34, and 35). Paleokarsts hosted in limestone
often have higher resistivity than the host rocks but can show lower resistivity if filled with
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Figure 6.1 Resistivity values of various earth materials.
Source: Modifed from Palacky (1987)
a conductive fluid or breccia. Hence borehole data may be needed to determine the relevant
resistivity range of paleokarst (see the case in Chapter 48). DC methods are widely used in
groundwater mapping because aquifers tend to have high resistivities compared to aquitards
and aquicludes. This may be relevant in relation to construction projects when assessing
needs for groundwater or surface water management (see the case in Chapter 50) or risks
related to potential contaminant flow. Fluids with large amounts of total dissolved solids,
such as those arising from containment ponds, have low resistivity, which may identify associated plumes and can help trace leaks or tears in containment liners (see the case in Chapter
49). In these examples and others, contrasts in resistivity are the link between the engineering question and the geophysical data. Delineating where transitions in resistivity (either
increasing or decreasing) are occurring can provide relevant geotechnical information.
In the sections that follow, we provide an overview of the fundamental principles of a
DC resistivity experiment and introduce different acquisition geometries and where they are
applied. We discuss how data are processed and inverted to create models that can be interpreted in the context of the geotechnical question being asked. Finally, we cover concepts of
resolution and uncertainty.
Direct current resistivity methods
6.2
69
FUNDAMENTAL PRINCIPLES
In the simplest survey, DC resistivity data are acquired with a 4-electrode system, as shown
in Figure 6.2. Current electrodes, A and B, are connected to a generator/battery. As the current flows in the ground, it is shunted around high-resistivity units and channelled into lowresistivity units. Electrical charges are built up at any interface where there is a change in
resistivity. These generate an electric potential, and the voltage between electrodes M and
N is measured and converted to an apparent resistivity, 𝝆a. If the earth were homogeneous,
then the apparent resistivity would be equal to the true resistivity. If the earth had conductive
and resistive zones, then the apparent resistivity would be a complicated average of the true
resistivities.
The guiding principles for the DC resistivity experiment are: (1) the electric currents in
the earth must extend deep enough to “see” the target of interest; (2) the signal produced
by the target must be sufficiently large so that it can be distinguished from the background
noise; (3) an adequate number of data need to be acquired to be able to resolve the target.
The power source for modern acquisition systems is a current transmitter, which provides
rectangular pulses with a short duration (e.g., 500 ms). Voltages are measured at a late time
window, and the pulses are stacked to improve the signal-to-noise ratio. In a 2D ERT system,
multiple electrodes are attached to a multichannel cable, and each pair can be used to inject
current or measure a voltage. Voltage measurements can be made simultaneously, and a relay
switch can multiplex current transmission and measurement channels. Modern multichannel
systems simplify data collection. The goal is to acquire enough data with the proper geometry to detect and resolve the structures of geotechnical interest. Modern ERT systems can
also measure chargeability or IP (induced polarisation) data; these can be beneficial for helping identify chargeable materials like sulphides, graphites, or clays at the cost of additional
Figure 6.2 Basic 4-electrode DC resistivity experiment.
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data collection time, e.g., 30–100%. For obtaining IP data, it is required to utilise the voltages measured at multiple time windows after the current has been turned off.
6.3
ELECTRODE ARRAYS AND ACQUISITION
Numerous configurations for current and potential electrodes are still used today. These and
their names are shown in Figure 6.3. Many of these can be acquired with the standard 2D
ERT system, although some, like 3D distributed arrays, require different systems. The choice
of how to collect data depends on the geology and geotechnical questions. If the earth is
horizontal and planar, then sounding data can be diagnostic; if it is 2D, then a 2D ERT survey is needed. If a 3D image is required, then multiple 2D ERT or distributed 3D arrays are
required. Next we explain each survey setup.
6.4
SOUNDINGS
In a sounding mode, a selected array geometry, like the Schlumberger or Wenner array in Figure 6.3, is expanded about a central location. These are often referred to as VES (vertical electric
sounding) surveys. When the array length is small, currents are near the surface, and the apparent resistivity is associated with the near-surface material. The smallest separation determines
the vertical resolution, whereas the largest separation determines the maximum target depth that
can be seen. So minimum and maximum separations and rate of increase for array expansion
are chosen based on the desired vertical resolution and the target depth. As a rule of thumb, the
maximum penetration is ~1/6 of the largest electrode spacing, but this value depends on the
variations of resistivity, and sometimes maximum penetration is better represented as 1/4–1/10
of the array length. The measured apparent resistivity is plotted as a function of the separation
between the current electrodes (e.g., AB/2) and referred to as a sounding curve.
Figure 6.3 Common electrode arrays.
Direct current resistivity methods
71
Soundings are used when the geology is expected to be planar to the ground surface and for
applications such as grounding systems for powerlines, pipelines, or cathodic protection systems.
If data are to be interpreted as a planar earth, then soundings in two perpendicular directions
should be carried out to see if a 1D assumption is valid. Sounding data can also be collected as
part of a 2D ERT survey, although standards (e.g., ASTM) are written for sounding techniques.
6.5
2D ERT
If the earth structure varies laterally, then the concepts of both sounding and profiling are
required. In profiling, a fixed array geometry is moved laterally. Variations in measured
apparent resistivity are sensitive to lateral changes in geology. The depth to which the array
is sensitive depends on the size of the array; small array lengths see shallow, and longer array
lengths see deeper. Smaller spacings between electrodes result in higher lateral resolution.
When the geology or infrastructure has a preferential direction, the appropriate survey line
is perpendicular to the strike.
In a 2D ERT survey, the electrodes (generally stainless steel) are put into the ground at fixed
distances. For geotechnical work, this usually ranges from 1 to 10 m. A 10 m electrode spacing system with 64 takeouts can span a distance of 630 m and thus have good penetration to
100 m or more. Longer continuous sections and enhanced penetration depth can be achieved
by adopting a “roll-along” procedure where a portion of the electrode layout is moved so that
the 2D line is extended. The data are plotted as a 2D pseudo-section of apparent resistivity
values with the positions and depths based on the separations between the electrodes.
For the 2D ERT survey, many different array types can be used to inject and measure electric potentials. These include the Wenner and Schlumberger arrays discussed earlier, as well
as dipole–dipole, pole–dipole, and pole–pole seen in Figure 6.3. Wenner- or Schlumbergertype arrays can be combined in a geometry referred to as Wenner–Schlumberger. Also, the
current and potential electrodes can be interchanged, as illustrated in Figure 6.3 by the “Schlumberger” and “Reciprocal Schlumberger” methods. Multiple gradient arrays, each using a
single current dipole, are commonly used (Dahlin and Zhou 2016). In Figure 6.3, each array
has the capability to delineate subsurface geology and features, and proper selection can
contribute significantly to the effectiveness/validity of the resultant resistivity model for the
planned objectives. The trade-offs include lateral and vertical resolution and effective depth
penetration for producing sufficient electric potential field strength. For example, a dipole–
dipole survey, in which the electric potential measurement electrodes are not between the
injection electrodes, provides very good lateral and vertical resolution, but electric field
strengths drop off quickly as the distance between the two dipole pairs increases. Therefore,
the effective penetration depth is lower because electrical currents are not strong enough to
produce repeatable measurements in the presence of typical electrical noise. A Wenner-type
array in which the distance between each of the four electrodes is the same, results in good
field strengths measured at the electric potential electrodes located between the outer injection electrodes (Dahlin and Zhou 2016).
6.6
DISTRIBUTED ARRAYS
When the geology has no preferential direction or when the locations of confined objects
are unknown, then a 3D survey is required. These scenarios arise when looking for leakage
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channels connected with dams or waste facilities (e.g., power/reservoir dams, tailings, or
lined ponds/landfills), delineating sources of water in in-mine environments, or locating
subsurface water or voids (e.g., karst environments, abandoned mine workings, mine shafts).
Two primary procedures are implemented to obtain a 3D image. The first is a quasi-3D
survey made up of 2D surveys that are parallel to each other. A more advanced procedure
is to lay out a 3D grid of electrodes and use these to generate resistivity data from a suite of
ABMN positions. The layout can be on the surface of the earth or in the tunnels of a mine.
For in-mine environments especially, it is particularly advantageous to place electrodes in
all three dimensions to improve resistivity modelling. Having current or potential electrodes
in a vertical direction may require the use of boreholes. Although the data can be converted
to apparent resistivities for modelling, there is no simple way to plot them. Examples of
3D-applications are shown in the cases in Chapter 42.
6.7
DATA PROCESSING AND INVERSION
Data from each field survey are provided as apparent resistivities and plotted as sounding
curves or pseudo-sections. These are valuable displays for checking data quality and identifying poor data points that should be culled. The process of culling data and assessing uncertainties of the remaining data is a time-consuming but important step. The processed data are
inverted to recover an image of subsurface resistivity that could have produced them. Inversion, which is sometimes referred to as inverse modelling, is a computational process for
which there is no unique answer; many distributions of resistivity can satisfactorily generate
the measured field data. There is thus uncertainty about details from any inversion result,
and this uncertainty increases with depth due to the geometric decay of signals. There is a
depth at which the measured voltages can no longer resolve resistivity structure, and this is
often referred to as a depth of investigation (DOI; Oldenburg and Li 1999). Similarly, there
can be uncertainty in lateral resistivity variation. If there is a gap in a layered structure, for
example, a clay layer that is serving as an aquitard, the risk of missing the gap increases
with the increased depth of the layered structure. Plots of data and the inversion results are
standardly provided by the contractor to visualise the effectiveness of the inversion to match
the measured data.
As an example, we consider a theoretical geological resistivity model illustrated in Figure 6.4a composed of a conductive layer, equivalent to a soft soil, and a resistive feature,
equivalent to a void. A theoretical 1D resistivity plot of the soft layer is shown in Figure
6.4b. The results of a sounding survey are shown in Figure 6.4c, and two inversion models
of the sounding data are illustrated in Figure 6.4d. The non-uniqueness of the inversion
process reveals that expertise is needed in developing the correct geophysical interpretation.
This process is improved with interaction between engineers and geophysicists, particularly
if invasive information (drilling or test pitting) is available. Figure 6.4e shows a pseudosection of profiling data collected across the theoretical geological model. The image is constructed by plotting the apparent resistivity obtained from each measurement at a midpoint
between the two dipoles and at a depth that depends on the distance between the dipoles.
The data image cannot be interpreted geologically. To obtain a meaningful image, the data
are inverted, and the inversion model is shown in Figure 6.4f. The model shows that the soft
soil and the void can be clearly delineated. The upper layer and the soft soil are detected, but
boundaries are smoothed. The lower part of the image has been blanked out in accordance
with the estimated DOI. In reality, no hard cutoff exists since our ability to see at depth
Direct current resistivity methods
73
Figure 6.4 (a) Theoretical example 2D earth structure. (b) 1D earth representation of the layered
media example. (c) Sounding curve. The horizontal axis is array length, not true depth.
(d) Two inversion models that ft the data; (e) Pseudo-section apparent resistivity data
from a dipole–dipole survey. The horizontal axis is x, and the vertical axis is pseudo-depth.
(f) Resistivity recovered by inverting the data. The vertical axis is depth.
diminishes smoothly. The inversion process allows control of a number of parameters that
can help resolve sharp boundaries between materials or consider gradual resistivity variations laterally or with depth.
6.8
RESOLUTION AND UNCERTAINTY
Resolution depends on the survey design and the “noise” in the data. More structure can be
obtained in an inverted model when the data have lower noise, but there is always uncertainty in the details of the final image, and resolution decreases with depth. For layered earth
structures, the conductance or resistance of a layer can be well determined even though the
exact thickness or resistivity cannot be, due to the issues of equivalent conductance and
resistance (Knödel and Lange 2007). The range of resistivity models for any data set can be
explored by changing parameters in the inversion algorithm and assessing the results.
Better images and additional confidence can require interaction with a geophysicist who
is familiar with the geophysical inversion and with a geological engineer or geologist who
has a background knowledge of the rock units and geologic (or human-made) structure. The
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Engineering Geophysics
Table 6.1 Important elements regarding resolution and uncertainty.
Method
Geology, data, and
outcome
Sounding Layered earth
(1D)
Number of data:
~10–100
A few soundings/day
1D resistivity curve
2D
survey
Structures aligned
with the strike
direction
Number of data:
~1,000
A few lines/day
2D resistivity image
3D
survey
Complex 3D
structures (e.g., voids,
contaminants)
Number of data: >
~10,000
< 1 survey/day
3D resistivity image
Factors affecting DOI
Factors affecting
resolution
Notes
Maximum
separation between
electrodes
Amplitude of the
injected current
(higher the better)
Noise level
Depends on
resistivity structure
(e.g., presence
of either highly
conductive or highly
resistive layering can
decrease the DOI).
Electrode spacings
(the smaller, the
better)
Decreases with
depth (related to
geometric decay)
Equivalent
conductance and
resistance
The number of
data (the larger,
the better, but
there is a limit)
Data are plotted
as a sounding
curve.
Can be a part of
either a 2D or 3D
survey.
Can be used for
imaging a deep and
regional structure
Data are plotted in
a pseudo-section
Often, multiple 2D
surveys are carried
out, forming a
quasi-3D survey.
Hard to plot the
data.
Often used with
boreholes or in a
mine setting.
inversions can be rerun with additional knowledge about the resistivity of the rock units
and their structure, potentially from boreholes. Information that the geologic unit is tightly
confined and has a sharp resistivity contrast with an adjacent unit can be incorporated into
the inversion. Table 6.1 lists some important elements regarding resolution and uncertainties
In all cases, it is highly recommended that a survey design for data acquisition be carried
out before going to the field. This requires generating a representative earth model, assigning
resistivities to each unit, hypothesising a data acquisition strategy, simulating the data, adding
realistic noise, and inverting the data with the same inversion code as will be done with the field
data. This quantifies whether the field survey has a good probability of answering the important
questions of relevance to an engineer and sets realistic expectations for what geophysics can
provide. This is especially important when the geology or engineering problem has novel elements (e.g., those found for leaks in liners or surveys conducted in marine environments).
6.9
PRACTICAL CONSIDERATIONS
In general, geophysical surveys are typically used either before geotechnical drill programs
to help locate areas of interest for drilling or as follow-up to boreholes to provide continuation of stratigraphic layers between boreholes. A common and useful approach is to use ERT
Direct current resistivity methods
75
with coincident or follow-up seismic refraction or MASW as complementary methods to
characterise soil and bedrock physical properties.
Identifying and being prepared to address the practical aspects of the data acquisition are
key factors for obtaining high-quality DC resistivity data. There can be various human-made
noise sources that could contaminate the signal, including ground cables, trains, and metallic infrastructure such as fences and wells. The electrodes, often made of stainless steel,
need to be inserted into the ground or rock, typically 10–30 cm (4–12 inches) but as deep
as 1 m or more in resistive conditions. They must make good electrical contact with the soil
or rock so that the contact resistance is low or fairly consistent for the array. This can pose
challenges in urban environments that are paved or in rocky, extremely sandy environments
or environments where the soils are dry and unconsolidated. Overcoming this challenge is
critical for obtaining quality data and can be time-consuming. Sometimes multiple electrodes are used, or electrodes can be wrapped with cloth that has been soaked in saltwater,
or gels or bentonite clays are used to facilitate contact. Modern cables have multiple wires
that are wrapped together; these are expensive and somewhat fragile and can be damaged by
vehicles running over them. Protective shields exist, but employing these increases costs for
the contractor. Anything that obstructs the organised layout of the cables and electrodes can
cause challenges. This includes environments with heavily vegetated terrain or rugged sites
with boulders or mountainous areas with severe topography. Surveys can be conducted in an
environment that includes water. Electrodes can be attached to floats or towed behind a boat;
they can also be deployed on lake or ocean bottoms. Geometries that have some electrodes
in water and others on land are feasible.
REFERENCES
Binley, A., and L. Slater. 2020. Resistivity and Induced Polarization: Theory and Applications to the Near‐
Surface Earth. Cambridge: Cambridge University Press. http://doi.org/10.1017/9781108685955
Dahlin, T., and B. Zhou. 2006. Multiple-gradient array measurements for multichannel 2D resistivity
imaging. Near Surface Geophysics 4(2):113–123.
Knödel, K., and G. Lange. 2007. Chapter 4.3 direct current resistivity methods. In Environmental
Geology: Handbook of Field Methods and Case Studies, ed. K. Knödel, G. Lange, and H. J. Voigt.
Berlin and Heidelberg: Springer.
Loke, M. H., J. E. Chambers, D. F. Rucker, and O. Kuras. 2013. Recent developments in the direct‐
current geoelectrical imaging method. Journal of Applied Geophysics 95:135–156.
Oldenburg, D., F. Jones, L. Heagy, et al. 2017. Geophysics for Practicing Geoscientists (GPG). GeoSci
Developers. http://gpg.geosci.xyz (accessed March 29, 2022).
Palacky, G. J. 1987. Resistivity characteristics of geologic targets. In Investigations in Geophysics No.
3, Electromagnetic Methods in Applied Geophysics: Volume 1, Theory, ed. M. N. Nabighian. Tulsa:
Society of Exploration Geophysicists. https://doi.org/10.1190/1.9781560802631.ch3
Ward, S. H. 1990. Resistivity and induced polarization methods. In Investigations in Geophysics
No. 5, Geotechnical and Environmental Geophysics. Tulsa: Society of Exploration Geophysics,
pp. 147–189.
Chapter 7
Electromagnetic methods
Niels Bøie Christensen
7.1
INTRODUCTION
This chapter outlines the basic theory, functionality, and application of electromagnetic
methods (EM). In geophysics, the term “electromagnetic methods” covers all the methodologies by which electromagnetic fields are measured to gain insight into the subsurface distribution of the electric resistivity or equivalently the conductivity (the inverse of resistivity),
the magnetic susceptibility, and the electric permittivity. In this chapter, only conductivity
plays a role in the methods presented here.
The usefulness of electromagnetic methods is based on the fact that various geological,
physical, and chemical distinctions can be made based on conductivity. They are typically
used in the pre-construction investigation phase to assist in building 2D or 3D models of the
subsurface geology (Bedrock: see the cases in Chapters 16, 24, 41, and 44, Sediment: see the
cases in Chapters 32, 34, and 36). Further, in order to map buried structure or archaeology
(see the cases in Chapters 15, 12, and 18), contaminated sites (see the case in Chapter 13),
possible leakage of contamination, mapping water resources and infiltration of rainwater
(see the case in Chapter 32).
7.2
FUNDAMENTAL PRINCIPLES
All EM methods are exhaustively described by Maxwell’s equations. The term “electromagnetic” is most often used to signify an inductive method where a varying magnetic field from a
source induces currents in the ground, and the electric and magnetic fields from these currents
are measured at various distances from the source and for various frequencies or time delays.
Inductive methods do not require physical contact between the ground and the instrument,
which is often mounted on a moving platform while measuring, either pulled along profiles on
the ground or mounted on an airborne platform. Compared with DC methods, this means that
inductive methods can have much higher productivity and thereby cover large survey areas
within a reasonable time; see the overview of method capabilities later in the chapter. Inductive
methods are also used in a waterborne setup where the instrument is towed on floats behind a
small boat or even deployed in the water close to the bottom of bodies of water.
7.3
BASIC PHYSICS
The methods presented in this chapter are based on instruments with a transmitter/source and
a receiver. The source is typically a small coil with a number of turns of wire or a larger loop.
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I0 cos (ˆt)
Tx
Hp cos (ˆt)
Rx
Hs cos (ˆt-)
Primary voltage
Primary voltage
Secondary current
Primary current
t
˜
t
Figure 7.1 Frequency domain EM methods. Left: Principle of induction and the primary and secondary
felds.A primary current, Iₒ cos (ωt), is passed through the windings of the transmitter (Tx)
coil, causing a primary magnetic feld, Hp cos (ωt), to arise.This feld has the same phase as the
primary current and induces currents in the ground, which give rise to a secondary magnetic
feld Hs cos (ωt-ψ), which is shifted in phase relative to the primary magnetic feld. Middle:
Sinusoidal functions describing the primary voltage and current. Right: How the primary
voltage and the induced secondary current are shifted in phase relative to the primary feld.
The transmitter passes a current in the wire with a specific waveform, as depicted in Figure 7.1.
When a varying current is passed through the wires of a transmitter, it is accompanied by
a magnetic field, the primary field that varies simultaneously with the same waveform as
the current. The varying magnetic field induces currents in the ground, which are stronger
for more conductive ground and which are stronger nearer the transmitter, decreasing with
distance. These varying currents in the ground, in turn, are accompanied by a magnetic field,
the secondary field. The receiver is most often a small coil, and it receives the magnetic
fields from the current induced in the ground. The secondary field – as well as the primary
field – induce an electromotive force, i.e., a voltage, in the receiver coils placed on or above
the ground. The voltage measured in the receiver coils are the actual data; it is the secondary
magnetic field that carries information about the subsurface conductivity. If the waveform
is sinusoidal with a particular frequency, the instrument is said to measure in the frequency
domain. If it consists of pulses, it is said to be a time domain method.
7.3.1
Frequency domain methods
In frequency domain methods, the waveform of the primary field is a sinusoidal current at
one or more frequencies that is on all the time. The secondary field consequently also varies sinusoidally with the same frequency, with an amplitude and a phase, or an inphase and
a quadrature part, or, equivalently, a Real and an Imaginary part (terms to be used here) that
depend on the subsurface conductivity and the transmitter-receiver configuration. The current
pattern in the ground is stationary, except, of course, for the sinusoidal variation. Since the
transmitter is on all the time, what is measured in the receivers is the sum of the primary field
from the transmitter and the secondary field arising from the currents induced in the ground.
For a low transmitter frequency, the current pattern in the ground – and thereby the sensitivity
to the conductivity – is determined by the transmitter-receiver geometrical configuration: the
longer the separation, the deeper the penetration and vice versa. For higher frequencies, the
current pattern “shrinks” toward the surface, and the penetration depth therefore decreases.
7.3.2 Time domain methods
For time domain methods (TEM), a primary magnetic field is established by passing a current through a transmitter loop/coil and then turning it off abruptly to induce currents in
Electromagnetic methods
79
the ground. Most often, the receivers measure the fields present after the current has been
turned off so that the measurements are influenced only by secondary fields. Shortly after
the turnoff, of the order of ~0.01 ms, currents in the ground are close to the surface and thus
carry information about the near-surface parts of the ground. As time passes, the currents
diffuse downwards and outwards and become weaker and weaker, thus being influenced by
the conductivity at larger depths. See Figure 7.2.
7.3.3 Assets and drawbacks
In frequency domain methods, measurements are conducted in such a way that they are sensitive only to the frequency of the transmitter, meaning that almost all ambient noise signals
are suppressed very effectively. This is definitely an asset of frequency domain methods.
However, the primary source field is on all the time, so receivers measure the sum of the
primary and the secondary fields. This is a problem because the primary field can be orders
of magnitude larger than the secondary field at the receiver position, so the influence of the
primary field must therefore be subtracted in the instrument. However, because the primary
field is much larger than the secondary field, this operation introduces quite a bit of uncertainty regarding the final value of the Real part, and minute changes in the instrument geometry caused, e.g., by temperature changes or mechanical instability will have a detrimental
effect on the quality of particularly the Real part of the data. The Imaginary part of the data
is less affected. This is why the Real part is often discarded in the subsequent data processing, and only the Imaginary part is used. This problem could be alleviated by calibration,
but presently no procedures fulfil the criteria of being simple and swift to apply and provide
enough information to determine the calibration constants.
For time domain methods, data are recorded in time windows, gates, in the transmitter offtime, i.e., when the transmitted signal has disappeared. The voltage induced in the receiver
coil has a very large dynamic range covering several orders of magnitude from large values
at the earliest gates with a very rapid attenuation to very small values and slower attenuation at the latest gates. The receiver electronics must therefore cover a wide dynamic range.
The noise suppression is achieved by stacking, i.e., averaging measurements over several
transmitter cycles. On the other hand, the measured signal is not compromised by a large primary field component. Only the secondary field carrying the information about the ground
is measured, provided that the transmitter waveform is well known and well modelled in the
inversion software.
7.4
INTERPRETATION AND MODELLING, RESOLUTION
AND UNCERTAINTY
In this section, the most important issues regarding the capabilities and limitations of EM
methods in a geotechnical context are presented. For more details, please refer to chapter
1.4 on inversion.
7.4.1
Inversion and interpretation
Two concepts often used in the geophysics community – inversion and interpretation – warrant a few words. Inversion is the process of finding the conductivity model, i.e., the distribution of conductivity in the ground that best explains the measured data. Interpretation is
100
105
150
t=20 s
200
–300 –200 –100
0
0
100
200
300
50
100
150
t=80 s
200
–300 –200 –100
0
0
100
200
300
50
100
150
t=320 s
200
104
103
102
101
100
–300 –200 –100
0
100
200
300
X-coordinate [m]
0
Voltage [nT/s]
Depth [m]
Depth [m]
Depth [m]
50
dB/dt plot for 2-layer model
Engineering Geophysics
106
80
Current density
0
0.5
10–1
10–3
1
10–2
10–1
100
101
Delay time [ms]
Figure 7.2 Time domain EM methods. (Author: Niels Bøie Christensen.) Left:Absolute value of the current density in a homogeneous half-space with a conductivity of 50 mS/m from a ground-based transient system in the central loop confguration for the times, from top to bottom: 20, 80, and 320 μs after
turnoff.The current pattern is symmetric around the centre of the transmitter situated in (0,0). For every delay time, the current density is normalised
with its maximum value. In reality, it becomes orders of magnitude weaker over the time span shown here. Right:Transient decay curve showing the
measured voltage in the Rx coil as a function of time after the turnoff for a 2-layer model with resistivities 64 and 2 Ωm in the top and bottom layers,
respectively, and a thickness of the top layer of 64 m.
Electromagnetic methods
81
the subsequent process of finding the most plausible geological/geotechnical/hydrological
model based on the inversion results and other available information, such as borehole logs,
surface observations, and background geological knowledge.
7.4.2
Data noise/uncertainty
Measured data are affected by noise. The voltages measured in the receiver coils are influenced not just by the primary field but also by, e.g., ambient electromagnetic fields, noise in
the electronic circuits of the measuring equipment, noise from movements of the receiver
coil in the Earth’s magnetic field, instabilities in the transmitter-receiver configuration. To
improve the signal-to-noise ratio of the data, the measured raw data are subjected to various
forms of data processing tailored to the specific data type and the noise characteristics of
the data. Choosing an optimal processing and quantitatively estimating the noise level are
crucial for an optimal outcome of the inversion. This leads to the first credo:
Without knowing the uncertainty of the data, it is not possible to perform a meaningful
inversion.
It must be kept in mind that a data value is always two numbers: one that gives the data
value and one that specifies its uncertainty.
7.4.3
Coupling
All EM methods are based on a varying primary magnetic field that induces currents in
the ground. However, induction also happens in human-made good conductors close to the
instruments such as buried cables, metal fences, highway crash barriers, power lines, large
conductive structures such as metal-clad buildings. These currents, of course, also induce
voltages in the receivers, and data are compromised. This phenomenon is called cultural
coupling, and data sets distorted by cultural coupling must be removed in the data processing
following the acquisition. Otherwise, they introduce model artefacts that are misleading in
the final interpretation. Since all electromagnetic methods are affected by cultural coupling
phenomena, their application is often precluded in an urban environment.
7.4.4
Uncertainty of the inverted models
When considering the resolution capabilities of electromagnetic methods, one must be aware
of the basic condition for any experimental science. Measured data represent an amount of
information – that is why we conduct geophysical surveys – but besides this obvious fact,
it must also be realised that data are inaccurate (due to noise), insufficient (the number of
data is limited), and inconsistent (the model used in the inversion is a simplified approximation to a complex reality). Therefore, the models found in the inversion of the data are
also affected by uncertainty, meaning that many models will fit the data equally well. These
models are called equivalent models. In some instances, the equivalent models span a considerable interval of specific model parameters. For inductive EM methods, one of these
cases is the low resistivity equivalence where the thickness and the conductivity of thin
conductive layers are undetermined: the layer might as well be twice as thin with twice the
conductivity, but the product of thickness and conductivity, the conductance, is quite often
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Engineering Geophysics
Table 7.1 Comparison of electromagnetic methods described in sections 7.4.2, 7.4.3, and 7.4.4.
GCM
Ground-based TEM
Airborne TEM
Main purpose
Conductivity
distribution between
~0.5 and 10 m depth
Mapping subsurface
conductivity in the
depth range down to
~100 m
Mapping subsurface
conductivity to depths
of hundreds of metres
Productivity
50–70 line km/day
40–60 line km/day
200–400 km/day
Accessibility
issues
Ground must be able
to carry a quadbike
weighing 200–300 kg.
Very steep topography
and dense vegetation
preclude its use.
Ground must be able
to carry a quadbike
weighing 200–300 kg.
Very steep topography
and dense vegetation
preclude its use.
Determined by local
air traffc regulations.
Flight heights:
20–50 m
Ground conditions of
no importance.
Steep cliffs and
mountain ranges can be
an impediment.
Resolution
range
Horizontally: Of the
order of ~1 to 2 m
Vertically: Depths
from 0.20 to 5–10
m, depending on
instrument type and
ground conductivity
Horizontally ~10
m Vertically ~1 to
50–100m depending on
conductivity regime
Horizontally:
~10 to 50 m Vertically:
From a few metres to
200–350 m
Cultural
coupling
Human-made good
conductors within
a distance of ~10
m, safety distance
increasing for lower
conductivities
Human-made good
conductors within a
distance of 50–150
m, safety distance
increasing for lower
conductivities
Human-made good
conductors within
distances up to ~100 to
250 m
Confguration
N/A
N/A
Transmitter-receiver
distances of 10–20 m
well determined. Another case arises from the fact that the resistivity of high resistivity layers is poorly determined.
EM methods in general applied on or above the surface all have the characteristic that
resolution decreases with depth. As a rule of thumb: if a layer is reasonably well resolved
at a certain depth, then at twice that depth, it would have to be twice as thick to be resolved
equally well. Moreover, at a certain depth, depending on the conductivity model and the
method used, resolution is lost. This depth is often referred to as the depth of investigation
(DOI).
In Table 7.1, the resolution properties of the three methods given special attention in this
chapter are listed as vertical resolution and horizontal resolution. The horizontal resolution
value indicates the lateral distance between measuring locations where lateral gradients in
the conductivity structure can be resolved by the data. The vertical resolution is indicated
as a depth interval within which, depending on the model, a reasonable resolution can be
expected. Most often, an analysis of the uncertainty of the model parameters is performed as
Electromagnetic methods
83
part of the inversion process, indicating the parts of the model that are well resolved and the
ones that are more dubious. And this leads to the second credo:
Without an indication of the uncertainty of the model parameters, it is not possible to
conduct a meaningful interpretation of the inversion result.
7.4.5
Resolution of different lithologies
EM methods are of interest in geotechnical mapping because different geological formations have
different electrical conductivities. Please refer to the list of conductivities in Figure 6.1. However, there can be fairly large overlaps of the conductivity intervals for different lithologies,
so the geological interpretation of conductivity is not unique, meaning that any additional
information, e.g., from boreholes or general knowledge of the most probable geological
formations in an area, is valuable, even necessary for a reliable geological interpretation.
The fact that clays have a higher conductivity than gravel and sand means that, in general,
EM methods are better at resolving the presence of clay layers than sandy/gravelly layers.
7.5
ACQUISITION REQUIREMENTS
7.5.1
Instruments used in geotechnical surveys
There is a wide range of situations where the use of EM methods can be relevant in a geotechnical investigation. EM methods have been used in surveying, e.g., along a planned
road (see the case in Chapter 24) and railroad lines; foundation problems, in particular, the
presence of soft sediments; mapping in relation to natural hazards, e.g., locating quick clay
that might cause landslides (see the cases in Chapters 16 and 36); mapping seepage from
tailings at mine sites; mapping old dump sites in connection with remediation (see the case
in Chapter 13). Next, three types of instruments are presented that are used for shallow or
deep investigations.
7.5.2
Ground conductivity meter
The ground conductivity meter (GCM) is a ground-based frequency domain instrument,
typically with one transmitter coil transmitting one frequency of ~10 kHz and one or several
receiver coils in various transmitter-receiver configurations, all installed in a tube made out
of electrically nonconducting material.
The tube can be moved along the survey lines in several different ways: carried by field
personnel (slow and inefficient), mounted on a rack attached directly to a quad bike (coupling between instrument and vehicle is detrimental to data quality), or – best of all – pulled
on a sledge behind a quad bike (minimising coupling, instrument height above ground, and
pitch and roll). In the latter case, the instrument is moved with a speed of ~5 m/s. Data consisting of the Real and Imaginary components are measured while moving. Data are stored
in a field computer together with GPS information. Older instruments would have only one
receiver coil and would only give profiling information. Modern instruments have several
receiver coils in different orientations at a series of distances from the transmitter, e.g., at
distances of 1, 2, and 4 m in both horizontal coplanar and perpendicular orientations that
possess information about the subsurface at different depths. The information in the data
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Engineering Geophysics
thus justifies an inversion that produces a subsurface model. The properties of the GCM are
summarised in Table 7.1.
7.5.3
Ground-based transient instrument
Previously, ground-based instruments for TEM measurements were carried from one location to another, recording data at one location at a time, but recently instruments have been
developed that measure TEM data plus GPS information continuously while being pulled
along survey lines by a quad bike with a speed of 4–5 m/s. This dramatically improves the
productivity of the method. A continuous instrument would have a transmitter loop with an
area of ~8–10 m2 and a receiver coil located ~10 m behind the transmitter. After data processing, data are inverted to provide a subsurface conductivity model. It is quite straightforward
to turn a ground-based continuous instrument into a waterborne instrument by mounting
transmitter and receiver on pontoons instead of sledges, and this option has been realised.
The properties of a continuous TEM method are summarised in Table 7.1:
7.5.4 Airborne EM methods
Airborne instruments of the frequency domain type were developed before similar ones
for time domain. The frequency domain instruments had several transmitter coils transmitting frequencies in the range of 0.3–100 kHz, each with a dedicated receiver coil,
all mounted in a tube hanging below a helicopter. Data consist of the Real and Imaginary parts of each of the frequencies. Being a frequency domain instrument, it suffers
the usual issues with calibration and drift, which are addressed and partly ameliorated
through high altitude measurements while flying and partly in the post-processing of the
data. Depth information comes from the fact that higher frequencies have lower penetration and vice versa.
Though frequency domain instruments are still used, more and more airborne TEM instruments are taking their place. They do not have the same calibration and drift issues, they
penetrate deeper into the ground, and modern instrumentation has the same or better nearsurface resolution capability. The airborne TEM instruments most relevant for geotechnical
purposes consists of a large loop with an area of ~100–300 m2 and one or several receiver
coils mounted in a rigid configuration 10–15 m from the transmitter and carried below a helicopter. Often they are able to measure with a low transmitter moment that can be turned off
quickly and thereby provides information about the near-surface and with a larger moment
that provides information about the deeper parts of the earth. After recording, the data are
inverted to provide a subsurface conductivity model. The properties of an airborne TEM
system are summarised in Table 7.1.
FURTHER READINGS
Papers
Fell, R. 1990. Engineering geophysics – a civil engineer’s viewpoint. Exploration Geophysics 21(1–
2):25–31. http://doi.org/10.1071/EG990025
Report by the Geological Society Engineering Group Working Party. 1988. Engineering geophysics.
Quarterly Journal of Engineering Geology and Hydrogeology 21(3):207–271.
Electromagnetic methods
85
Stapledon, D. 1990. Engineering geophysics – a geophysicist’s view. Exploration Geophysics 21(1–2):
17–24. http://doi.org/10.1071/EG990017
Whiteley, R. J. 1990. Engineering geophysics – a geophysicist’s view. Exploration Geophysics 21(1–2):
7–16. http://doi.org/10.1071/EG990007
Books
Aizawa, T. 2014. Application Manual of Geophysical Methods to Engineering and Environmental Problems. Houten: European Association of Geoscientists and Engineers, 664 pp. ISBN 9789462821088
Butler, D. K. 2005. Near-Surface Geophysics. Investigations in Geophysics. Vol. 13. Houston, TX:
Society of Exploration Geophysicists.
Kirsch, R. 2009. Groundwater Geophysics. A Tool for Hydrogeology. Berlin, Germany: Springer.
Nabighian, M. N. 1988. Investigations in Geophysics No 3, Electromagnetic Methods in Applied
Geophysics: Volume 1, Theory. Houston, TX: Society of Exploration Geophysicists. https://doi.
org/10.1190/1.9781560802631
[This reference requires a solid mathematic/physical background.]
Nabighian, M. N. 1988. Investigations in Geophysics No 3, Electromagnetic Methods in Applied Geophysics: Volume 2, Application, Parts A and B. Houston, TX: Society of Exploration Geophysicists.
https://doi.org/10.1190/1.9781560802686
[This reference list several applications of EM methods and should be more accessible.]
Styles, P. 2012. Environmental Geophysics: Everything You Ever Wanted (Needed!) to Know But
Were Afraid to Ask! (Education Tour Series). Houten: European Association of Geoscientists and
Engineers.
Chapter 8
Ground penetrating radar
Matteo Rossi
8.1
FUNDAMENTAL PRINCIPLE
Ground penetrating radar (GPR), also known as Georadar, is a well established geophysical
technique, which employs electromagnetic (EM) radio waves to probe the ground, including
soil, rock, ice, and snow, as well as natural media such as water and wood or artificial material such as concrete and asphalt. The spatial scale of investigation has a broad range, from
centimetres (i.e., in concrete) up to kilometres (i.e., geological characterisations).
The radar systems deploy a transmitter antenna that sends an EM impulse and a receiver
antenna that records the EM signal that passes through the investigated medium. GPR operational modes can be summarised in three main categories: reflection, velocity sounding, and
transillumination.
The reflection method implies a transmitter and a receiver antenna, most often at a fixed
distance (common-offset), which are moved along a surface (Figure 8.1a). This method is
performed for mapping heterogeneities due to layer/material interfaces. Velocity soundings
need to vary the distance between the transmitter and the receiver antennas on a surface. The
most common methods use the common midpoint or the wide-angle reflection and refraction
configuration.
Transillumination requires locating the antennas around a specific volume, as in some
borehole applications or in non-destructive testing of walls or pillars (where the transmitter
and the receiver antennas are placed on the opposite sides of the structure).
The last two methods are performed for estimating a distribution of EM velocities in media,
which is directly correlated to the intrinsic physical properties of the materials. GPR commonoffset reflection is the most popular application and usually employs an instrument that contains
both the transmitter and the receiver antennas. The instrument is moved along the ground surface
(Figure 8.1a), while the transmitter periodically emits an electromagnetic pulse and the receiver
antenna records the response (wavelets) from the EM waves that propagate into the ground. The
result is a radargram (Figure 8.1b): a spatial collection of 1D vertical soundings (traces).
8.2
BASIC PHYSICS OF THE ELECTROMAGNETIC
WAVE PROPAGATION
The physical principles of GPR derive directly from the EM theory, which is described
by Maxwell’s equations and electromagnetic constitutive relations (Annan 2009; Cassidy 2009a; Reynolds 2011). Maxwell’s equations define the physics of EM fields. The
DOI: 10.1201/9781003184676-8
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Engineering Geophysics
Figure 8.1 Example of GRP acquisition for the refection (or common-offset) method. (a) Sketch of a
surface GPR acquisition with a schematic subsoil characterised by linear heterogeneities. (b)
Resulting pre-processed radargram of a 170 MHz antenna with no topographic correction.
(c) Same radargram with topographic correction and depth transformation applied.
Ground penetrating radar
89
constitutive relations describe the behaviour of a material under the influence of EM fields,
hence the relations among physical parameters that are specific for a material and its impact
on the EM fields.
Combining Maxwell’s equations with the constitutive relations, it is possible to write two
dissipative wave equations, which describe how the electric and the magnetic fields propagate through a medium. The propagation of EM waves depends on intrinsic properties of the
material (µ, magnetic permeability; ε, the dielectric permittivity; σ, the electrical conductivity) and the frequency of the signal (ω).
Those EM equations are described by two terms that involve the intrinsic properties: the
dissipation term, uwo , and the propagation term, uew 2 . The ratio of these terms is the loss
factor (P):
P=
uwo
o
=
2
ew
uew
(8.1)
P is a key parameter for understanding the physical behaviour of geophysical EM methods.
From Eq. 8.1, it is evident that, for low frequencies and high conductive materials, P has
large values (P >> 1). This means that the diffusive field is dominant, and we are in the range
of EM induction methods. On the other hand, if P tends towards 0 (P << 1) – hence high frequency and low conductivity – the EM field is described by wave propagation. The latter is
the condition for GPR methods. The operative frequencies of GPR range from a few MHz up
to 1–3 GHz; for lower frequencies, diffusion equations are valid, while at higher frequencies,
the phenomenon of relaxation of water molecules occurs. Another important aspect of this
frequency range is the non-dispersivity of EM waves: the velocity (V ) of the propagation of
radio waves does not depend on the frequency of the signal for low-conductivity materials.
Thus velocity is a material property, and in a low-loss non-magnetic medium, it is governed
by and inversely dependent on the dielectric relative permittivity (εr). εr is a dimensionless
intrinsic property of the material and varies from 1 in air to 81 in water (see Table 8.1 for two
examples of common velocities in geological materials).
Table 8.1 Examples of theoretical resolution (vertical and horizontal) according to different dominant
frequencies of the antennas, depth of the target, properties of the medium, and EM wave
velocities.
Antenna
frequency
[MHz]
Velocity [m/ns]
Velocity [m/ns]
0.05
0.1
Wavelength [m]
50
200
600
1000
1.00
0.25
0.08
0.05
Vertical
resolution
[m]
0.25
0.06
0.02
0.01
Horizontal
resolution [m]
At 0.5 m
depth
At 5 m
depth
0.56
0.26
0.15
0.11
1.60
0.79
0.46
0.35
Wavelength
[m]
Vertical
resolution
[m]
2.00
0.50
0.17
0.10
0.50
0.13
0.04
0.03
Horizontal
resolution [m]
At 0.5 m
depth
At 5 m
depth
0.87
0.38
0.21
0.16
2.3
1.1
0.6
0.5
90
8.3
Engineering Geophysics
PROPERTIES RELATED TO DIFFERENT LITHOLOGIES
OR TARGETS FOR THIS METHOD
Geological and artificial materials are a complex mixture of components. The resulting
dielectric properties of the lithologies mainly depend on the grain size distribution, the
porosity and the filling of the pore space. Pores can be filled with air, water and/or contaminants like oil and DNAPLs (dense non-aqueous phase liquids). The pore fluids and
their degrees of saturation affect the bulk properties of the material, even if the solids are
invariant.
EM waves propagate from the antenna into the ground, and when they encounter a significant discontinuity in the properties of the material (ε r, V), part of the signal is reflected
backwards and is measured by the receiver. The amplitude of the recorded signal is proportional to the contrast of the properties; the higher the difference in relative permittivities, the
larger the amplitude of the reflected signal.
Significant reflections can occur; for example, between unconsolidated sediments and
crystalline bedrock (see the cases in Chapters 21, 22, and 29); in the transition from lowsaturated to fully saturated porous media, especially in sand-gravel deposits; between ice/
snow and ground; at the interface between frozen/unfrozen ground (see the case in Chapter 23); at the boundary of a freshwater body and the underlying sediments (see the case
in Chapter 46); in the presence of cavities and fractures or utilities and other underground
constructions (see the cases in Chapters 14 and 20). Reflections can also occur in lithological boundaries where the grain size distribution or the porosity varies; an example is
the heterogeneous distributions of fluvial or eolian deposits, as well as the transition from
artificial filling and underlying geological media (see the case in Chapter 27).
The reflection with the highest amplitude in Figures 8.1b and 8.1c is caused by the groundwater table in a sandy quaternary deposit, while all the other features are due to lithological
variations in the glaciofluvial sediments.
8.4
LIMITATIONS
Electrical conductivity, σ, is an EM property that plays an essential role in GPR sounding.
In high-conductive materials, the diffusive field is dominant (P >> 1, Eq. 8.1), and the signal
that propagates as EM waves is strongly attenuated. For this reason, GPR does not provide
suitable results in clay-rich lithologies or in the presence of water with high concentrations
of dissolved ions, such as, for example, saline water, except for finding their upper limit.
GPR is instead a very efficient technique for resistive environments such as bedrock, coarse
grain sediments, ice, frozen ground, and peatlands.
Attenuation is an exponential decay of the amplitudes of the EM waves propagating
through space and, in addition to σ, directly depends on the frequency. GPR systems that
employ high-frequency antennas show higher attenuations for the same travel distance than
do low-frequency antennas. This results in a shallower depth of penetration. An example of
this phenomenon is shown in Figure 8.1b, where the most evident and energetic reflection
seems to “disappear” in the first 22 m of the profile and is highly attenuated in the last 3 m
(from about 58 m to the end). The profile is a cross section of an isthmus in the Baltic Sea.
The higher salinity of the seawater intrusion probably increases the bulk conductivity of the
quaternary sediments and hence the attenuation.
Ground penetrating radar
91
Besides attenuation, the energy loss is caused by several factors:
•
•
•
•
When the radio waves hit an interface, part of the signal is reflected, while only a portion
of the energy is transmitted through the discontinuity.
EM waves propagate from the instrument with a cone angle, so the energy spreads over
larger areas with increasing distance from the antenna; the energy reduction per unit
area is inversely proportional to the square of the travelled distance.
A fraction of the energy is lost through the absorption of radio waves and the transformation into heat, as happens in a microwave oven.
The last cause of energy loss is scattering: objects with a dimension similar to the wavelength of EM waves create reflections in all directions, spreading the energy away from
the receiver. Scattering is also a cause of coherent noise (clutter) in the data.
A GPR radargram is a discrete compilation of traces acquired at discrete distance intervals on the ground surface (Figure 8.1b). A trace displays the fluctuations (wavelets) of the
amplitude of the reflected signal as a function of time (usually expressed in nanoseconds
[ns]) because the receiver measures the reflected radio waves as a time-varying electric field
induced in the antenna. So the data collected are reflected signals as a function of time and
space (radargram, as in Figure 8.1b). The user typically needs the depth to or the elevation
of a feature rather than the travel time; consequently, an estimation of the EM velocities
is necessary to transform the y-axis of a radargram from time to space (Figure 8.1c). The
common-offset reflection method does not provide an accurate estimate of the velocities,
since the travel distance from the antenna to the reflector is usually unknown, contrary to
other acquisition methods (velocity soundings and transillumination). There are two options
for estimating the velocities. The first possibility is to guess an average velocity according
to values reported in the literature for the expected lithology. The second option is the estimation of the velocities by fitting diffraction hyperbolas that can occur when point objects
are present along the profile. The shape of the hyperbolas is a function of time and average
velocity of the material above the object.
If complementary information is available at the site, the depth of a reflector can be calibrated; hence the velocity of EM waves above the target can be estimated. An example is
the use of geotechnical data, such as core-drilling descriptions or cone penetrating testing
(CPT), which can locally provide an accurate estimation of the depth of an interface. Other
examples of velocity calibration with external information are the integration of GPR with
complementary geophysical methods and the visual inspection of an excavated trench.
8.5
RESOLUTION AND UNCERTAINTY
The resolution of GPR is mainly determined by the antenna central frequency and bandwidth. The horizontal and vertical resolution of the technique is directly dependent on the
frequency of the signal. In non-dispersive media, the velocity is independent of the frequency; consequently, higher frequencies depict shorter wavelengths.
The vertical resolution is theoretically a fourth of the wavelength of the dominant frequency. The horizontal resolution is described by the Fresnel radius (Reynolds 2011), which
increases with the distance from the source (transmitter) and the wavelength. It means that,
in the deeper portion of a radargram, narrow interfaces cannot be detected as well as they are
in the shallower ground.
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Engineering Geophysics
Energy attenuation is a function of the frequency. Thus higher frequencies can resolve less
extended and thinner interfaces, but the signal attenuates faster, resulting in a shallower penetration depth. Table 8.1 summarises the expected resolution for several central frequencies
of the antenna and two different medium properties (so velocities of EM waves).
8.6
ACQUISITION REQUIREMENTS
Instruments have several acquisition parameters. The most important, as reported in the
previous sections, is the operative frequencies of the antennas. Traditional antennas have a
dominant (or central) frequency centred on a relatively narrow range. The physical dimension of the antenna is inversely correlated with the central frequency: a 100 MHz antenna
has a length of almost 1 m, while a 1 GHz system can be operated with one hand, since the
antenna length is about 10 cm.
The decision on the type of antenna hence depends on the nature of the material to be
investigated and the target of the survey, considering both desired resolution and aimed
depth of investigation. In recent years, dual-antennas and step-frequency antennas have
become available on the market. Dual-antennas send at the same time with two antennas,
characterised by different dominant frequencies. Step-frequency instruments are capable of
simultaneously measuring on a broad range of frequencies.
There are a few essential settings of a GPR system.
•
•
•
8.7
The first parameters are the sampling frequencies, both in time and space, which should
be carefully settled to avoid aliasing.
The length of the recorded time window is conditioned by the depth of the target and the
effective depth of penetration.
The stacking number is the number of soundings that are averaged to obtain a trace, with
the aim of suppressing incoherent noise and increasing the signal-to-noise ratio.
PROCESSING
GPR data need a few processing steps to produce a readable graphic representation. Two
processing algorithms always need to be applied: time-zero correction and gain.
The position of the first wavelet that rises from the reflection at the air/ground interface
can vary due to variations in the distance between the instrument and the ground. The position of the first reflection is also caused by instabilities in the internal electronics of the
instrument (i.e., thermal drifts) that delay the sending of the current pulse to the transmitter
antenna. Therefore, the time-zero correction is a temporal shift of the recorded traces conforming to the first air/ground reflection.
The gain filter is required to enhance the amplitude of the later/deeper wavelets to compensate for the loss of energy that is mainly due to attenuation, reflections, and geometrical
spreading. A GPR radargram without a gain filter shows the strongest wavelets concentrated in
the first and most energetic air/ground reflection, while the later arrivals usually have weaker
amplitudes, lower in orders of magnitude, and they are essentially invisible in a graphic illustration. Several gain functions are available and the choice depends on the target of the survey.
The topographic correction is another essential processing step if the differences in elevation along the profile are significant. Correcting the elevation of the profile allows a less
Ground penetrating radar
93
unambiguous interpretation of the data. Figure 8.1b shows the radargram obtained from the
model displayed in Figure 8.1a without a topographic correction, where the flat groundwater
table is visualised in the radargram as a concave reflection. It is evident that the interpretation is facilitated in Figure 8.1c, which displays the same radargram after the topographic
correction.
The GPR profile is recorded as a series of time traces separated by a discrete space interval
(Figure 8.1b); an accurate estimation of the velocities is essential to transform time to depth
or to elevation (Figure 8.1c). A homogeneous velocity distribution (e.g., an average velocity for the entire radargram) is usually assumed, as the reflection method does not provide
exhaustive information about the EM wave velocity distribution.
Other useful processing steps are temporal filters (low-, high-, and band-pass filters),
which are applied to remove the energy content at those frequencies that are lower and/or
higher than the operative frequencies of the system and that are considered noise.
Background removal is probably the most common spatial filter, and it is used to remove
horizontal features in the radargram, like the first air/ground reflection, which is not informative on the internal structures of the ground.
Further, less common processing steps are deconvolution and migration, which derive
from seismic methods and frequently result in moderate improvements of the radargram.
These filters can be applied in specific conditions, as they need assumptions on the distribution of material properties and wavelets propagation. Migration might be essential for collapsing diffraction hyperbolas and properly imaging dipping reflectors.
Figure 8.1b presents a radargram processed through the following steps: band-pass filter,
time-zero correction, gain, and background removal. Figure 8.1c is the same radargram with
further processing steps applied: topographic corrections and transformation to elevation
above sea level.
8.8
INTERPRETATION
GPR provides a picture of the investigated medium like ground, ice, concrete, asphalt, and
wood. The wavelets of stronger and weaker reflections displayed in the radargram can be
directly interpreted as structures, although some events demand caution in inferring the
material geometry.
•
•
•
•
If GPR antennas are not shielded, reflections from objects above the ground (nearby
trees, pillars, buildings) might occur and could be misinterpreted as subsurface features.
Point objects in the ground, such as small boulders or sections of pipes and rebars, create hyperbolic reflections. The apex of the inverted U-shaped hyperbola determines the
position of the object.
On some occasions, the GPR profile shows multiple reflections (or ringing) when the
signal repeatedly bounces between two reflectors. It can happen when the energy is
“trapped” between two strong reflectors (i.e., pipes, voids, metallic objects) and the air/
ground interface. In this case, the reflection is repeated with a constant time interval
(multiples) and is distinguished by a minor attenuation of the wavelet amplitudes.
Some features could disappear laterally due to local variations of the EM properties of
the interface or the top material (i.e., higher σ, so higher attenuation). See, for example,
the first 22 m of Figure 8.1b.
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•
If the changes of the EM properties are gradual compared to the wavelength, the interface does not produce a reflection; for example, when the groundwater table has a high
capillary fringe with a progressive decline of the degree of saturation. The groundwater
level is generally more evident in coarse deposits than in fine sediments.
The ground structures that dip with high angles are represented in the radargram rotated
towards the horizontal plane if the GPR section is not migrated. The extreme case is a
vertical interface displayed as a feature dipping with an angle of 45º.
•
REFERENCES
Annan, A. P. 2009. Chapter 1 – electromagnetic principles of ground penetrating radar. In Ground
Penetrating Radar Theory and Applications, ed. H. M. Jol. Amsterdam: Elsevier2, pp. 1–40. ISBN:
9780444533487. https://doi.org/10.1016/B978-0-444-53348-7.00001-6
Cassidy, N. J. 2009a. Chapter 2 – electrical and magnetic properties of rocks, soils and fluids. In
Ground Penetrating Radar Theory and Applications, ed. H. M. Jol. Amsterdam: Elsevier, pp. 141–
176. ISBN: 9780444533487. https://doi.org/10.1016/B978-0-444-53348-7.00002-8
Cassidy, N. J. 2009b. Chapter 5 – ground penetrating radar data processing, modelling and analysis.
In Ground Penetrating Radar Theory and Applications, ed. H. M. Jol. Amsterdam: Elsevier,
pp. 141–176. ISBN: 9780444533487. https://doi.org/10.1016/B978-0-444-53348-7.00005-3
Reynolds, J. M. 2011. An Introduction to Applied and Environmental Geophysics. 2nd ed. Hoboken,
NJ: John Wiley & Sons, Ltd. ISBN: 978-0-471-48535-3
Chapter 9
Reflection seismic methods
Richard D. Miller
9.1
INTRODUCTION
Seismic reflection is a very powerful geophysical imaging and characterising tool with few
inherent limitations or requirements of the targeted earth interval. Applying the method to
near-surface problems requires consideration and compensation for the unique characteristics of the developing seismic wavefield. Like all seismic methods, seismic reflection uses
mechanical/sound vibrations measured as a function of time. Most of the unique challenges
of shallow reflection imaging are related to the very complex, heterogeneous, and non-linear
nature of the near-surface. Common applications of the near-surface seismic method include
mapping the top of bedrock, delineating bed or layer geometries, identifying changes in
subsurface material properties, detecting voids or fracture zones, mapping faults, defining
the top of the water table, mapping confining layers, and estimations of elastic wave velocity
in subsurface materials.
9.2
BASIC PHYSICS OF SEISMIC WAVEFIELDS
Seismic energy propagating in the earth consists of body waves and surface waves (Press
and Siever 1986). Reflection methods traditionally employ body waves (Telford et al. 1990).
The primary or compressional part of the body waves (the P-waves) propagates through
the earth analogous to sound waves propagating in the air with particle displacement in the
direction of the travel. The secondary or shear part of the body waves (the S-waves) propagates through the earth with particle displacement in directions perpendicular to the direction
of travel. However, in the subsurface, the media is more complex and heterogeneous with
body wave energy being only part of the total wavefield. Surface waves (Chapter 11) are the
dominant part (in total energy) of a seismic energy pulse and propagate along the free surface of the earth much as a wave on the ocean moves toward shore. Surface waves penetrate
the earth to depths relative to their wavelength, which for most shallow seismic-reflection
surveys puts their arrival coincident with a large percentage of the shallow reflected body
waves.
Seismic wave propagation is dependent on the elastic properties of the rocks (Telford
et al. 1990). The applied force associated with particle displacement is related to the deformation of the material as defined by Hooke’s law. This stress/strain response is expressed
as the shear modulus, which is defined by Lame’s constants and basically represents the
material’s resistance to shearing strain. The pure seismic wavefield is composed of surface
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waves and two types of body waves: compressional (P) and shear (S). A seismic pulse from a
controlled source instigates a wavefield that eventually spawns all the components of elastic
waves consistent with the earth materials.
The propagation velocity of the body waves is dictated by:
Vp =
˙( K + 4µ / 3) / ° ˘
ˆˇ
Vs = (µ / ° )
1/ 2
{
= E / ˙ˆˇ 2° (1 + ˛ )˘
(9.1)
}
1/ 2
(9.2)
where:
Vp = compressional wave velocity,
K = bulk modulus,
μ = shear modulus,
ρ = density,
E = Young’s modulus,
σ = Poisson’s ratio, and
Vs = shear wave velocity.
Seismic energy arrival time at individual seismic sensors depends on the wave type, travel
path, and the material’s velocity (Pelton 2005). The travel path of reflected body waves is
controlled by the subsurface material velocity and geometry of interfaces defined by acoustic impedance (product of velocity and density) changes. A difference in acoustic impedance between two layers results in an impedance contrast across the boundary separating
the layers and determines the reflectivity (reflection coefficient) of this boundary – e.g., how
much energy is reflected versus how much is transmitted (Eq. 9.3). At normal incidence,
R=
rV
r2V2 − r1V1
and A = 2 2
r1V1
r2V2 + r1V1
(9.3)
where:
R = reflectivity = reflection coefficient,
V1, V2 = velocity of layers 1 and 2,
ρ1, ρ2 = density of layers 1 and 2, and
A = impedance contrast.
Snell’s law (Eq. 9.4) describes the relationship between angles of incident, refracted, and
reflected seismic waves.
i r
V1
V2
t
V1
V
V
= 1 = 2
sin i sin r sin t
(9.4)
Reflection seismic methods
97
where:
i = incident angle,
r = reflected angle, and
t = refracted angle.
At each boundary represented by a change in the product of velocity and density (acoustic
impedance), the incident seismic wave generates a reflected P-, reflected S-, transmitted P-,
and transmitted S-wave. This process is described by the Zoeppritz equations in Telford et
al. (1990). Analysis and recognition of seismic energy arrival patterns at different seismic
sensors allow estimating depths to reflectors (impedance contrasts) and average velocity
between the reflection and the earth’s surface.
Energy partitioning dictates how these different wave types are distributed within the
recorded wavefield. Each wave type has the potential to be signal or noise depending on the
method and associated recording techniques (Figure 9.1). Refracted waves are widely used
to establish the average subsurface velocity (Chapter 10). Reflected waves have the potential
to provide significantly more information about the subsurface and at higher resolutions than
most other geophysical imaging methods.
It is imperative that special care be taken to process and analyse near-surface data with
the full awareness that all modes of the elastic wave are likely to be present in the full
wavefield (Figure 9.2). The key to differentiating different modes of the full elastic wavefield is understanding the limitation and preferential/potential travel paths of energy pulses
between source and receiver. It is critical that each component of the wavefield be identified to avoid handling signal like noise or, worse, handling noise like signal. Assumptions inherent with each data analysis operation depend on the energy being targeted for
each processing step to enhance reflections from subsurface acoustic impedance contrasts.
Artefacts from inappropriate or misused processing operations can inhibit the possibility of fully exploiting the method’s potential and the accuracy of the resulting image or
characterisation.
A
C
B
D
E
BODY WAVE
A: Air wave
B: Direct wave
D: Reflected wave
E: Refracted wave
SURFACE WAVE
C: Ground roll
Figure 9.1 Generalised propagation path for each primary wave mode produced from a surface
impulse of mechanical energy in a homogeneous half-space.
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Source-to-receiver offset (m)
15
1.5
44
0
15
1.5
44
Time (ms)
50
100
150
(a)
Ground roll
Air-coupled wave
Optimum recording window
(b)
Refraction
Reflection
Direct wave
Figure 9.2 Gained feld records from two different positions on 1 seismic line.The refection arrivals
are shown on both records.
Seismic data is typically displayed as the velocity or acceleration of particles as a function
of time. A wavefield is generally displayed in wiggle trace format, for which the display’s
horizontal axis shows the linear source-to-seismic-sensor offset (Figure 9.2). Particle motion
is displayed on the seismogram vertical axis and referenced to the instant in time the source
energy was generated (t0). The arrivals of the wavefield at each seismic sensor are synchronised in time based on the selected digital sampling rate of the seismograph. Each seismic
event of the wavefield represents different travel paths, particle motions, and velocities of the
energy spreading outward from the seismic source.
9.3
FUNDAMENTAL PRINCIPLE OF REFLECTION
SEISMIC METHOD
Reflection is a continuous profiling method with high-resolution potential and few limitations related to material types or depositional sequences. Images closely resemble geologic
cross sections and therefore are intuitive for non-geophysical end users to understand.
Reflection imaging also has disadvantages such as being relatively expensive, technically
challenging, and sensitive to cultural noise, as well as usually requiring 4–5 days to process
for each day of acquisition. Further, soils of the shallow subsurface often have small acoustic
contrast, which can cause unclear imaging.
Reflection seismic methods
Source
99
Receiver
Clay
Figure 9.3 Ray paths for propagating body wave energy travelling between source and receivers when
encountering distinct velocity contrasts in the subsurface.
Near-surface seismic reflection requires a high-frequency energy source and excellent
coupling to generate broadband wavelets essential for high-resolution images (Steeples
and Miller 1990); see also the cases in Chapters 39 and 40. Seismic energy propagates
from the source through the earth, reflects off subsurface features (velocity/density contrasts (e.g., alluvium/bedrock contact, limestone/shale contact)), and returns to the surface
(Figure 9.3).
These seismic waves returning from reflectors in the earth are recorded as a function of
time starting from the instant the energy was imparted to the ground by a source (Figure 9.4).
Seismic sources come in all types and use a range of methods to optimally impart energy into
the ground (Miller et al. 1992). They can range from simple means as sledgehammers to as
complex as coded sources like seismic vibrators. Seismic sensors, analogous to microphones
deployed along the ground surface in a known geometry, respond to mechanical energy by
producing an electrical response representative of the velocity or acceleration of the ground.
Sound waves travelling from the source reflect at contacts where the velocity or density
of materials increases or decreases abruptly and returns to the ground surface along different travel paths for each source and receiver pair (Figure 9.5). Reflection wavelet arrivals
from a single reflector recorded on multiple seismic sensors spaced at different offsets from
the source can be collectively used to estimate the average velocity of material between the
reflection point and seismic sensor.
Seismic energy returning from reflectors as reflection wavelets follow travel paths dictated by the velocities of the materials through which they propagate en route from source
to a receiver. This results in reflection wavelets from different source and receiver pairs but
from the same subsurface location requiring different travel times and therefore arriving at
a time determined by material velocity and travel distance. Correcting for different travel
paths using the average velocity between the reflector and ground surface allows all traces
to appear as if the travel path was vertically incident. It is common for near-surface material
velocities to change rapidly and produce a velocity gradient that results in conflicting normal
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Time break Wire
(start time)
Roll-along Box
Source
optional
Seismograph
Seismic cable
Sensors
(one or more
at each location)
Takeouts
Layer 1
V1 p1
R1
Layer 2
V2 p2
v = velocity, p = density, R = reflector
R2
Figure 9.4 Generalised feld layout for seismic acquisition system.The recording system begins saving
samples converted from analogue to digital data at the moment of source impact. Sensors
respond to changes in velocity or acceleration of the ground-coupled device.
Source
Receiver
Time
Reflection
Figure 9.5 Model refection arrivals on traces at different offsets from the source. The wave trains
are recorded in time and are consistent with refections from the model in Figure 9.3. The
hyperbolic curves along the wave train are representative of a particular offset distance
from the source (normal move-out curves).
Reflection seismic methods
101
Time
Figure 9.6 Seismic traces with refection wavelets from Figure 9.5 that are time-corrected for an earth
material velocity that increases with depth (normal move-out correction). These traces
now simulate vertically incident ray paths between source and receiver.
Clay
Figure 9.7 Superposition of earth model used to show the ray paths of the seismic wavelets with offset
and velocity corrected vertically incident seismic traces with refection wavelets correlated
to both the material change and the relative velocity change at that interface (lower velocity
to higher velocity or higher velocity to lower velocity – changes to the wavelet polarity).
move-out velocity curves at longer offsets (Miller 1992; Miller and Xia 1998). This highvelocity gradient situation must be considered when correcting multiple reflection wavelets
to vertical incidence. Reflection wavelets arriving at consecutive receivers from a single
reflecting interface can be displayed as the time image of the reflector once corrected for
different travel paths (Figure 9.6).
Additionally, wavelet characteristics can be analysed to provide insights into the material properties at, above, and immediately below the reflector (Figure 9.7). Reflection events
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recorded across several offset sensors can be used to characterise properties of the subsurface
such as continuity, thickness, and depth of layers and changes in velocity and material type.
9.4
ACQUISITION REQUIREMENTS FOR
SHALLOW SUBSURFACE
Reflection seismic profiling is best performed using multiple receivers for each source point
and multiple source points for each receiver (multichannel seismic data). This allows for
stacking the channels representing the same midpoint between source and receiver, increasing the signal-to-noise ratio considerably.
Acquiring meaningful reflection data for shallow targets requires well coupled sources
and receivers and high-resolution data. High-resolution seismic reflection is defined as
reflection sections with a dominant frequency greater than 80 Hz (Sheriff 2002). Only on
rare occasions can reflection wavelets with dominant frequencies lower than 80 Hz provide
stacked sections with discriminable reflections from multiple shallow targets. A broadband
source and recording system is imperative when considering the shallow subsurface of less
than 100 m.
Broadband data for near-surface applications consist of reflection wavelets with a bandwidth in excess of two octaves. Increasing the bandwidth effectively decreases the width and
side lobes of a reflection wavelet (decreases ringy waveforms), pushing the wavelet closer
to a spike in time. For near-surface reflection imaging, the data need to be high-frequency,
broadband, and high signal-to-noise. Rarely are those ideal criteria for spectral and signal
characteristics of near-surface reflection data met and hence the challenges of pushing data
during processing to improve those characteristics on the final stacked sections.
Near-surface reflection data requires optimal acquiring geometries, a well tuned source,
and high-quality sensors (Hunter et al. 1984). Modern seismographs can be centralised, distributed, or nodal, with benefits and shortcomings of each type. The system best suited for a
particular project depends on the application and first metre of earth material. High quality
sensors with a low noise threshold are essential and not difficult to come by. It is, however, imperative that sensors be given some degree of attention when designing an acquisition program. Sensors can be geophones (magnet and coil devices that measure velocity)
or accelerometers (solid-state devices that measure acceleration), with neither being a clear
go-to choice for every situation. Finally, a shallow seismic source can be a make-or-break
acquisition component in planning a successful data acquisition program. Site limitations,
safety concerns, repeatability, coupling, environmental impact, energy partitioning, and
mobility are just a few things to consider (Miller et al. 1986). However, sometimes collecting high-frequency, broadband, noisy data with relatively low amplitude reflected energy
can prove superior to high signal-to-noise data with lower resolution potential and narrow
bandwidth (Figure 9.8).
9.5
RESOLUTION AND UNCERTAINTY
Resolution of seismic-reflection data depends on velocity, wavelet dominant frequency and
bandwidth, or depth to target. The wavelength of a seismic wavelet ultimately controls resolution (both vertical and horizontal). Wavelength (λ) is velocity divided by dominant frequency. Therefore, to increase the resolution (resolving power) of data, the frequency of the
Reflection seismic methods
0
0
Source offset (m)
103
30
10
Time (ms)
20
30
40
50
60
Figure 9.8 This 120 μs, 96-channel shot gather with receivers spaced at 0.5 m is contaminated by
source, coherent, and random noise but, once processed, will produce more than a dozen
high-quality refections with dominant frequencies greater than 300 Hz and a resolution
potential of less than 1 m. Ugly raw data can transform into that swan once properly
processed.
Figure 9.9 Seismic wavelet is superimposed on a bed that represents 1/4 the wavelength of this wavelet, showing that an abrupt contact in the subsurface is represented on stack refection
sections as a wavelet with duration.
reflecting wavelet must be increased since velocity is native to the material. Increasing the
dominant frequency is almost exclusively related to improvements in source configuration,
type, or deployment.
Vertical resolution is generally modelled to be 1/4 λ with migration using a highly accurate velocity that can be reduced to 1/10 λ (Miller et al. 1995). From a practical perspective,
planning for 1/2 λ is always a safe approach since vertical resolution greater than 1/4 λ
requires very high signal-to-noise ratio. Often for near-surface reflection arrivals, there is
significant coherent noise interference. See Figure 9.9.
Horizontal resolution is traditionally estimated using a concept from optics: Fresnel zone.
For seismic-reflection images, the Fresnel zone is considered to be around 1/4 λ on the surface of a reflector as a function of depth (Brühl et al. 1996). This distance is the radius of the
first Fresnel zone and can be calculated as the square root of half the wavelength times depth.
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r~
~
r~
~
1/2yh
1/2V
T0/f
h+
y /2
h+
h
y/4
r - (First) Fresnel Zone
V - Avg Velocity to T0
h - Depth to Reflector
y - Wavelength
T0 - Vertical Incident
Two-way Travel Time
f - Dominant Frequency
at Depth h
r
Figure 9.10 Radius of the frst Fresnel zone has been the classical defnition for the smallest feature
that can be discriminated horizontally.
Uncertainty in interpretations of seismic-reflection images can be qualitatively estimated
based on the accuracy of the velocity function, signal-to-noise ratio, spatial sampling, and
degree of lateral heterogeneity in the upper few metres. A quantitative measure of uncertainty
is not possible due to the extreme number of variables that must be considered to provide
a definable value. It is, however, possible to estimate the uncertainty as a measure of confidence in the depth interpreted for a reflector based on the reflection and data characteristics.
9.6
INTERPRETATION
The seismic-reflection method depends on the presence of discrete seismic-velocity or massdensity changes in the subsurface, which represent acoustic impedance changes. Mathematically, acoustic impedance is proportional to the product of mass density and acoustic wave
velocity. Reflections may or may not occur at natural boundaries between geologic layers or
human-made boundaries such as tunnels and mines. The classic use of seismic reflection is
to identify the horizontal and gently dipping boundaries of layered geologic units. However,
the technique can also be used to search for localised anomalies such as sand or clay lenses
and faults (Miller and Xia 1997) (Figure 9.11). Cross-sectional images of reflectors (velocity
or density contrast) as a function of time (vertical) and distance between receivers (horizontal) are used to interpret subsurface conditions and materials.
Geologic and hydrogeologic data obtained from borehole logs, geologic maps, data
from outcrops, or other surface and borehole geophysical methods are generally helpful
in interpreting subsurface conditions using seismic-reflection data (Figure 9.12). The seismic-reflection method provides a non-unique representation of the subsurface. When layer
boundaries are visualised without supporting or complementary data, properties cannot be
uniquely or confidently interpreted.
Reflection seismic methods
North
160
180
200
220
240
260
280
CDP numbers
300
320
340
360
380
400
420
440
105
South
460
Time (ms)
50
100
150
0
Basal contact of channel cuts
Pungo River confining unit
Channel infill
Upper Castle Hayne confining unit
Yorktown confining unit
Lower Castle Hayne confining unit
100 ft
Figure 9.11 Stacked seismic section displayed in two-way travel time on the Y-axis and station numbers
on the X-axis. Interpreted layers are shown with thickness in time, but the coloured portion is the refection wavelet representing the contact between two different materials.
The embedded set of traces around station 340 is an uphole survey designed to correlate
time to depth to optimise the conversion of time (native measure for seismic data) and
depth for geologic interpretations.
0
Ideal
0
Actual
Time
Depth
Surface
Figure 9.12 Model demonstrating the correlation of seismic-refection traces to geologic cross
sections.
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9.7
CONCLUSIONS
Near-surface seismic-reflection imaging is an extremely powerfull and one that provides
greater resolution and that more closely approximates/resembles a geologic cross section
without assumptions as to material characteristics or signal degradation due to environmental interference than any other geophysical technique (Yilmaz 2015). Seismic-reflection
imaging is a multistep method that requires a methodical approach to data acquisition, technically sound processing software and workflow, and interpretations that understand how a
wiggle trace and stacked reflections relate to a geologic cross section and changes in material
properties across discrete boundaries.
REFERENCES
Brühl, M., G. J. O. Vermeer, and M. Kiehn. 1996. Fresnel zones for broadband data. Geophysics
61:600–604.
Hunter, J. A., S. E. Pullan, R. A. Burns, R. M. Gagne, and R. S. Good. 1984. Shallow seismic reflection mapping of the overburden-bedrock interface with the engineering seismograph – Some simple
techniques. Geophysics 49:1381–1385.
Miller, R. D. 1992. Normal moveout stretch mute on shallow-reflection data. Geophysics 57:1502–1507.
Miller, R. D., N. L. Anderson, H. R. Feldman, and E. K. Franseen. 1995. Vertical resolution of a
seismic survey in stratigraphic sequences less than 100 m deep in southeastern Kansas. Geophysics
60:423–430.
Miller, R. D., S. E. Pullan, D. W. Steeples, and J. A. Hunter. 1992. Field comparison of shallow seismic
sources near Chino, California. Geophysics 57:693–709.
Miller, R. D., S. E. Pullan, J. S. Waldner, and F. P. Haeni. 1986. Field comparison of shallow seismic
sources. Geophysics 51:2067–2092.
Miller, R. D., and J. Xia. 1997. Delineating paleochannels using shallow seismic reflection. Leading
Edge 16(11):1671–1674.
Miller, R. D., and J. Xia. 1998. Large near-surface velocity gradients on shallow seismic reflection
data. Geophysics 63:1348–1356.
Pelton, J. R. 2005. Near-surface seismology: Wave propagation. In Investigations in Geophysics No.
13, Near-Surface Geophysics, ed. D. K. Butler. Tulsa, OK: Society of Exploration Geophysicists,
pp. 177–217.
Press, F., and R. Siever. 1986. The Earth. 4th ed. New York: W. H. Freeman and Company, 656 p.
Sheriff, R. E. 2002. Encyclopedic Dictionary of Applied Geophysics. 4th ed. Tulsa: Society of Exploration Geophysicists. https://doi.org/10.1190/1.9781560802969.fm
Steeples, D. W., and R. D. Miller. 1990. Seismic-reflection methods applied to engineering, environmental, and groundwater problems. In Investigations in Geophysics No. 5, Volume 1: Review and
Tutorial, ed. S. H. Ward. Tulsa, OK: Society of Exploration Geophysicists, pp. 1–30.
Telford, W. M., L. P. Geldart, and R. E. Sheriff. 1991. Applied Geophysics. Cambridge: Cambridge
University Press. https://doi.org/10.1017/CBO9781139167932
Yilmaz, O. 2015. Investigations in Geophysics No. 17, Engineering Seismology with Applications to
Geotechnical Engineering. Tulsa: Society of Exploration Geophysicists.
Chapter 10
Seismic refraction methods
Colin A. Zelt
10.1
INTRODUCTION
The velocities of elastic (seismic) waves, e.g., compressional or pressure waves (P-waves)
and shear waves (S-waves), vary in different materials. By determining the velocity of these
waves, it is possible to interpret subsurface structure and the distribution of geological materials with different seismic properties. Additional geological information from boreholes
is often used to improve the geotechnical relevance of the velocity models. The seismic
refraction method utilises the first arrivals of seismic traces, typically a refracted wave, for
mapping the velocity distribution and thereby layering geometries in the subsurface. Either
through classical layer-based, graphical, or analytical techniques or tomographic methods,
analogous to techniques used in medical imaging and global earthquake seismology. By
comparison, the seismic reflection method uses echoes (reflections) from discontinuities or
sharp transitions in elastic properties in the subsurface to locate the position of lithological
boundaries (see Section 9 on seismic reflection methods).
The seismic refraction method is often applied to evaluate bedrock variation e.g., caused
by erosion, weathering, or faulting/fractured zones (see the cases in Chapters 29, 30, and 37;
for karst/faults: see the case in Chapter 48). Depth to bedrock and layering of sediments (see
the cases in Chapters 38 and 45; e.g., soft soils: see the case in Chapter 43). It is also used to
map sediment layers and properties in engineering, aquifer, and environmental studies (see
the cases in Chapters 22 and 31), as well as slope stability investigations (see the cases in
Chapters 33 and 34).
10.2
BASIC PHYSICS
A blow to the ground, e.g., with a hammer or weight drop, generates a wave at the impact
location, denoted the “shot point” or “source location”. In the following discussion, “source”
is used. The wave travels in all directions into and through the ground at a velocity determined by the subsurface properties. Think of the way waves propagate when a stone is
dropped into the water. According to Fermat’s principle, the first-arriving wave will have
travelled along the path between the source and a receiver that takes the least time, and since
velocity in the near-surface most commonly increases with depth, this means the path may
not be the most direct one, but a path that penetrates to some depth, bends or refracts and
eventually returns to the surface (Figure 10.1a). Though invisible to the eye, the wave generated in the ground can be recorded by sensitive ground-motion-detecting instruments called
geophones (receivers) deployed on the earth’s surface.
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0
Geophone
40
T (ms)
Source
80
120
(a)
160
(b)
0
1
0
Wave velocity (km/s)
2
3
4
3
6
9
12
15
O˜set (m)
5
18
21
6
24
7
Dry sand
P-waves
Clay
S-waves
Wet sand
Glacial till
Mudstone
Sandstone
Limestone
Granite
(c)
Basalt
layer 1
layer 2
Z
(d)
layer 3
(e)
Z
(f)
(g)
X
X
Figure 10.1 Illustration of the seismic refraction method. (a) Cross section of a typical experiment
showing an array of geophones on the surface and example ray paths going through a
two-layer model from a source at the left end of the array (star). (b) Example of real
data showing a shot gather with 72 traces with source-receiver offset increasing to the
right; frst-arrival picks indicated by a line (Chen et al. 2017). (c) Ranges of P- and S-wave
velocities in common near-surface materials; for the bedrock materials weathering can
lower the velocities considerably (mostly from Earle 2019, with some minor adjustment
from Kearey et al. 2002). (d) Example of the type of blocky, layered velocity model parameterisation used with graphical, analytical and some ray tracing algorithms; open circles are
velocity nodes, squares are boundary nodes. (e) Example of a fnely gridded velocity model
parameterisation used with most tomographic algorithms; velocity or slowness is either
defned at each grid point or with constant values in each pixel; (f) Actual velocity model
using the parameterisation in (d). (g) Actual velocity model using the parameterisation in
(e).The same scale is used in (f) and (g) to represent velocity. Both models were derived
from the same set of travel times using real data from 16 sources indicated by black dots.
Only portions of the models sampled by ray paths are shown with isovelocity contours
of 5 km/s (black) and 6.0 and 6.1 km/s (white). (Parts (d)–(g) modifed from Zelt [2021].)
Seismic refraction methods
109
A seismic refraction profile is produced by recording the arrival times of a wave at several
geophones usually connected to a cable at an even spacing along a straight line, as illustrated
in Figure 10.1a. Figure 10.1b shows an example of real data for a single shot recorded by 72
geophones, this is called a shot gather (Chen et al. 2017). The geophones record the waves
that have travelled along the profile. Based on the travel times of the seismic waves to the
different geophones, the subsurface seismic velocities and positions of layer boundaries can
be estimated. After picking the first-arrival times, either manually or using a semi-automated
approach, they will define a straight line if the simplest assumption that each layer has a
constant velocity is valid. However, this is seldom the case in the real earth, and both vertical and lateral velocity gradients can cause minor or significant deviations from straight-line
arrival patterns (Figure 10.1b). Velocity variations may be mapped with how they change
with depth or in a vertical cross section called a two-dimensional (2D) model beneath the
source-geophone array. Layer boundaries, typically corresponding to changes in lithology or
water saturation, are mapped as a horizontal interface, a planar dipping interface, or a surface
with piece-linear or smoothly varying structural relief.
Due to compaction, velocities tend to increase downwards and with increasing distance
between the source and a geophone, it is more likely that the first-arriving wave path samples
deeper parts of the ground. This is fortunate since the generally increasing velocity with
depth is a requirement for the seismic refraction method to work when using the common
experimental arrangement with sources and geophones at the earth’s surface. If velocity
decreases with depth, the fastest path between the source and the receivers remains in the
shallow high-velocity layer so that the first-arriving seismic waves do not sample the deeper
low-velocity layer.
Most of this section has assumed 2D P-wave refraction experiments and data analysis.
However, 3D engineering-scale surveys and analysis are possible and sometimes necessary (Zelt et al. 2006), but 3D surveys are significantly more expensive, time-consuming,
and thus relatively rare. S-wave experiments to estimate the shear wave velocity or modulus in the subsurface or combined P- and S-wave experiments to estimate Poisson’s ratio
(Uhlemann et al. 2016) are more common than 3D surveys but still not the norm. In order
to generate significant shear waves, specially designed S-wave sources are needed, such as
a hammer or horizontally propelled weight impacting the vertical side of an object that is
well coupled to the ground (Chen et al. 2017). In addition to providing independent physical
properties of the subsurface, S-waves are always slower than the corresponding P-waves in
the same material and therefore have shorter spatial wavelengths and theoretically provide
higher resolution. Unfortunately, in most cases, it is harder to generate S-waves with as good
a signal-to-noise ratio as P-waves, so the potentially higher resolution is often challenged by
higher uncertainty due to noisier data.
10.2.1
Physical properties related to lithology
or specific targets
Seismic velocity increases with stiffness (Figure 10.1c), and, with few exceptions, it correlates positively with the density and strength of sediments and rocks. Stiffness is the ability of a material to resist deformation when stress is applied, whereas strength is the ability
of a material to withstand stress without breaking. Thus weathered or fractured bedrock
has lower seismic velocity compared to the corresponding competent intact version of the
rock. Loose or unconsolidated soils typically have lower velocities than consolidated or
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Engineering Geophysics
compacted soils. Soils typically become more compact with depth due to the increasing
load of the overburden, so velocity most commonly increases with depth. For an elaborate
example of how P- and S-wave velocities from refraction seismic data have been used to
establish elastic properties and mitigate landslide risk in specific lithologies, see the cases in
chapters 33 and 34.
10.2.2
Limitations
For the refraction method to work best, velocities must increase with depth. If velocity
decreases with depth, only an upper bound on the low-velocity values can be estimated
using first-arrival travel time data. However, the avoidance of low-velocity layers or of
low-velocity bodies of limited lateral extent by first-arriving waves causes a time delay
of the first arrivals as compared to the otherwise same earth model without a low-velocity
layer or body so that the low-velocity zone can be detected and an upper bound on the low
velocities can be determined using advanced analysis techniques. All things being equal,
bodies of limited lateral extent are more likely to be identified as anomalies in a seismic
profile than low-velocity layers extending across the entire length of the profile. Thus
limestone interbedded with soft marlstone/clay layers is not a configuration that is well
resolved by the refraction method. However, an average velocity of the interbedded layers may be determined. When the velocity increases with depth, the method is suitable for
mapping, e.g., the depth to more competent layers. These could be bedrock or limestone
below sediments, compact glacial deposits underneath looser postglacial deposits, etc.
Figure. 10.1c shows seismic velocities in common near-surface materials (Earle 2019).
Dry, unconsolidated sediments at the surface can have a seismic velocity that is less than
sound velocity (about 340 m/s). In this case, the first-arrivals at the geophones closest to the
source will be the airwave as opposed to a seismic wave that has travelled through the earth,
and this scenario should be easy to diagnose as the first-arrivals would follow a perfectly
straight line with an apparent velocity of about 340 m/s. Further, if it is not possible to pick
the first seismic wave after the airwave, the velocity of the ultra-slow surface layer is not
constrained other than to know that it is less than 340 m/s.
10.3
RESOLUTION AND UNCERTAINTY
As a rule of thumb, the maximum depth of investigation for a refraction profile is about 1/5
of its total length (Table 10.1), including sources. However, this factor can vary significantly
depending on how rapidly or slowly velocity increases with depth in the study area. As a
result, deep investigations of more than a few tens of metres in depth may require a large
energy source for the arrival times to be confidently picked with a sufficient signal-to-noise
ratio over the entire profile length. This is because energy is lost since:
•
•
•
It propagates in all directions, spreading over a larger area at greater distances.
It is reflected and/or converted between P- and S-waves at sharp transitions or layer
boundaries.
Earth is not perfectly elastic, particularly when considering unconsolidated sediments in
the shallow subsurface, so additional wave energy is continuously lost during propagation due to inelasticity, effectively in the form of frictional heating.
Seismic refraction methods
111
Table 10.1 Comparison of different source types.
Source type
Purpose
Typical max. depth
of investigation
Resolution
Notes
Sledgehammer Understand
shallow layer
geometry
Max. 1/5 of layout
length including
offset shot
Does not work with
soft soils.
High data
coverage
Multiple blows
at the same
location are
possible for
stacking to
higher quality.
Typically not
deeper than
10–15 m, but very
site-dependent,
potentially as deep
as 50 m at quiet
times and with
competent soils/
rock.
Depends on the
number of shots
and geophone
spacing.
Horizontal:
Depends on
geophone spacing,
decreasing with
depth.
Vertical: Depends
mainly on wave
frequency.
Risk of hidden
layers:
Seismic
shotgun
Accelerated
weight drop
Explosives
Good in soft
soils; not
applicable in
hard rock
A velocity inversion
(downwards
Max. 1/5 of layout decreasing velocity)
length including
is unresolved.
offset shot
Tomographic
approaches may
Depth of
penetration similar potentially resolve
these.
to that of the
sledgehammer.
Thin layers can
Often gives
be challenging to
improved defnition resolve. A layer
of frst arrivals
must be visible in
compared to
several geophones
sledgehammer.
to be identifed.
When
information
from a greater
depth is
required
Max. 1/5 of layout
length including
offset shot
When
information
from great
depth is
required
Max. 1/5 of layout
length including
offset shot
Depth of
investigation
depends on the
weight used
Depends on
the amount of
explosives. Softer
sediment requires
more potent
explosives for the
same penetration.
Specifc theoretical
calculations using
synthetic models
is the best way
to investigate
what survey setup
can resolve the
expected velocity
contrasts and layer
dimensions.
Fastest method,
requires a strong
person for a higher
amount of energy.
Heavier hammer
means higher quality
but slower data
collection or larger
crew needed.
Depending on site
conditions, a crew of
3 people may collect
between 100 m to 1
km/day.
Useful for soft soils
to stiff soils.
Requires special
ammunition.
One-shot equals
approximately 1 g of
explosives.
The shot damages
the hole. Thus
stacking is
impossible unless
more holes are
available.
More energy
provides a better
signal-to-noise ratio.
Survey time: It takes
longer to collect
the data (including
drilling boreholes),
but better data
quality may mean
less time required
for data analysis and
inversion.
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Engineering Geophysics
The determination of a 2D velocity model from travel time data that allows for arbitrary
lateral velocity variations is a non-linear inverse problem. It is, therefore, difficult to precisely estimate the resolution and uncertainty of a velocity model from seismic refraction
data, both in general and in particular for a model from a specific set of data. However, we
know that spatial resolution depends primarily on the number and spacing of source points
and geophones, as well as the dominant frequency of the seismic waves. In a typical land
refraction survey, a set number of geophones are available for the experiment, and all should
be used, evenly spaced along the profile to allow for maximum resolution. For sources, the
number is usually determined by the time, effort, and cost associated with each source point.
Again, to allow for maximum resolution, the greatest possible number of source points,
evenly spaced, should be used. The absolute minimum number of source points should be
two, one at each end of the profile. This is called a reversed refraction experiment, and in the
simplest case, it allows the dip on a boundary to be unambiguously determined, something
that is not possible with a single shot.
From a theoretical perspective, it is not necessary to use a source point or geophone spacing that is significantly less than one-half of the spatial wavelength corresponding to the
dominant frequency of the seismic data. Therefore, the dominant frequency of the seismic
data places a limit on spatial resolution. However, from a practical perspective, it may be
necessary to have a spacing of less than this to aid in the ability to confidently pick the firstarrival times when the subsurface contains very strong lateral velocity variations. The frequency of the data is determined by the source characteristics, including its ability to couple
to the ground and the degree of subsurface inelasticity in the study area. High frequencies
are more rapidly attenuated than low frequencies. Thus, generally speaking, the amount of
inelasticity or attenuation correlates negatively with seismic velocity: low-velocity materials
are more attenuating and vice versa.
Sometimes a seismic refraction survey is carried out to locate an object in the subsurface,
as opposed to determining the overall variations in velocity in the study area. In this case, it
is more appropriate to think in terms of detection as opposed to resolution. For detection, a
target’s dimensions can be smaller than the length scales required for resolution, even less
than the dominant wavelength of the seismic waves (Chen et al. 2017).
Model uncertainty or error is dependent on the noise level in the data; the uncertainty of
the travel time picks; the accuracy of the physics of wave propagation used, i.e., wave theory
versus curved paths versus straight paths; and the limitations imposed by the model parameterisation. An example of the latter is when a model with constant-velocity dipping layers
is used, but the real structure in the study area does not conform to this model. Ray theory,
an infinite frequency approximation to the wave equation, is almost always assumed in the
analysis of seismic refraction data. However, shallow refraction data are often low frequency,
yielding long spatial wavelengths relative to the structural variations. An additional source
of potential error comes from the common use of linear refraction profiles that are limited to
constraining 2D subsurface structure when the real earth contains 3D structural variations.
10.4
ACQUISITION REQUIREMENTS
The field layout consists of a communication cable connecting a data logger and geophones.
Furthermore, a source is needed for creating P- or S-waves, along with a trigger that allows
an accurate origin time to be assigned to each recorded trace. Space must be available for
Seismic refraction methods
113
a secure layout of the cable spread free from disturbance during the survey. The position
of each source and geophone location must be accurately measured, including elevations.
Geophones either have spikes on the underside for inserting them into soft ground or use
a putty-like substance to attach them on hard surfaces like bedrock. Geophones may be a
single component registering only vertical motion (P-waves) or horizontal motion in one
direction (S-waves), or 3-component devices registering motion in both vertical and two
horizontal directions. The latter is needed if both P- and S-waves are to be analysed or if a
type of elastic waveform inversion is carried out.
The layout of the geophones and sources can be any desired pattern, for example, to
allow denser spacing in more important areas of the survey, but uniform spacings are most
commonly applied. Although 2D data recorded along linear profiles are most common, 3D
surveys are also performed if either (1) a region needs to be covered and/or (2) a 2D earth
along a series of linear profiles is considered not accurate enough (Zelt et al. 2006).
The maximum distance between sources and receivers determines the depth of investigation. Sometimes sources offset from the geophone spread are used for greater depth
penetration, although it makes subsequent data analysis more complicated or limited as
potential lateral velocity variations beneath the offset shots are not well resolved. In order
to minimise the uncertainties in layer-based interpretation, it is recommended to use a line
length longer than the target area to allow the critically refracted ray path to be recorded
from the entire length of the target refractor. In addition to the profile’s total length, the
choice of source also determines the possible depth of investigation since there must be
enough energy to provide a good signal-to-noise ratio at the maximum source-receiver
offsets (see Table 10.1). Large energy sources for engineering studies include shotguns
and small samples of chemical explosives placed in boreholes, but it may be hard to get
permission to use these from the local governing authority due to concerns over damage
or safety. A compromise is to use a smaller energy source, such as a hammer or weight
drop, and to repeat multiple times at each source point and stack, i.e., sum the data to
improve the signal-to-noise ratio.
Noise may come from, for example, heavy machinery, traffic, wind, waterfalls. In busy
urban areas, noise can sometimes become acceptable if recording at night. The source type
must also be chosen in accordance with the soil or rock type at the surface; e.g., a shotgun
cannot be used if there is hard rock at the surface. Along with the frequency content inherent to the source, the density of geophones and the number of sources determine the spatial
resolution of velocity.
In summary, the planning of a refraction survey must consider the sources of noise, the
desired depth of investigation, spatial resolution, space available, and source type. Additionally, especially for shotgun or explosive sources, permission from landowners and authorities can be harder to obtain.
10.5
DATA ANALYSIS AND INVERSION
Some information can be read directly from the raw field records. For example, in Figure 10.1b,
we can ascertain that there is a relatively smooth increase in velocity with depth because
the first-arrival pattern shows a general increase in apparent velocity with source-receiver
offset, where apparent velocity is the inverse of slope in a time-offset plot. By comparison,
in some study areas, the first-arrival pattern shows a more abrupt change in slope (see the
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Engineering Geophysics
cases in Chapters 30 and 43), likely indicating an abrupt increase in velocity with depth at a
boundary separating two layers. Assuming a near-horizontal layering, the depth to the top of
a layer can be inferred from the intercept time of a straight line fit to the set of first arrivals
corresponding to waves that travel sub-horizontally in that layer. All else being equal, the
greater the intercept time, the deeper the top of the layer.
Most methods for analysing refraction data are types of inverse problems (chapter 1.4)
that convert travel time data measured at the surface into a model of seismic velocities of the
subsurface. They fall broadly into two categories:
1
2
Older, simpler graphical and analytical techniques using layered/blocky models with
discrete boundaries, usually constant-velocity, sub-horizontal layers, using head waves
that are treated as ray paths travelling along the boundaries between layers. Popular
examples in this category are the reciprocal method and the delay-time method (Palmer
1981; Mussett and Khan 2000; Pelton 2005).
Newer, more elaborate ray tracing and tomographic techniques, usually with finely sampled model parameterisations, often with constraints to keep the velocity field smooth,
using curved diving or turning rays (Zelt et al. 2013; Zelt 2021). Popular examples in
this category are ray tracing and tomographic algorithms (Figure 10.1d–g) implemented
by software packages, such as Rayinvr, RayFract® (Zelt and Smith 1992; RayFract®
2022), and TomoPlusTM (GeoTomo 2013).
RayFract includes a way to incorporate finite frequency effects into the methodology based
on the approach of Zelt and Chen (2016), making it more robust and accurate for the often
highly heterogeneous near-surface compared to most algorithms based on infinite-frequency
ray theory. The advantage of graphical and analytical techniques is that they are easier and
quicker to apply and, in some sense, provide a more robust and unique solution. However,
this latter advantage is misleading because it is due to using model parameterisations with
fewer independent model parameters and thus with less opportunity to resolve potential
fine-scale structure that may exist in the study area. The advantage of ray tracing and tomographic techniques is the ability to achieve higher spatial resolution, but at the cost of greater
computational effort, potential non-uniqueness, and time commitment to understand how the
more advanced algorithms work.
Besides the first-arrival times, conventional seismic refraction analysis disregards all
other information contained in the recorded seismograms, such as the amplitude and phase
of the first arrival, as well as other later arrivals, such as non-first-arrival refractions, diffractions, reflections, multiples, and conversions. Waveform inversion methods offer improved
resolution, including low-velocity zones. However, they are a more non-linear inverse problem than travel time inversion and are therefore prone to cycle skipping and require a good
starting model (prior information), such as from travel time methods, to overcome the strong
non-linearity. Cycle skipping occurs when the phase match between the recorded and predicted data is greater than half a wavelength and causes a local minimum that the inversion
cannot escape. Waveform methods are computationally demanding, especially using elastic
modelling, and consequently most applications use the 2D acoustic wave equation for forward modelling. Research is being carried out to overcome these issues, e.g. (Liu and Zhang
2017; Cai and Zelt 2022), and it is expected that waveform inversion will be the approach
of the near future with the obvious advantage of improved resolution, once limitations are
overcome.
Seismic refraction methods
10.6
115
INTERPRETATION
Two-dimensional models from refraction data show us the lateral and vertical variations of
subsurface velocity beneath the source-receiver profile. These models can be interpreted in
two general ways.
1
2
The velocity values can be translated into predictions of lithology and water saturation
(Figures 10.1c), ideally with the help of one or more boreholes in the study area.
The spatial velocity variations can be used to infer structure, as with seismic reflection
data, for example, depth to layers or layer thicknesses, bedrock topography (Figure 10.1d
and f ), including offsets, and the locations of isolated bodies, such as tunnels or buried
objects.
ACKNOWLEDGEMENT
The author would like to thank Anna Bondo Medhus for providing a first draft of the method
section to work from and Roger Wisén for input on Table 10.1.
REFERENCES
Cai, A., and C. A. Zelt. 2022. Early-arrival waveform inversion using data uncertainties and matching
filters with application to near-surface seismic refraction data. Geophysics (in press).
Chen, J., C. A. Zelt, and P. Jaiswal. 2017. Detecting a known near-surface target through application
of frequency-dependent travel time tomography and full waveform inversion to P- and SH-wave
seismic refraction data. Geophysics 82:R1–R17.
Earle, S. 2019. Physical Geology. 2nd ed. Victoria, BC: BC Campus. https://opentextbc.ca/physicalgeology2ed/ (accessed March 29, 2022).
GeoTomo. 2013. TomoPlus™ Source. https://geotomo.com/TomoPlus.dmx.
Kearey, P., M. Brooks, and I. Hill. 2002. An Introduction to Geophysical Exploration. 3rd ed. Oxford:
Blackwell Science.
Liu, Z., and J. Zhang. 2017. Joint travel time, waveform, and waveform envelope inversion for nearsurface imaging. Geophysics 82:R235–R244.
Mussett, A. E., and M. A. Khan. 2000. Looking Into the Earth: An Introduction to Geological Geophysics. Cambridge: Cambridge University Press.
Palmer, D. 1981. An introduction to the generalised reciprocal method of seismic refraction interpretation. Geophysics 46:1508–1518.
Pelton, J. R. 2005. Near-surface seismology: Surface-based methods. In Near-Surface Geophysics, ed.
D. K. Butler. Tulsa: Society of Exploration Geophysicists, pp. 219–263.
Rayfract®. 2022. Seismic refraction and borehole tomography software. Intelligent Resources Inc.
https://www.rayfract.com/
Uhlemann, S., S. Hagedorn, B. Dashwood, et al. 2016. Landslide characterization using P-and S-wave
seismic refraction tomography – The importance of elastic moduli. Journal of Applied Geophysics
134:64–76.
Zelt, C. A. 2021. Traveltime tomography using controlled-source seismic data. In Encyclopedia of
Solid Earth Geophysics, ed. H. Gupta. Cham, Switzerland: Springer, pp. 1828–1848.
Zelt, C. A., A. Azara, and A. Levander. 2006. 3-D seismic refraction travel time tomography at a shallow groundwater contamination site. Geophysics 71:H67–H78.
Zelt, C. A., and J. Chen. 2016. Frequency-dependent travel time tomography for near-surface seismic
refraction data. Geophysical Journal International 207:72–88.
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Engineering Geophysics
Zelt, C. A., S. Haines, M. H. Powers, et al. 2013. Blind test of methods for obtaining 2D near-surface
seismic velocity models from first-arrival traveltimes. Journal of Environmental Engineering Geophysics 18:183–194.
Zelt, C. A., and R. B. Smith. 1992. Seismic travel time inversion for 2-D crustal velocity structure.
Geophysical Journal International 108:16–34.
Chapter 11
Surface wave methods
Julian Ivanov
11.1
INTRODUCTION
Mapping of sediment velocities with seismic surface waves can support geotechnical site
investigations but requires an understanding of how the surface waves interact with the soils
and the surface itself. The method can provide shear wave velocity for soil volumes, especially in the uppermost layers, when acquired, processed, and interpreted with care. Shear
wave velocity is theoretically proportional to shear modulus (rigidity) through the square
root of rigidity over density. However, care must be taken as different volumes of soils are
considered for the geotechnical and geophysical methods. The method is well used for mapping the bedrock surface (see the case in Chapter 45), weak zones (see the cases in Chapters
29 and 34), voids, as well as faults and fractured zones.
11.2
FUNDAMENTAL PRINCIPLES AND BASIC PHYSICS
Surface waves propagate along the surface, and each wavelength can be viewed as if it is tied
to the surface (Figure 11.1). The longer the wavelengths, the deeper the penetration into the
subsurface. This implies that the energy decreases with depth.
Seismic surface waves are dispersive when velocities vary with depth. This means that as
longer wavelengths reach greater depths, their particle motion is affected by the typically higher
velocities at those depths and, as a result, propagate faster than the shorter wavelengths.
For investigations with surface waves, the final product is shear wave velocity (Vs) estimates with depth, Vs(z). However, it is also possible to map relative changes over a surface,
e.g., from soil compaction, by looking only at the relative change of the dispersion curve
at certain frequencies. The basic assumption for the investigations of Vs is a predominantly
horizontal layering with near-horizontal surfaces (a flat layer cake earth). For the target
depth of an investigation, the following needs to be considered:
1
2
3
Maximum investigation depth is ~1/2 longest wavelength (λ) (Rix and Leipski 1991).
The longest λ = (surface-wave velocity at the lowest frequency)/(lowest frequency).
However, maximum investigation depth can vary between 1/2 and 1/3 λ depending on
factors, such as site velocities, expert’s judgement, etc.
The attenuation depends on the depth and the seismic source energy (as well as other
variables), and, generally, investigation depths from an active (i.e., human-controlled)
source can vary considerably from 15, 20, to 30 m and more.
Resolution decreases with depth due to the averaging effect of the longer waves that
reach greater depths.
DOI: 10.1201/9781003184676-11
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Engineering Geophysics
Figure 11.1 Illustration of surface waves in a layered earth where velocity is increasing with depth.
Seismic surface waves propagate along the surface, similar to ocean waves, but are a result
of interference at the surface of two types of seismic waves: compressional and shear waves
(with corresponding media velocities Vp and Vs) after converting and/or reflecting. Surfacewave’s dispersive properties can be estimated on a dispersion-curve image, which can be
viewed as a remapped version of a decomposed assembly of seismograms (2D Fourier transform). A seismic shot record containing recorded time signals from many receivers (e.g., 24
and more) is transformed to frequency domain and imaged with phase velocity as a function
of frequency with the energy distribution shown by a colour scale. Dispersion-curve points
are picked along the maximum energy trend observed toward the lower left corner of the
image (Figure 11.2). Velocity over frequency pairs is used to establish the corresponding
wavelengths (λ = velocity/frequency), and half of the wavelengths represent the approximate
depth (Table 11.1).
The results from the dispersion-curve points are used as a starting model for inversion.
Using velocities representing the approximately estimated depths, an approximate Vs(z)
depth model can be obtained. The calculated dispersion curve is usually notably different
from the observed data points. Based on that difference, the inversion algorithm calculates
Vs model corrections, which are applied to the initial (approximate) depth model, and a
new model is created. The difference between the data calculated from the new model and
observed data is typically smaller and smaller with each sequence of such steps, aka iteration. The inversion can stop when a reasonable fit between the observed and calculated dispersion-curve points is reached. Then it can be assumed that the final Vs(z) model provides a
reasonable representation of the subsurface (Figure 11.3).
An alternative inversion approach can be to use a stochastic inversion (Socco et al. 2009;
Wathelet 2008) that can run thousands of times forward modelling using layers with variable
thickness but usually limited in number (e.g., 3–4). Typically, a large number of models can
Surface wave methods
119
Figure 11.2 Dispersion-curve points estimated on a dispersion-curve image with arrows indicating
phase velocities values of ~387, 289, 230, 192, and 182 m/s at ~13.3, 20.5, 30.6, 40.2, and
60.9 Hz.
Table 11.1 Five of the dispersion-curve points estimated from Figure 11.2 and their corresponding
wavelength and approximate depth estimations.
Frequency (1/s)
Vph (m/s)
Wavelength (m)
~Depth (m)
13.3
20.5
30.6
40.2
60.9
387
289
230
192
182
29.11
14.09
7.51
4.77
2.99
14.56
7.04
3.75
2.39
1.50
have dispersion curves that match well the observed dispersion curve and can be assembled
to form a “cloud” of solutions. Then a statistical approach can be used to select a representative model from that “cloud”.
The spatial location of each 1D Vs(z) inversion result is assigned to the middle of the seismic spread. By assembling multiple 1D Vs(z) results using various interpolation algorithms,
2D sections or 3D volumes can be assembled. A laterally constrained inversion (Auken and
Christiansen 2004; Socco et al. 2009) approach that uses a priori information and constraints
to provide the 2D (3D) results can be applied as well.
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Engineering Geophysics
Figure 11.3 Inversion for Vs(z) using estimated dispersion-curve points (black dots). The thin bar chart
represents an approximate initial 10-layer model obtained from the picked dispersioncurve points, and it has a different calculated dispersion curve (dashed thin line). The thick
bar chart represents the fnal Vs(z) model, which has a calculated dispersion curve that
overlays (matches well) over the observed dispersion-curve (thick line curve) points.
11.3
PROPERTIES RELATED TO DIFFERENT LITHOLOGIES
OR TARGETS FOR THIS METHOD
Soft material, such as soils, are associated with lower Vs and shallower investigation depths. Hard
material, such as rock, is associated with higher Vs and greater investigation depths. Cracks and
fractures can decrease Vs, while built-up stress above voids can lead to increased Vs. Moisture can
either decrease Vs when it acts as a lubricant and decreases bond between particles, e.g., in sandy
material, or it can increase Vs when it increases forces between particles, e.g., expanding clay.
11.4
•
•
•
•
LIMITATIONS
Non-horizontal layering beneath the surface, e.g., >~30°.
Topography irregularities and/or subsurface heterogeneities with a size comparable to
the observed wavelengths and targeted investigation depths.
Limiting to surface-wave analysis can be the inability to observe (e.g., on dispersioncurve images) and correctly interpret the fundamental mode of the surface wave, which
is located in the lower left corner of a dispersion-curve image.
Surface-wave analysis investigation depths can be limited by source-unfavourable surface
conditions, e.g., meaning that a limited amount of energy is generated by low site velocities. In which case, attenuation reduces the signal strength already at shallow levels.
Surface wave methods
11.5
121
RESOLUTION AND UNCERTAINTY
Resolution and uncertainty are very challenging topics due to the non-linear nature of surface-wave physics and the corresponding necessary numerous assumptions during seismic
data acquisition and processing.
Horizontal resolution
•
•
Formal: spread size.
Practical: 0.35–0.25 × spread size (Ivanov et al. 2006; Miller et al. 1999).
Vertical resolution:
•
•
•
Layer thicknesses: These increase with depth (e.g., by 25%) to meet the natural averaging occurring with longer wavelengths propagations.
Number of layers: For example, 10 in Figure 11.3 (i.e., to maximum depth, which is
~0.5 maximum wavelength).
When using few layers (3–4) variable thickness stochastic inversion can provide additional insights (see prior discussions and Section 1.4).
Ambiguities (non-uniqueness) can be due to:
•
•
•
Dispersion-curve imaging non-uniqueness.
General dispersion-curve inversion non-uniqueness (see prior discussions on inversion),
which is a result of the non-linearity of the physics related to the surface waves (Foti
et al. 2015; Ivanov et al. 2020)
Other reasons related to processing.
11.6
ACQUISITION REQUIREMENTS
11.6.1
Instrumentation
In order to conduct a surface wave survey, a seismic source is needed. It may be an active
source (controlled by seismic data acquisition specialists) or a passive source (uncontrolled
source, such as nature, industry, etc.). Furthermore, if an active source is applied, a trigger
cable can be needed to connect the active source to the seismograph. As well, a radio trigger or GPS that is synchronised with the source and continuously recording receivers can
be used. The longer wavelengths included in the surface wave analyses requires vertical
low-frequency receivers (e.g., 4.5 Hz are a good trade-off between need for low frequencies
and cost) with cables to connect them to a seismograph. A seismograph must either have
an internal computer or a cable connecting it to an external computer. A personal computer
(e.g., a ruggedised laptop) paired with the manufacturer’s software is used to control the
seismograph.
In order to implement the multichannel analysis of surface waves (MASW) method
(Miller et al. 1999), at least 12 linearly aligned, preferably equally spaced receivers (aka
spread) with a linearly aligned source away from the spread for obtaining a single 1D Vs
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result is required. Optimised spread size and active-source offset are necessary to avoid nearfield and far-field effects (Park et al. 1999).
11.6.2
Choosing the spread size
The spread size should be at least the size of the wanted investigation depth, but a larger
spread is recommended. If during processing, a shorter optimal spread is identified, then
unnecessary or undesired traces can be removed/omitted before further analysis. This is a
better situation than if a larger spread turns out to be needed. An enlarged spread may be
needed, e.g., if there is interference by higher modes, in order to be able to separate modes
on dispersion-curve images.
11.6.3
Choosing the source offset (active source)
For optimum source offset from the receiver spread, tests with different source offsets
paired with corresponding dispersion-curve images (Ivanov et al. 2008) are recommended as a minimum. For example, the fundamental mode on the 1 m image (Figure
11.4a) lacks low frequencies (yellow dashed circle) due to near-field effect and has
higher frequencies (white circle) in comparison to the 16 m image (Figure 11.4b),
which has low frequencies (white circle) and fewer high frequencies (yellow dashed
circle) due to fundamental mode attenuation at greater offsets. Notice that there are
high frequencies on the image, but they are not from the fundamental mode. For that
reason, checking for high frequencies needs to be done on dispersion-curve images,
not on raw data.
11.6.4
Receiver spacing
The receiver spacing, dx, can impact the minimum investigation depth. A known rule of
thumb is that dx approximates the minimum depth that can be resolved since it approximates half of the minimum wavelength recorded. Still, with the MASW method’s ability to confidently track fundamental mode to higher frequencies that correspond to
shorter wavelengths (λs), such a rule of thumb may be unnecessarily restrictive. For
example, Ivanov et al. (2008) finds that the minimum depth resolved could be as small
as 1/6 dx.
11.6.5
Passive source setup
Optimal passive-source MASW can be applied by using linear receiver arrays (spreads)
oriented toward single-direction source(s) in conjunction with 2D receiver arrays used for
quality control and data optimisation (Morton et al. 2021), such as choosing data with singledirection source(s) over multi-direction source(s).
2D receiver arrays (e.g., circular, square, cross, etc.) can be used when directions of passive sources vary and/or are unknown. Other passive-source surface wave methods exist
(Hayashi et al. 2021; Louie 2001), which we consider to be not as practical.
Surface wave methods
123
Figure 11.4 Dispersion-curve images from a 71 m spread from seismic data acquired with the source
at (a) 1m and (b) 16 m.
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11.6.6 Acquisition requirements for maximum
investigation depths
If any of the following requirements are not met, it can limit the maximum investigation
depth:
1
2
3
4
5
Source needs to provide corresponding low frequencies. In other words, low frequencies need to be present in the signal.
Receivers must be capable of recording those low frequencies – low-enough natural/
resonant frequency, such as 4.5 Hz.
Spread size is large enough to be sensitive to the wavelengths associated with those low
frequencies.
Source offset is long enough to allow for the development of the wavelengths associated
with those low frequencies.
Space is available for at least one full receiver spread size and source offset.
11.7
PROCESSING
11.7.1
Fundamental mode
In surface wave methods, the identification of the fundamental mode in the dispersion curve
is key and depends on both the dispersion-curve image and the clarity of the fundamental
mode in the image.
Dispersion-curve imaging depends on:
1
2
3
Spread size;
Source offset (Ivanov et al. 2008); and
Dispersion-curve imaging algorithms, such as the phase shift (Park et al. 1998), the
HRLRT (Luo et al. 2008), etc.
Fundamental mode interpretation from a single dispersion-curve image that has more than
one trend can be ambiguous because of the possibility that one of the secondary trends can be:
1
2
A higher mode or
Another fundamental mode from two different subsurface velocity models, both detected
by the seismic spread.
For the latter example, this could happen if the receiver array is in a transition zone from one
geology to another. It may be possible to spot such a transition or heterogeneity in the raw
seismic records. In such cases, seismograms can be split where the influence of the lateral
transition is observed in order to evaluate vertical situations separately for the different areas.
For passive-data dispersion-curve image trends’ interpretations (if that is the leading/
selected/preferred seismic source for surface wave data acquisition), it is recommended to
be supported at least sporadically by active-data dispersion-curve image trends’ interpretations to avoid or minimise the risk of interpreting at low frequencies a higher mode as the
fundamental mode (Park et al. 2007). Accurate higher-mode identification can be significant
to help avoid earthquake site classification overestimations.
Surface wave methods
11.7.2
125
Inversion for V s
Inversion for Vs (Xia et al. 1999) requires accurate Vp, density, number of layers, and layer
thickness information. It can be non-unique due to:
1
2
3
4
Inaccurate Vp or Poisson’s ratio a priori information used during the inversion (Ivanov
et al. 2019);
Inaccurate density a priori information used during the inversion (Ivanov et al.
2016);
Inaccurate number of layers and their thicknesses trend assumptions;
Solving a math problem that is ill posed mathematically (Foti et al. 2015), which is
related to its non-linear nature, i.e., more than one solution to the math problem can be
available. Stochastic inversion, as mentioned earlier, can be a useful tool for estimating
such non-uniqueness.
11.7.3
Higher mode picking
Higher-mode picking (can be challenging), and its inclusion in the inversion can improve
both vertical resolution (by using more and thinner layers in the inversion) and numerical
stability by making the math problem “less” ill posed (Xia et al. 2003).
11.8
INTERPRETATION/MODELLING
Interpretations are conducted on 2D Vs images or 3D Vs volumes. The results can qualitatively map:
1
2
3
4
5
6
7
Bedrock (Ismail and Anderson 2007; Miller et al. 1999);
Weak zones (Ivanov et al. 2007);
Fault zones (Ivanov et al. 2006);
Fracture zones (Ivanov et al. 2006);
Voids (Ivanov et al. 2019);
Other types of anomalies (Ivanov et al. 2013);
Standard penetration test N (SPT N) correlations (Anbazhagan and Sitharam 2008).
If accurate Vp and density a priori information are available during the inversion (a very rare
case in practical applications) (Ivanov et al. 2016), it is possible to quantitatively describe:
1
2
3
2D Vp/Vs (or other derivatives, such as Poisson’s ratio, bulk modulus, etc.) image, if
quantitative 2D Vs images were obtained;
2D shear-modulus image, if quantitative 2D Vs images were obtained;
Earthquake-hazard assessment, per Table 11.2 derived from FEMA (2020, page 86), if
quantitative 2D Vs images were obtained (Figure 11.3).
Layer boundaries and/or anomalies can be interpreted by high Vs gradients. Other 2D info
from above might also be useful. The Vs image and gradients may not overlap with geologic
units of soils. For example, there can be a layer that has variable Vs, Vp, and density properties with space.
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Engineering Geophysics
Table 11.2 Site classifcation for seismic design.
Vs (m/s)
Class
A
B
BC
C
CD
D
DE
E
Hard rock
Medium rock
Soft rock
Very dense sand or hard clay
Dense sand or very stiff clay
Medium dense sand or stiff clay
Loose sand or medium stiff clay
Very loose sand or soft clay
1500 < Vs
900 < Vs ≤ 1500
630 < Vs ≤ 900
435 < Vs ≤ 630
300 < Vs ≤ 435
210 < Vs ≤ 300
150 < Vs ≤ 210
Vs ≤ 150
REFERENCES
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region: A case study. Journal of Environmental and Engineering Geophysics 13(2):69–84. https://
doi.org/doi:10.2113/JEEG13.2.69
Auken, E., and A. V. Christiansen. 2004. Layered and laterally constrained 2D inversion of resistivity
data. Geophysics 69(3):752–761. https://doi.org/10.1190/1.1759461
FEMA. 2020. Site classification procedure for seismic design. In NEHRP Recommended Seismic
Provisions for New Buildings and Other Structures (FEMA P-2082-1), ed. B. S. S. C. o. t. N. I.
o. B. Sciences, Vol. 1. Building Seismic Safety Council, pp. 86–88. www.fema.gov/sites/default/
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Foti, S., F. Hollender, F. Garofalo, et al. 2018. Guidelines for the good practice of surface wave analysis: A product of the InterPACIFIC project. Bulletin of Earthquake Engineering 16(6):2367–2420.
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Foti, S., C. G. Lai, G. J. Rix, and C. Strobbia. 2015. Surface Wave Methods for Near-Surface Site
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Hayashi, K., M. Craig, S. Tan, et al. 2021. Common-midpoint spatial autocorrelation analysis of seismic
ambient noise obtained from a spatially unaliased sensor distribution. Geophysics 86(4):EN51–EN62.
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Ismail, A., and N. Anderson. 2007. Near-surface characterization of a geotechnical site in north-east
Missouri using shear-wave velocity measurements. Near Surface Geophysics 5(5):331–336. <Go to
ISI>://000249983600005
Ivanov, J., B. Leitner, W. T. Shefchik, T. J. Schwenk, and S. L. Peterie. 2013. Evaluating hazards at
salt cavern sites using multichannel analysis of surface waves. The Leading Edge 32(3):289–305.
https://library.seg.org/doi/abs/10.1190/tle32030298.1
Ivanov, J., R. D. Miller, J. B. Dunbar, J. W. Lane, and S. Smullen. 2007. Interrogating levees in Texas,
New Mexico, and New Orleans using various seismic methods. Symposium on the Application of
Geophysics to Engineering and Environmental Problems 20(1):69–81. https://library.seg.org/doi/
abs/10.4133/1.2924728
Ivanov, J., R. D. Miller, A. M. Hoch, S. L. Peterie, and S. Morton. 2019. Surface wave analysis sensitivity to a-priori information assumptions. SEG Technical Program Expanded Abstracts
2019(1):5030–5034. https://doi.org/10.1190/segam2019-3216657.1
Ivanov, J., R. D. Miller, A. M. Hoch, S. L. Peterie, S. Morton, and D. Borisov. 2020. A unique approach
for estimating surface-wave instability and nonuniqueness. SEG Technical Program Expanded
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Ivanov, J., R. D. Miller, P. Lacombe, C. D. Johnson, and J. W. Lane. 2006. Delineating a shallow fault
zone and dipping bedrock strata using multichannal analysis of surface waves with a land streamer.
Geophysics 71(5):A39–A42. https://library.seg.org/doi/abs/10.1190/1.2227521
Ivanov, J., R. D. Miller, S. Morton, and S. L. Peterie. 2019. Detecting and delineating voids and mines
using surface-wave methods in Southeastern Kansas. In Fifth International Conference on Engineering Geophysics, AL AIN, UAE, 21–24 October 2019, ed. H. Baker and R. Miller. Houston, TX:
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0120000098
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bedrock. The Leading Edge 18:1392–1396. https://doi.org/10.1190/1.1438226
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Park, C. B., R. D. Miller, and J. Xia. 1999. Multichannel analysis of surface waves. Geophysics
64(3):800–808. https://library.seg.org/doi/abs/10.1190/1.1444590
Park, C. B., R. D. Miller, J. H. Xia, and J. Ivanov. 2007. Multichannel analysis of surface waves
(MASW) – active and passive methods. The Leading Edge 26(1):60–64. https://library.seg.org/doi/
abs/10.1190/1.2431832
Rix, G. J., and A. E. Leipski. 1991. Accuracy accuracy and resolution of surface wave inversion. In
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(Geotechnical Special Publication). Reston, VA: American Society of Civil Engineers, pp. 17–23.
Socco, L. V., D. Boiero, S. Foti, and R. Wisén. 2009. Laterally constrained inversion of ground roll
from seismic reflection records. Geophysics 74(6):G35–G45. https://doi.org/10.1190/1.3223636
Socco, L. V., S. Foti, and D. Boiero. 2010. Surface-wave analysis for building near-surface velocity
models – Established approaches and new perspectives. Geophysics 75(5):75A83–75A102. https://
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Wathelet, M. 2008. An improved neighborhood algorithm: Parameter conditions and dynamic scaling.
Geophysical Research Letters 35(9). https://doi.org/https://doi.org/10.1029/2008GL033256
Xia, J. H., R. D. Miller, and C. B. Park. 1999. Estimation of near-surface shear-wave velocity by inversion
of Rayleigh waves. Geophysics 64(3):691–700. https://library.seg.org/doi/abs/10.1190/1.1444578
Xia, J. H., R. D. Miller, C. B. Park, and G. Tian. 2003. Inversion of high frequency surface waves with
fundamental and higher modes. Journal of Applied Geophysics 52:45–57.
Chapter 12
Case: mapping potential unexploded
ordnance (UXO)
Joanna Hamlyn
12.1
BACKGROUND AND OBJECTIVE
A geophysical survey was commissioned along a proposed sewer route to identify any unexploded ordnance (UXO). The client required information on the presence of any metallic
material that might indicate the presence of an UXO. The 10 m wide swath requested by the
client was extended where possible to provide greater coverage.
12.2
SURVEY PLANNING AND CONSIDERATIONS
The survey area comprises pastoral farmland crossed by numerous hedges and fields. Most
hedges coincided with metal fences, and metal obstructions such as cattle feeders were present on the site. Bedrock geology is largely homogeneous. The location of surface metal was
noted and transposed onto the site plan to allow cross-correlation before the final interpretation of the data. Where surface metals dominate the data, it is impossible to distinguish it
from any potential subsurface UXO. Therefore, it is important to clear survey areas prior to
data acquisition.
An integrated survey approach combining magnetometry and time-domain electromagnetics (TDEM) to map the presence of metal content in the shallow subsurface was selected.
Whilst magnetometry is more likely to observe ferrous objects, it is important to include
time domain electromagnetics as not all UXO are ferrous. Both data sets can also provide
approximate depths to potential UXO to aid intrusive investigations.
An orthogonal survey grid comprising 1 m spaced parallel survey traverses was established across an area specified by the client. The total field magnetic data were acquired
with a Geometrics G-858 at a sampling rate of 10 Hz. The TDEM data were acquired using
an EM61-MK2 collected on an odometer wheel to obtain a data point every 18 cm. Both
instruments were walked along each survey traverse under the control of an RTK GPS. The
magnetic data were acquired first, followed by the TDEM data.
12.3
RESULTS AND CONCLUSIONS
A subset of the results is shown in Figure 12.1. The results of the magnetic and electromagnetic surveys are presented as colour contoured plots of the ETMF (Earth’s total magnetic
field) (nT), the analytic signal of the ETMF, and the electromagnetic instrument response
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Figure 12.1 Multidisciplined geophysical survey for the detecting UXO. (a) Total feld magnetometry
data. (b) Analytic signal derived from total feld magnetometry data. (c) Time domain electromagnetic data.
Mapping potential unexploded ordnance (UXO)
131
(mV). Data interpretation is based on the principle that metal objects within the range of
the instrument (<3 m below ground level [bgl]) produce a relative increase in the amplitude
of the signal response, though surface metal objects are likely to have masked signals from
any buried metallic or ferrous objects/structures beneath. Numerous buried metallic objects
were identified, and a representative subset of these was investigated intrusively by qualified
explosive ordnance disposal (EOD) engineers. Depth calculations of the analytic signal and
the time domain electromagnetic data suggest that the features identified are between 1 m
and 1.5 m bgl.
The large monopole observed in the magnetic data in the eastern part of the site is not
observed in the electromagnetic data. This feature may be too deep (over 3 m bgl) to be
observed in the electromagnetic data, or, more likely, the monopole is caused by burning.
Where the ground is heated to high temperatures (past the Curie point), ferrous minerals in
the soils can realign and cause local deviations from the observed magnetic field. On agricultural land, farmers sometimes have areas where they burn waste, or if large trees have
blocked pathways or tracks, they could be cut down, and the remaining stump burned.
Chapter 13
Case: geophysical investigation
to delineate landfill
Joanna Hamlyn
13.1
BACKGROUND AND OBJECTIVE
A highway improvement project included a proposal for a new dual carriageway to link two
existing roads. The current land use is agricultural; however, records suggest the presence of
historical landfills along the planned alignment. Magnetometry and frequency domain electromagnetic (EM) surveys were acquired to confirm the location and extent of the landfill.
13.2
SURVEY PLANNING AND CONSIDERATIONS
Historical mapping indicates that shallow gravels across the south of the site have been quarried, and extensive extraction has taken place. The resulting pits were subsequently used for
landfills. The landfill is reported to contain extensive ferrous debris from nearby manufacturing activities. In order to map the landfill area, a multidisciplined approach including both
magnetic and electromagnetic techniques were applied: magnetic methods to map the distribution of ferrous materials and, if the landfill area contained non-ferrous metals, they would
be observed in the electromagnetic data. Therefore, the combined use of both techniques
allows for a more comprehensive understanding of the subsurface conditions.
Boreholes 100 m to the east and 300 m south of the site indicate a shallow geological
sequence of ~18 m of till overlying mudstone bedrock. Therefore, variations in the bedrock
geology are unlikely to be observed in the data and changes in the ETMF (Earth’s total magnetic field) will solely be caused by the contents of the landfill.
A Geometrics G-858 total field instrument was used to map the effect of subsurface ferrous materials on the ETMF. Total magnetic field instruments are more useful on sites where
large amplitude variation in the ETMF is expected, as extremely high magnetic gradients
may not be fully resolved by magnetic gradiometry. The instrument acquired data at 20Hz
whilst towed under dGPS control behind an all-terrain vehicle (ATV), nominally providing
data at a resolution of 0.2 m along the traverse (y-axis). For this survey, data were acquired
along 2 m spaced traverses. However, some areas were overgrown or too steep to be surveyed. At these locations, no interpretation of the extent of the landfill could be made.
An electromagnetic ground conductivity metre maps the electrical conductivity of shallow subsurface materials. Areas of increased clay, metal, and leachate content are evident as
regions of high values. In contrast, coarse-grained sediments, dry waste, and many bedrock
types appear as areas of low conductivity. The recorded EM response comprises a “Quadrature-Phase” and an “In-Phase” component. The In-Phase response is particularly sensitive
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Figure 13.1 Geophysical survey targeting a historical landfll. (a) Total feld magnetometry data.
(b) In-phase response from an electromagnetic ground conductivity survey. The white
dashed line is the extent of the landfll as suggested by a desk study.
Investigation to delineate landfill
135
to the presence of buried metal, including non-ferrous metals. The in-phase response was
measured across the site by integrating a Geophex GEM-2 instrument with the towed array
and simultaneously collecting both the EM and magnetic data at the spatial resolution at
the same time. By collecting the EM and magnetic data simultaneously, the duration of
data acquisition is significantly reduced. Theoretically, EM instruments can interfere with
ETMF. However, in practice, the effect is negligible. Before collecting any geophysical data
simultaneously, extensive testing should ensure that any additional instrumentation does not
create artificial signals.
13.3
RESULTS AND CONCLUSIONS
The combination of magnetic and electromagnetic techniques defines the extent of the landfill well and shows that it extends further to the northwest and east than suggested by the
desk study (white dashed line, Figure 13.1). The large-amplitude distortion of the ETMF
over the same area identified by the electromagnetic data suggests that much of the buried
waste is ferrous.
The use of frequency domain electromagnetics has successfully mapped the in-phase
response of the shallow materials. Specifically, the in-phase response from such a survey
effectively maps the magnetic susceptibility of the subsurface materials, which is useful
where the metallic content is high. In addition, the in-phase response has a smaller lateral
resolution to that of the magnetics and therefore produces a more precise delineation of the
edge of the landfill, most evident along the northern boundary of the landfill.
Chapter 14
Case: 3D GPR in the inner yard
at Frederiksborg Castle
Ole Frits Nielsen and Søren Wedel Nielsen
14.1
BACKGROUND AND OBJECTIVE
In the inner yard of Frederiksborg Castle, Denmark, a vertical displacement of a row of
statues (Pragtgalleriet) has been observed. This initiated a survey of the underground conditions. It is known that the castle is built on the remains of an older castle from the Middle
Ages. Therefore, 3D GPR was applied to map underground structures such as foundations,
cavities, and tunnels.
14.2
SURVEY PLANNING AND CONSIDERATIONS
The yard is surrounded by tall buildings of the castle, and it was expected that only a poor
GPS signal could be achieved. Therefore, the positioning had to be made using measuring
tape supported by local positioning features in the data, e.g., the pattern in the cobblestone
pavement as described here.
There is a need for a detailed mapping with a high lateral resolution. To achieve this, a 3D
GPR was applied. The GPR is a TerraVision from GSSI with a centre frequency of 400 MHz
and 14 individual channels with a crossline separation of 12 cm, giving a total width of the
antenna array of 156 cm. It was possible to map most of the inner yard except for minor areas
where obstacles placed on the ground made it impossible to pull the GPR antennas across the
ground. The fieldwork was finished by a crew of 2 in 5 hours.
14.3
RESULTS AND CONCLUSIONS
Visualising the data as horizontal slices at different depth intervals provides a strong basis
for a detailed interpretation of underground structures. One of the main advantages of horizontal slices compared to vertical cross sections is that they provide an overview of the
complete site. Cavities and buried objects such as utilities or foundations show on the map as
coherent structures. This saves time as the entire area can be interpreted from a low number
of maps. Thus, in this case, the interpretations have been made primarily from the horizontal
slices supported by cross sections in selected areas.
The data coverage is shown in Figure 14.1, which shows an aerial photo of the castle
together with a horizontal slice through the 3D GPR data. The slice is extracted 11 cm
bgl with a thickness of only 5 cm. The detailed pattern from the cobblestone pavement
is clearly seen in the data indicating the structural resolution in the data. These shallow,
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Figure 14.1 Aerial photo of the inner yard at Frederiksborg Castle shown with the GPR data. A horizontal slice (5 cm thick) of the 3D GPR data at a depth of 11 cm bgl is shown in greyscale.
The magnifed cutouts (red frame) show the aerial photo of the brick stone pattern (left)
and the GPR data for comparison (right).
detailed results can be utilised to verify or even enhance the accuracy of the positions during
the data processing.
At a depth of 0.5 m bgl, a human-made tunnel can be seen crossing the yard (Figure 14.2a).
Also, a circular well is seen in the data. The tunnel is approximately 1.5 m wide and has some
3D GPR at Frederiksborg Castle
139
Figure 14.2 (a) Horizontal slice of 5 cm thickness through GPR profles at approximately 0.5 m depth.
The main interpreted features are labelled. (b) Horizontal slice of 50 cm thickness through
GPR profles at approximately 1.0 m depth.The main interpreted features are labelled.The
solid line represents part of the old foundation known from excavations.
turns and a side branch, which apparently is collapsed as the signal becomes weaker along the
collapsed tunnel.
At 1.0 m bgl, the tunnel is still visible, but also several pipes, which are interpreted as
drainage or sewer pipes, can be seen, Figure 14.2b. Centrally, a dark area is seen, which
indicates a high amplitude level. This area could be the old foundation of the former castle.
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Note that the horizontal slice in Figure 14.2b is 0.5 m thick and thus the averaged amplitude of approximately two wavelengths. The result is – compared to a thin slice – a calmer
picture showing mainly the “big” structures with less noise. On the other hand, detail and the
ability to resolve small or thin structures are lost. This can be seen from, e.g., the horizontal
slice in Figure 14.1, which is only 5 cm thick but able to resolve the detailed pattern in the
cobblestones.
The conclusion of the GPR survey is that it was possible to get detailed structural information on the underground down to a maximum depth of approximately 2.5 m. A human-made
tunnel was found together with several pipes and wells. Also, a large part of the central area
showed a fairly sharp delineated area with strong and scattered reflections indicating heterogenic conditions. A plausible interpretation of this area is that it constitutes the foundation
area of the former castle.
ACKNOWLEDGEMENT
The authors wish to thank Slots-og Kulturstyrelsen for making this survey possible and for
allowing the results to be published.
Chapter 15
Case: mapping of utilities
when developing at an old coal
storage facility
Peter Thomsen
15.1
BACKGROUND AND OBJECTIVE
The harbour area in Aarhus is under continuous transformation, where historical industrial
areas are converted into residential and office areas. In the late 1960s, an area used for coal
storage (Figure 15.1a) was converted into a parking lot (Figure 15.1b). Now, to address
further development, a large office building is planned. Prior to the geotechnical investigation for this building, non-invasive geophysical methods were proposed for mapping the
remains of the coal storage below the present parking lot. As seen in the Figure 15.1a, the
coal was distributed in subareas, separated by concrete walls. The geophysical investigation
is expected to reveal remnants of the walls, which is crucial knowledge when planning the
foundation of future buildings.
15.2
SURVEY
The presence of residual salt in the fill material that covers the old coal storage facility limits
the use of some EM methods, e.g., GPR. For that reason, the ground conductivity meter
(GCM) was chosen. GCM offers resistivity data, regardless of whether the environment is
resistive or conductive, and applying the GCM sensor enables a dense spatial coverage to be
obtained, also including paved areas. To optimise data quality, the sensor was installed on
a non-metallic sledge. This secured constant and limited distance to the ground in order to
minimise pitch and roll. Because of the intensive use of the area in the daytime, the survey
was planned to take place during 2 h at night.
For the specific survey, the DUALEM421s sensor was applied. The DUALEM421s has
dual geometry for transmitter-receiver separations of 1, 2, and 4 m (HCP1, PRP1.1, HCP2,
PRP2.1, HCP4, PRP4.1) where HCP indicates a horizontal co-planar transmitter-receiver
configuration and PRP a perpendicular configuration. The DUALEM421s operates at 9 kHz
and has a depth penetration of 5–7 m.
The collected data are processed using the Aarhus Workbench. The individual channels
are shifted laterally based on the lateral sensitivity of each channel. Finally, processed data
are inverted using the 1D spatial constrained inversion (SCI) approach.
15.3
RESULTS
In Figure 15.1c and d, results are shown as inverted resistivity in the depth intervals 0.5 to
1.0 m and 2.0 to 2.5 m. Since data are densely collected, they are displayed by point themes
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Figure 15.1 (a) Coal storage use until the late 1960s. (b) Parking lot (2015). (c) and (d): Results as
resistivity in the depth interval 0.5 to 1.0 m (c) and 2.0 to 2.5 m (d).
Case: Developing at an old coal storage facility
143
avoiding any gridding effect. At the resistivity map in the depth interval 0.5 to 1.0 m, known
utilities are shown with solid lines. These utilities include pipes for water, district heating,
and communication cables. In the resistivity map, these installations show up as conductive
anomalies. Black dashed lines outline anomalies caused by utilities other than the pipes. In
the map to the right, the effect from the known installations is, in general, less visible. However, the installations other than pipes remain visible, identified as areas of low resistivity.
The location, geometry, and orientations of these structures correlate nicely with what is
expected from the historical photos from the area and are interpreted as the remains of the
coal storages facilities. In the depth interval 2.0 to 2.5 m, the resistivity is, in general, lowest
closest to the present coast because of the influence from saltwater intrusion.
15.4
CONCLUSION
A detailed geophysical survey using the DUALEM421s system enabled the identification of
historical facilities now buried under a paved area. Results from this survey are used in the
planning of detailed geotechnical investigations.
Detailed pre-investigation using the DUALEM421s system has proven a cost-effective
pre-investigation tool when developing in urban areas.
Chapter 16
Case: near-surface electromagnetic
survey to support the design of
urban development plans
Peter Thomsen
16.1
BACKGROUND AND OBJECTIVE
When developing areas with bedrock in the near subsurface, knowledge of the top of bedrock is crucial for the construction. As part of pre-investigations for developing a logistic
centre approximately 5 km southwest of Norrköping, soil conditions were investigated. The
geological setting at the Klinga site is described as clay till on top of bedrock. Borehole
information indicates a relatively sharp boundary dipping towards the northeast. In order to
obtain spatial information of the top of bedrock, a geophysical investigation was selected.
The survey focused on obtaining a spatial mapping of the top of the bedrock in the area
by correlating the geophysical results and borehole information. The survey results were
included as part of the background information for planning the exact locations of the
buildings.
16.2
PLANNING CONSIDERATIONS
In the survey planning, several parameters were considered. First, the investigation should
deliver a reliable result about the depth to the bedrock, and at the same time, the survey
should cover the entire area (23 ha) in sufficient detail.
Traditionally, seismic methods are applied to map the depth to bedrock since acoustic
velocity changes rapidly at the boundary between sediments and hard rock. The disadvantage
of using seismic methods is that the data acquisition is time-consuming and therefore expensive. Often a single section or multiple sections with sparse coverage are collected. In order to
enable full spatial coverage of the development area, a combination of the time domain pulled
tTEM (towed transient electromagnetic) system providing information to 50–70 m depth and
the frequency domain ground conductivity meter (CGM), the DUALEM421 system, providing higher resolution but at a lower target depth of 5–10 m depth, were chosen.
The result from the tTEM survey is a resistivity model of the area. Top bedrock shows up
as a highly resistive layer, overlain by sediment of lower resistivity correlating well with the
clay till observed in boreholes.
The tTEM system setup is shown in Figure 16.1. The size of the transmitter coil is 2×4 m,
and the current is transmitted in a dual-moment setup. The receiver coil is pulled 7 m behind
the transmitter coil. The entire setup is pulled on a non-metallic sledge behind an ATV, with
a speed of 10–15 km/h (Auken et al. 2018; Maurya et al. 2020).
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Figure 16.1 tTEM setup used at Klinga.
16.3
KLINGA
The survey area at Klinga is 23 ha and is currently used for cropping (see Figure 16.1). The
area slopes northeast from a forested area to the southwest. Bedrock appears at the surface
in the forest, dipping toward the northeast.
Prior to the tTEM survey, geotechnical boreholes were drilled, and in order to map areas
where the depth to bedrock is shallow, a GCM investigation was conducted. Both tTEM and
GCM data were collected within 1 day.
A total of 25 line-km tTEM were collected, processed and inverted. Data were collected
using an approximate distance between lines of 30 m. Data were processed and inverted in
the Aarhus Workbench software package using a module specially designed for handling
tTEM data. After filtering and removing noisy and disturbed data, the data set was inverted
using a smooth spatially constrained inversion scheme (Vignoli et al. 2015). Inversion is
applied to convert data from measured responses to a subsurface resistivity distribution
model. Results are displayed as resistivity sections, mean resistivity maps, and 3D visualisations for visual inspection.
Figure 16.2 shows two model sections through the area. The inverted tTEM models and
the inverted GCM models are shown jointly with the available geotechnical boreholes. It
is seen that there is a strong correlation between high resistivities and bedrock indicated
by red colours in the boreholes. Correlation of resistivities from both the tTEM and the
GCM survey with borehole information enables spatial interpretation of top bedrock in
the entire area.
Near-surface electromagnetic survey
147
Figure 16.2 Overview of survey and results from the geophysical survey a Klinga.The model sections
are shown from southwest to the northeast. Orange lines in the overview map shows
the location of the cross sections shown. Cross sections include resistivity from tTEM as
background resistivity from GCM as thin bars. On top, boreholes are shown together with
the interpreted top bedrock based on both the resistivity and the boreholes. Boreholes
have ID labels above them. At the boreholes, yellow colours indicate sediment, and red
colours indicate bedrock.
16.4
CONCLUSIONS
The survey at Klinga illustrates electromagnetic methods’ ability to spatially map the interface between sediments and hard rock as an alternative to a line-based seismic survey. To
obtain the best resolution at Klinga, the towed time domain tTEM system and the ground
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conductivity meter, DUALEM421, were combined. The interface shows up as the boundary
between a low-resistivity layer covering the highly resistive bedrock. Combined with information from boreholes, the interface was interpreted and extrapolated from the boreholes
based on the tTEM and GCM survey.
REFERENCES
Auken, E., N. Foged, J. J. Larsen, et al. 2018. tTEM – A towed transient electromagnetic system for
detailed 3D imaging of the top 70 m of the subsurface. Geophysics:E13–E22.
Maurya, P. K., A. V. Christiansen, J. B. Pedersen, and E. Auken. 2020. High resolution 3D subsurface
mapping using a towed transient electromagnetic system – tTEM: Case studies. Near Surface Geophysics 18:16.
Vignoli, G., G. Fiandaca, A. V. Christiansen, C. Kirkegaard, and E. Auken. 2015. Sharp spatially constrained inversion with applications to transient electromagnetic data. Geophysical Prospecting
63(1):243–255.
Chapter 17
Case: archaeological Investigation to
identify a Romano-British farmstead
using magnetic gradiometry
Joanna Hamlyn
17.1
BACKGROUND AND OBJECTIVE
A proposed solar farm required a geophysical survey to identify subsurface features potentially indicating buried archaeology within the site boundary. The survey area comprised 300
ha of open farmland. The survey results were to be used to determine the requirement for and
location of intrusive archaeological investigation.
17.2
SURVEY PLANNING AND CONSIDERATIONS
As the potential archaeological features are likely to be shallow and are expected to cause
low-amplitude perturbations to the ETMF (Earth’s total magnetic field), the magnetic survey
was acquired using fluxgate sensors configured as gradiometers. Due to the size of the site,
8 fluxgate gradiometers were installed at a 0.5 m separation on a specialist cart, creating a
3.5 m wide swath of data. The cart was subsequently towed at speeds of <15 km/h under the
positional control of an RTK GPS, and the data were acquired at a rate of 100 Hz, nominally
providing data at a resolution of 0.05 m along the traverse (y-axis) and 0.5 m between each
traverse (x-axis). This allowed the total area to be surveyed within a commercially viable
time frame of approximately ~15 days.
17.3
RESULTS, INTERPRETATION, AND CONCLUSIONS
The survey acquired vertical gradient of the ETMF in nT/m and normalised around zero. A
subset of the acquired data is shown in Figure 17.1 on a colour scale of ±4 nT, and it shows
subtle responses from both geological and archaeological features. The survey area shown
in Figure 17.1 was covered by long grass, with no visible evidence on the site or in satellite
aerial photos that the structure was present in the subsurface.
The prominent NNE-SSW lineation of positive anomalies is generated by sub-vertical
joints within the underlying bedrock. This is most evident in the north, where the overlying
soil is thinnest along a small topographic ridge. Towards the south, there is a valley with
thicker subsoil, and the influence of the bedrock is diminished.
A small Romano-British farmstead is found to lie in a sub-rectangular ditched enclosure.
The ditches are observed as positive magnetic anomalies. The enclosure revealed is defined
by 2 or, locally, 3 parallel ditches. There is an entrance on the south side with a funnel-shaped
arrangement of external ditches. Further ditched fields are located up to 150 m west and 100 m
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Figure 17.1 Subset of magnetic gradiometry data over an archaeological target.
east of the main enclosure. In the south-west of the figure, subtle, narrow, negative magnetic
anomalies, aligned NE-SW and NW-SE, are seen; these mark the location of former field
boundaries. In comparison, the very high amplitude magnetic anomalies along several of the
current field boundaries originate from the presence of ferrous fencing materials.
Based on the survey results, archaeologists were able to target the Romano-British farmstead in their intrusive campaign.
Chapter 18
Case: integrated geophysical survey
to locate buried structures
Joanna Hamlyn
18.1
BACKGROUND AND OBJECTIVE
A utility company commissioned a geophysical survey to confirm the suspected location of a
mine shaft thought to be located close to a proposed pipeline. There is a single historical coal
mine shaft recorded in the area. However, there is little information on the shaft type or its
remediation following its decommissioning in the 1960s. In addition, the surrounding area has
been industrialised, and there may be additional historical structures within the area of interest.
The area’s underlying geology is the Pennine Coal Measure Formation comprising mudstone, siltstone, and sandstone to the south-east of the site and sandstone to the north-west. To
confirm the location of the mine shaft, an integrated survey approach comprising frequency
domain electromagnetic ground conductivity (EM), magnetometry, and microgravimetry was
applied. The EM maps shallow variations in conductivity across the site, which may be caused
by slag and mine waste or by altered ground conditions related to the location of the shaft. The
magnetometry detects distortions in the ETMF (Earth’s total magnetic field) associated with
suspected buried ferrous features and potential capping structures. The microgravity survey
detects localised density changes that may be associated with voids or loose backfill.
18.2
SURVEY PLANNING AND CONSIDERATIONS
Data were acquired over a 30×30 m grid covering the area of the suspected mine shaft. The
magnetic and electromagnetic data were acquired along 1 m spaced traverses. The microgravity data were acquired on a staggered 2 m grid. However, where a surface depression
was observed, the stations were reduced to a 1 m spacing, and at the edge of the grid, the
station spacing was expanded to 3 m.
The magnetic data were acquired by walking a Geometrics G-858TM magnetometer unidirectionally along 1 m spaced traverses. Subsequently, the EM data was acquired by walking a Geophex GEM-2TM instrument unidirectionally over the same 1 m spaced traverses.
Both data sets were acquired using an EGNOS dGPS system. The micro-gravity data was
acquired using a Scintrex CG-5TM instrument. The Scintrex CG-5TM uses statistical averaging and filtering of readings taken every second over a fixed period (typically 2–3 min),
thereby significantly reducing low-level noise from wind, seismic activity, and nearby roadways. For each gravity station, the location and elevation of the gravity meter were recorded
using a total station and a target prism placed on the gravity meter to a precision better than
0.5 mm. At each survey location, at least two readings were taken.
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A busy road was located immediately adjacent to the area of interest. The gravity survey
was acquired at night to avoid surface vibration from passing cars.
18.3
RESULTS AND CONCLUSIONS
Anomaly A (see Figure 18.1) is thought to indicate the location of the mine shaft. It is characterised by responses from the ground conductivity, magnetic and micro-gravity surveys,
and situated at or close to the surface depression.
Figure 18.1 Multidisciplined geophysical survey over a suspected mineshaft. (a) Electromagnetic ground
conductivity. (b) Total feld magnetometry. (c) Micro-gravity.
Case: Integrated geophysical survey
153
The associated magnetic response (Figure 18.1b) is an isolated monopolar anomaly
approximately 5m wide exhibiting a low amplitude. It is offset from the other geophysical
responses and the surface depression. The low amplitude and the broad size of the response
may indicate a broad spread of shallow ferrous debris or an anomaly at depth, for example,
a capping structure or response from the shaft lining. If the latter is the case, following the
half-width rule, the causative body should be no deeper than 2.5 m.
The electromagnetic ground conductivity survey reveals an isolated high response (Figure 18.1a). The anomaly is small in amplitude and lateral extent. It may indicate an increased
content of water, clay, or both near the surface or a presence of conductive material (metal)
at a greater depth. Such conductive material could be shallow/surface conductive backfill
material associated with a mine shaft structure or metallic material associated with a capping
structure at depth.
The micro-gravity plot in Figure 18.1c shows a significant gravity low, which indicates a
void within 5 m bgl. The centre of the low is slightly offset from the surface depression; this
may suggest that the depression is due to a lateral collapse within the shaft.
Chapter 19
Case: total field magnetometry
to locate buried foundations
Joanna Hamlyn
19.1
BACKGROUND AND OBJECTIVE
A total field magnetometry survey was designed to assist in the location of subsurface pile
foundations beneath an existing car park, which was due to be developed into a library and
community centre. The 0.7 ha car park historically housed storage and warehouses from the
late 1880s into the early 2000s.
Geological maps indicate that the survey area is immediately underlain by till extending
to depths over 10 m bgl (metres below ground level). Therefore, the natural geology is not
expected to influence the survey results. There is likely to be an unknown thickness of made
ground covering the site.
19.2
SURVEY PLANNING AND CONSIDERATIONS
An orthogonal survey grid comprising 1 m spaced parallel survey traverses was established
across an area specified by the client. The total field magnetic data were acquired using a
Geometrics G-858TM with a sampling rate of 10 Hz at a walking pace along each survey
traverse, under the control of an RTK GPS. The car park was cleared of vehicles and other
metallic obstructions, which would have affected the data acquired.
19.3
RESULTS AND CONCLUSIONS
Figure 19.1 shows the total field response over the surveyed area. Monopolar magnetic highs
are observed at regular spacings across the centre of the site and along the north-western
Figure 19.1 Total feld magnetometry data to locate subsurface pile foundations across a brown feld site.
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boundary. These locations are consistent with the footprints of the previous buildings
observed in past satellite photos of the area and are caused by ferrous material within
each pile foundation. The results allowed the client to efficiently clear the site for future
development.
Chapter 20
Case: utility mapping with GPR
at Copenhagen harbour
Ole Frits Nielsen and Søren Wedel Nielsen
20.1
BACKGROUND AND OBJECTIVE
In connection with the repositioning of a pressurised pipe in Copenhagen, ground penetrating radar (GPR) was applied to map the existing underground structures. Maps with existing utility information showed several pipes in the area. Furthermore, ground anchors were
expected to be installed to support the sheet pile wall delimiting the harbour.
20.2
SURVEY PLANNING AND CONSIDERATIONS
The site area covers approximately 3500 m² and was mapped in 2 days using both singleand multichannel GPR by 3 people on foot. Due to the need for a detailed mapping with
a high lateral resolution, the positioning was made using RTK GPS supported by known
positioning features in the data, e.g. manhole covers in the pavement. Furthermore, there
was a need for high penetration depth as a deep-lying sewer pipe is also present in the area.
To achieve this, both single- and multichannel GPR was applied. The applied multichannel GPR is the TerraVision™ from GSSI with a centre frequency of 400 MHz and 14 individual channels with a crossline separation of 12 cm, giving a total width of the antenna
array of 156 cm. The applied single-channel GPR is a ProEX™ from MALÅ with 100 and
250 MHz shielded antennas.
20.3
RESULTS AND CONCLUSIONS
The multichannel GPR was visualised as horizontal slices at different depth intervals to provide information on utilities from the entire site. Buried utilities show in the slices as coherent structures, which make them easy to identify. Also, six profiles with single-channel GPR
were made with both 100 and 250 MHz antennas for comparison and to gather information
from greater depth. The multichannel data made it possible to make a 3D interpretation
(x, y, z) of the underground structures. Utilities and several ground anchors were mapped.
The maximum penetration depth was reached at around 40–50 ns, corresponding to approximately 2.0–2.5 m below ground level (bgl). However, applying the lower frequencies with
the single-channel GPR showed an increase in penetration depth.
Horizontal slices through the multichannel data with a thickness of 0.5 ns are presented in
Figure 20.1a. The labelled areas show data from different slices. High amplitude is shown in
white/black and low amplitude in grey. A series of ground anchors are seen along the sheet
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Figure 20.1 (a) Horizontal slice through the multichannel GPR data with an aerial photo as background. (b) Comparison of 3 different antenna frequencies along the same line (100, 250,
and 400 MHz).
pile wall along the water (area A). The pointed shape of the reflection in the horizontal slice
shows that the anchors are dipping towards the north. An example of poor geometry can be
seen in the southwestern part of the figure (area B). A pipe seems to be “shifted” at different
positions in the different antenna arrays. This is due to poor GPS data quality in this part of
Utility mapping at Copenhagen harbour
159
the data. Notice how easy it is to recognise the erroneous geometry, which is due to the high
lateral data density in the multichannel data. If the data were collected using a single channel
with a significantly higher line spacing, this would be less visible, and there would be a much
higher risk of errors in the interpretation. In the northernmost section, several pipes are seen
crossing the mapped area. Amongst others, a large pipe in the north-western part is seen to
be dipping towards the north (area C).
A comparison of the different antenna frequencies applied is given in Figure 20.1b. The
antennas are moved along the harbour quay perpendicular to several buried steel anchors,
which results in a series of hyperboles around a two-way time of t = 35 ns (approximately 2
m depth). The hyperbolas show greater detail with higher frequency. This can also be seen
from the more shallow reflections in the first 10 m of the radargram, which are caused by
buried cables. Also, a deeper-lying structure centrally in the profile is seen around t = 90 ns.
It has a relatively strong amplitude in the 100 MHz data, a somewhat weaker amplitude in
the 250 MHz, but cannot be seen in the 400 MHz. The comparison illustrates the increasing resolution with higher frequency, as well as decreasing penetration depth with higher
frequency. It should also be mentioned that the saline groundwater in the area sets an upper
limit on the penetration depth for all frequencies.
Chapter 21
Case: thickness of peat and depth to
bedrock for road construction using
ground penetrating radar
Jaana Gustafsson
21.1
BACKGROUND AND OBJECTIVE
New access roads were planned in a forested area where both peat of unknown thickness
and shallow bedrock were expected. According to existing geological maps, the bedrock
was overlain by till. The access roads were needed for the construction of an extensive wind
power park. Thus even with very low traffic intensity, the roads should be able to carry heavy
loads and have suitable dimensions horizontally and vertically for wide and long transports.
The distribution and thickness of the peat need attention when designing the road for the
needed bearing capacity. Also important at an early stage of planning is knowledge of the
levelling options, e.g., whether excavation can be sufficient or if blasting of rock is needed.
As the area was quite large, approximately 15 km of access roads in non-cleared coniferous forest terrain and with varying geology, a geophysical survey was conducted to provide
a quick overview of the site conditions prior to a more detailed geotechnical investigation.
21.2
SURVEY PLANNING AND CONSIDERATIONS
Since the expected geology in the area comprised peat, till, and shallow bedrock, and no
clearance of the coniferous forest was planned, GPR (ground penetrating radar) was the
natural choice for geophysical site investigation. GPR can be used in dense vegetation and in
hilly terrain and can be operated by a single person, offering a swift and easy investigation of
the geological conditions. Further, as none of the important geological features were conductive (e.g., clay), GPR had a high possibility of delivering good-quality data.
The coniferous forest was in parts rather compact, so the planned access roads were marked
with sticks or plastic tape prior to the GPR investigation. These markers were both used to
find the correct path while measuring and keeping track of the position of the GPR profiles if
the GNSS used for the GPR investigation failed to gather positioning data. GPR data can in
the post-processing step be adjusted to known points along the investigated profile.
The investigations were conducted with a 160 MHz GPR antenna, providing a depth of
investigation of approximately 15 m in this type of geological conditions. The choice of frequency was mainly based on the expected thickness of the peat, which was unknown but
suspected to extend quite deep. A higher-frequency antenna (450 MHz) would have been
lighter in the rough terrain but would most likely miss the base of some of the deepest peat.
The GPR investigations were trigged by a measurement wheel (which also kept track of
the profile length) and measured with a point interval of 5 cm. As the GPR equipment is fast
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in data gathering, there is no need to use a wider measurement point interval. This would
only make data harder to interpret.
The GNSS used for positioning the GPR profiles was an integrated DGPS, corrected by
EGNOS. This has an approximate accuracy of ±1–3 m at the X- and Y-coordinates. This position accuracy is quite sufficient when looking at the large-scale geological conditions in the
first stage of investigations. However, the Z-level from a DGPS is, in most cases, of very low
quality, so this was not used. Further information on positioning is given later in the case.
The terrain and the layout of the roads (how much back and forth we needed to walk) set
the limit of the investigation speed, which equivalated a walking pace. Consequently, the 10
km of data were gathered during 1½ to 2 field days. If the terrain had allowed it, the GPR
equipment would be easy to pull behind an ATV, which would have eased the acquisition
work.
21.3
RESULTS AND CONCLUSIONS
When the GPR measurements were completed, a comparably quick first interpretation of
the data was made, focusing on the peat and the bedrock. In order to aid the interpretation
process; geological maps were used as background information. Otherwise, no geological
information was available. In order to carry out this type of interpretation, two major challenges needed to be addressed:
1
2
What is the velocity of the GPR wave in peat? This can differ from, say, 35 m/ns in
really wet conditions to almost 90 m/ns in dry conditions, which will affect the depth
estimate.
How to distinguish between bedrock and till with large or numerous boulders? It can be
quite easy to identify bedrock in the data, but to set the correct depth level if the bedrock
is overlain by boulder-rich till is more challenging.
However, despite these two plausible sources of error, GPR provided an excellent overview
of the geological conditions, and the planning for geotechnical drillings could be carried out
much more efficiently. In the bedrock areas where the geophysical results were high quality
and reliable, fewer drillings were made, whereas in areas where GPR did not provide reliable
data, more dense drillings were carried out. For the peat, the number of drillings was minimised, as even one ground truthing point is enough to set the correct velocity and thereby
the depth for the peat occurrence.
When the geotechnical investigations were finalised, the results were incorporated into
the GPR data, and a second round of detailed interpretation was carried out. The results from
these are shown in Figure 21.1.
The GPR resulted in clear interpretations of both the bedrock surface and the base of the
peat (Figure 21.1c and d, respectively). The interpretations were used to build a model of
both the bedrock surface and the peat thickness for the design work of the access roads.
The results from the GPR investigations were exported as simple text files with information on X-Y position and depth below ground level to the interpreted layers. As most geotechnical work today is based on a topographical model in CAD or other design software
packages, the depth can easily be transformed to a Z-level. For correct elevation, the profile
depths must be relative to the topography of the model.
Thickness of peat and depth to bedrock
163
Figure 21.1 Site, instrument, and results. (a) Investigation area, with investigated profles marked in
red.The section shown is 4×3 km, located in northern Sweden. (b) The used GPR equipment, a MALÅ Ground Explorer system from Guideline Geo with a 160 MHz antenna.
(c) Example of bedrock interpretation (red), with borehole data in two points (marked
BH143 and BH121). The bedrock was found by soil/rock probing where the red staples
end.The bedrock is identifed at a depth from 1.5 to 11.5 m along the measured sections.
(d) Example of peat interpretation (the peat bottom is represented by the black/white
strong refection). One stick sounding was made to set the correct velocity and thereby
depth. The peat reaches approximately 200 ns as a maximum, in other words, approximately 6 m thickness.
To conclude, the thickness of peat and the depth to the bedrock were successfully resolved
with GPR and geotechnical drilling. The GPR investigation in the forest terrain with till,
peat, and shallow bedrock proved very efficient. GPR provided an excellent planning basis
for further investigations and was both time- and cost-efficient. In 3 working days, a better
understanding of the geological settings was achieved, which could be used as a basis for
the geotechnician’s further work. Moreover, with an iterative interpretation process, the final
result gave the maximum data coverage for the planning of the access roads.
Chapter 22
Case: multidisciplinary geophysical
investigation for a new railway
track in Norway
Roger Wisén, Anders Almholt, and Fredrik Olsen
22.1
BACKGROUND AND OBJECTIVE
Norwegian Railway authorities planned to construct an additional track on the railway from
Sandnes to Nærbø, Utbygging Sandnes, for which a geophysical investigation was required
as an initial survey of the ground conditions. It was essential to depict the upper 20 m. The
client needed detailed information about the depth to a solid moraine layer or to the bedrock,
the extent of soft soil (especially peat), and the detection of weak zones in the rock. The chosen methods were seismic refraction, electrical resistivity tomography (ERT), and ground
penetrating radar (GPR).
22.2
SURVEY PLANNING AND CONSIDERATIONS
A multidisciplinary geophysical investigation was proposed, along with and around the railway
tracks. See Figure 22.1a for the location of the acquired geophysical data. The geophysical
investigation comprised approximately 12 km SRT, 6 km GPR, and 2 km ERT collected in winter 2020/2021. It was performed with a field crew of 3 persons during approximately 35 days.
22.2.1
SRT/2D layer-based refraction seismic
interpretation
The seismic refraction measurements were performed with a 48-channel seismograph
(ABEM Terraloc Pro) and 10 Hz vertical geophones with a geophone spacing of 2.5 m. The
pulse source used was a heavy accelerated hammer (Gisco ESS100 TURBO) and explosives, with wireless triggering via an accelerometer mounted directly on the hammer or
trigger cable using a short circuit. Where these sources could not be used, an 8 kg handheld
sledgehammer was used instead. The measurements were performed with 2–4 offset shots
outside each layout to ensure full refractor coverage on all geophones down to at least 20 m.
For the calculation of loose material velocities, approximately 6–15 shots were made inside
each layout, depending on the shot geometry and conditions.
22.2.2
ERT
The electrical resistivity measurement was performed with a Terrameter LS2 (ABEM)
with 12 channels. The measurements were performed with a roll-along technique, where 4
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Figure 22.1 (a) Overview of the survey area, geophysical lines in cyan. (b) Examples of radar data
where the peat varies between 1 and 6 m, marked with the dashed black line. (c) Seismic tomography. (d) Resistivity tomography. The profles (c) and (d) are overlaid with
combined bedrock interpretation (red dashed line), layer-based bedrock interpretation
(orange line), and peat interpretation (yellow line).
Multidisciplinary geophysical investigation
167
multiconductor cables with 2 m between the electrodes were used. The method resulted in a
2D resistivity profile along the measured line.
22.2.3
GPR
The GPR measurements were acquired during two separate field campaigns. The first campaign used a dual-frequency radar with 170/600 MHz antennas (CO1760), and the second
campaign used a dual-frequency radar with 70/300 MHz antennas (CO730) (both systems
from Impulseradar). The latter has a lower frequency and lower resolution, but in order to
obtain a deeper penetration, it was used for the second campaign.
22.3
RESULTS AND CONCLUSIONS
The acquired refraction seismic data are, in general, of high quality. A small part of the seismic data has poorer quality, mainly due to bad weather conditions during data collection and
the presence of a thick peat layer in the area. However, mapping the bedrock was fulfilled
in the entire area.
The ERT profiles show varying quality with those placed at some distance from the railway generally of high quality. However, other profiles are strongly affected by conductive
objects in the ground along the railway, such as fences or wires. Where the ERT has high
quality, it complements the seismic results for the interpretation of the soil materials by
delineating low-resistive soils like clay and peat in greater detail.
The acquired GPR data are generally of very high quality, and it has been possible to interpret peat on most of the collected profiles. The peat layer was hand-picked graphically, as
this horizon is quite evident in almost all profiles. However, it is difficult to properly depict
the horizon in areas where the peat layer is thinner than approximately 0.5 m. Figure 22.1b
shows an example of a radargram.
22.3.1
Bedrock interpretation
The bedrock interpretation is based exclusively on seismic sections with support from a
few drillings. A combined interpretation is made based on two different seismic processing
methods: the layer-based method and the tomographic inversion method. In general, there is
relatively good agreement between layer-based interpretation and tomography. On average,
the layer-based interpretation follows the tomographic isoline at 2750 m/s. In most sections,
there is a good correlation between the methods, but in some sections, a lower or higher
velocity is chosen to make the tomography fit. The geology is apparently complicated in
these stretches, causing poor agreement. This disagreement provides important information
on the uncertainty of the final interpretation.
22.3.2
Peat interpretation
For peat, the interpretation is based on all three methods. In most sections, where multiple methods are available, there is a good agreement. However, in sections with a poor
agreement, GPR is given the highest priority, as this data clearly distinguish peat areas
and at which depth it should be interpreted. In most sections, the peat interpretation from
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Figure 22.2 Example of results from a smaller section at the city of Bryne. Left: Interpreted depth to
bedrock. Right: Interpreted thickness of peat.
the GPR follows the seismic tomography at a velocity of around 500 m/s. Therefore, this
velocity has been used to interpret peat on lines without GPR data. The seismic refraction and ERT data also supplement interpretation where the depth to the peat is out of the
GPR depth range. However, in areas with a poor agreement between SRT and ERT, the
most conservative interpretation with the greatest peat depth has been chosen as the final
interpretation.
Figure 22.1c shows an example of the peat and bedrock interpretation together with the
seismic refraction tomography, layer-based seismic and electrical resistivity tomography.
22.3.3
Bedrock
An example of the depth to bedrock is shown in the left image in Figure 22.2. There is generally more than 10 m to the bedrock, which is found in the range of 8–34 m. In a few sections, the bedrock is even deeper than penetration of the seismic refraction data and therefore
impossible to interpret.
22.3.4
Peat
The presence and thickness of peat vary very locally. An example is shown in the right image
in Figure 22.2, where the peat thickness varies more than 5 m in depth over a short distance.
This variation in peat thickness is a general trend for the areas. The interpretation of peat is
considered reliable.
Multidisciplinary geophysical investigation
22.4
169
CONCLUSION
In this project, it has been crucial to depict the uppermost 20 m and to identify areas with
peat and the depth to solid moraines and bedrock. In the material used for the interpretation
to date, especially in areas with few available geotechnical soundings, there is not enough
information to interpret the moraines. Therefore, the final result from the combined interpretation is limited to the base of the peat and the top of the bedrock.
22.5
FUTURE WORK
When the ongoing geotechnical survey results have been reported, it makes sense to verify
the results reported here and calibrate and reinterpret the geophysical results if necessary.
The interpretation performed is based on the results from a combination of several geophysical methods, with the support of a few geotechnical positions. With additional geotechnical
investigations at critical strategic points identified from the geophysical surveys, an update
can probably be carried out, reducing the uncertainty in the combined interpretation. It might
also be possible to supplement with an interpretation of the moraines.
ACKNOWLEDGEMENTS
The authors would like to acknowledge BaneNOR for their progressive thinking in this project and for allowing us to publish this material.
Chapter 23
Case: road maintenance
and ground frost
Jaana Gustafsson
23.1
BACKGROUND AND OBJECTIVE
Roads deteriorate with both use and loading. For proper and cost-efficient maintenance measures, the
status of the road needs to be investigated regularly during its lifetime. This is in Sweden often done
with, for instance, core samples, giving point-wise information on the different internal road layers,
from asphalt thickness down through the substructure. For longer stretches of roads, these samples
tend to get rather sparsely located. A better and more continuous picture of the road conditions can be
obtained by using GPR to map the internal road layers as well as underground conditions.
This case comprises investigations of both paved and unpaved roads in northern Sweden,
where the aim was to map the internal road layers prior to maintenance measures.
23.2
SURVEY PLANNING AND CONSIDERATIONS
Generally, the layers in a road vary in thickness, from some centimetres of asphalt to some
metres of substructure constructions. Both thin and thick layers needed to be resolved in
the investigation. Therefore, the GPR investigations were made with two different antenna
frequencies simultaneously. A high-frequency antenna of 2.3 GHz was used for the asphalt
investigations and a lower-frequency antenna, 500 MHz, for the internal layers in the road.
If needed, this setup could be supplemented with a 250 MHz antenna for information also on
sub-road conditions, such as bedrock and peat.
The high-frequency antenna can resolve layers down to some centimetres but with a low
depth penetration, approximately 0.8 m, while the lower-frequency antenna gives a depth of
approximately 3–5 m, covering the full roadbed and resolving layers approximately 10 cm thick.
As the GPR antennas were mounted on a small cart behind a car, this limited the speed
of the investigation. Depending on the quality of the road and the cart type, the speed was
adjusted to keep the antennas safe. On the cart, a GPS was also mounted to keep track of the
positioning.
The project needed to be finalised before the summer, meaning that the ground conditions
on the investigation sites could still be frozen, in other words, the measurement could be
affected by ground frost. This was considered a risk, as ground frost may affect GPR data
negatively. Ground frost can create a single layer and sometimes several layers, and if water
is present between these layers, the GPR waves can be efficiently attenuated. It should be
noted that the use of salt spray to prevent ice could also affect the GPR data. However, as the
investigations area was in northern Sweden, the use of salt on roads is forbidden.
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During one field day, 66 profile km were investigated, covering 2 lanes of 33 km of road.
The interpretation of all identified layers (1–5 layers depending on road conditions and GPR
data quality) took approximately 25 h.
23.3
RESULTS AND CONCLUSIONS
As no knowledge of the road was present beforehand, all layers in the GPR data were identified and marked in the first round of interpretation. Two of the layers were from the start
suspected to be ground frost and were also categorised as such. When the first interpretation
round was finished, a decision was made on the location of core samples. The project had
Figure 23.1 GPR equipment and results. (a) The used GPR system, a MALÅ ProEx control unit with an
XV Monitor for data collection and a setup with 2 antennas, from Guideline Geo.The highfrequency 2.3 GHz is situated between the wheels of the road cart, and the low-frequency
500 MHz antenna in front of the wheels.The control unit and monitor are placed in the
front seat, so measurements can easily be monitored by the driver. (b) Example of road
layer interpretation. Radargram with interpretations above and interpretations below.The
radargram shows approximately 4000 m of road, with one core sample point. Layers 1 and
2 correspond to road layers in this gravel road, while layers 3 and 4 are an effect of the
ground frost, and layer 5 features below the road construction, in this case, is a small peat
area. See (c) for detail. (c) Example of GPR data from a gravel road.The 2 clearest layers,
below 1 m depth, correspond to the ground frost.These are marked with arrows.
Road maintenance and ground frost
173
Figure 23.1 (Continued)
maximised the number to 3 samples/km of road, and these samples were placed in areas
where the GPR data quality was low and results uncertain but also in critical spots from geotechnical points of view. When the results from the core sampling were ready, an overview
of the GPR interpretations was made. The results from this are seen in Figure 23.1.
As seen in Figures 23.1b and c, the presence of ground frost was very clear. Core samples
below the road construction confirmed the presence of ground frost. Otherwise, the GPR
data clearly resolved the thickness of the first and most important layer of a gravel road, the
bearing layer. In some parts, a second layer, the reinforcement layer, could also be identified.
Data were delivered as shown in Figure 23.1b and as simple text files with position (X-Y)
and depth to interpreted layers.
The use of GPR gave information on the gravel road conditions in the form of layer thicknesses as well as the amount of ground frost that can be expected in this area. The information was intended to be used for maintenance work on the road and to calculate new bearing
capacities for the traffic.
Chapter 24
Case: depth to bedrock detection
by integration of airborne EM data
with sparse geotechnical drilling
data for early phase road alignment
Craig W. Christensen, Guro H. Skurdal,
Andi A. Pfaffhuber, and Martin Panzner
24.1
BACKGROUND AND OBJECTIVE
Significant cost overruns often plague large-scale infrastructure projects because of unknown
and uncertain ground conditions in the initial planning stage. Delays are often a result of
the difficulties with obtaining accurate and seamless information of the subsurface geology.
Airborne electromagnetic (AEM) systems (Figure 24.1a) provide rapid acquisition of large
amounts of geophysical data, covering large areas and probing the subsurface up to several hundred metres down. Integration of AEM-based resistivity models with geotechnical
data using machine learning can significantly reduce uncertainty and aid in planning such
projects. This integrated approach can provide accurate bedrock topography models, identify hazardous weak sediments and delineate rock and sediment boundaries, reducing costs
and saving time in large-scale linear infrastructure projects. We demonstrate the use of this
method for a tunnelling project in central Norway (Figure 24.1b). Such integration of AEM
data with geotechnical data leads to safer, more time-efficient, and more cost-efficient tunnel construction due to the early insights about ground conditions that this method provides.
Here, we discuss the delineation of lithological boundaries and mapping of the rock cover
thickness. Some of the content of this case study has been presented previously by Christensen et al. (2021).
24.2
SURVEY PLANNING AND CONSIDERATIONS
Bedrock often produces a significant contrast in electric resistivity compared to weak and
water-saturated, porous sediments such as sand and clay. This makes bedrock detection an
ideal task for the airborne electromagnetic method. This study utilised the airborne time
domain electromagnetic system SkyTEM-304 (Sørensen and Auken 2004). The system
operates with 2 different dipole moments, a low moment with ~3000 Am2, allowing for
high-quality data at early times, and a high moment with up to 150,000 Am2 ensuring sufficient depth penetration. The system was carried as a sling load below a helicopter, about
30 m above the ground. The survey was flown with a 75 m to 100 m line spacing. System
altitude, roll, and pitch were continuously measured during the survey. Within 4 flight days,
a total of 170 line-km of data were collected over the survey area. In addition, a total of 2362
geotechnical drillings were made in the course of six drilling campaigns before, during,
and after the AEM survey. A total of 1155 drillings encountered bedrock and were used to
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Figure 24.1 (a) Schematic sketch of the airborne time domain electromagnetic SkyTEM304 system.
(b) Map of the survey area in central Norway, northeast of Trondheim.The map also shows
locations for available geotechnical data at the completion of ground investigations, and
Depth to bedrock detection
177
the locations for the examples shown in (c)–(e) are marked by black arrows. (c) 3D view
of the resistivity model (coloured voxels, light yellowish colours indicate high resistivities
and dark purple colours show low resistivities).The bedrock model is displayed as a grey
surface with quantifed uncertainty (the darker the colour, the lower the uncertainty)
derived from the integration of the 3D resistivity model and the geotechnical drillings
(red cylinders) by using an ANN. (Figure modifed after Christensen et al. 2021.) (d) Evolution of the ANN predicted bedrock models and tunnel placement, projected along a
long-section at Grubbåsen and (e) along a cross section at Fossingelva around Åsen, in
Trøndelag/Central Norway. Note that the models accurately match boreholes in 3D but
appear partially off when projected onto a section through the model. (Modifed after
Harrison et al. [2021].)
determine the depth to bedrock. However, only 941 drillings were within the survey area.
Using a high-resolution (<1 m) digital terrain model and aerial photography, an additional
173 points of outcropping bedrock were identified within the survey area.
24.3
RESULTS AND CONCLUSIONS
The AEM data processing was done using the software package Aarhus Workbench (Auken
2009) and consisted primarily of altitude processing, stacking of the acquired transient measurements, and the removal of data affected by noise and coupling effects from interference with existing infrastructure. All AEM soundings were inverted simultaneously using
the Gauss–Newton-type inversion code with a Marquard modification, AarhusInv (Auken
2015). The algorithm seeks to find a pseudo-3D resistivity model that varies smoothly in
both vertical and lateral directions, also referred to as spatially constrained 1D inversion
(SCI). The vertical resolution of the resistivity model ranges from a few metres near the surface to several tens of metres at depth, and the lateral resolution varies between 50 and 150
m (Christiansen 2006). Based on the data sensitivities computed by the inversion algorithm,
the depth of investigation DOI (Christiansen 2015) was estimated to be 50–300 m, depending on geological conditions.
Manual interpretation of subsurface resistivity models from AEM data inversion can be
very tedious, often with very subjective results biased by the interpreter analysing the data.
Therefore, we utilise an artificial neuronal network (ANN), trained with data from geotechnical drillings, outcropping bedrock, and AEM-based resistivity models to provide an
objective and automated interpretation of the depth to bedrock (Lysdahl et al. 2018; Lysdahl
et al. 2022). The tunnelling projects at the Grubbåsen and Fossingelva locations in central
Norway (Figure 24.1b) provide key examples illustrating the strength of this integrated and
automated interpretation approach.
At the onset of the tunnel project, bedrock was assumed to be only a few metres deep
and to closely follow terrain elevation at Grubbåsen (Figure 24.1d). However, the trained
ANN, which combines information in the geophysical data and the available boreholes,
predicted the bedrock surface to be deeper than initially thought, leaving only a thin rock
cover above the planned tunnel ceiling (red dashed line in Figure 24.1d). These results
prompted further drillings at this location. The additional drillings were then included in
the training data set for a new ANN-based interpretation of a new bedrock topography
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model (blue dashed line in Figure 24.1d). The updated model indicated a narrow depression in the bedrock surface with little to no rock cover above the planned tunnel ceiling, motivating even more drillings to verify the ANN predictions. These final drillings
showed that the planned tunnel would pass through several metres of sediments. There
was little flexibility in adjusting the tunnel alignment at this location. Thus the tunnel
support design, construction plans, and project budgets were revised to account for the
challenging ground conditions. In the second example, location Fossingelva, the initially
assumed bedrock model suggested ample rock cover for the planned tunnel alignment
(Figure 24.1e). Using geophysical data and a few existing geotechnical soundings, the
predicted bedrock surface was revealed near the top of the planned tunnel (tunnel placement 1, Figure 24.1e). As a result, the planned tunnel alignment was shifted westward to
an area believed to have enough rock cover to excavate safely (tunnel placement 2, Figure
24.1e). Follow-up drillings were undertaken to ensure the results of the first model prediction. The updated bedrock model, which accounted for these new drillings, suggested that
the surface was likely nearer to the tunnel ceiling than previously hoped. Further drilling
showed that the bedrock surface was at or touching the second proposed tunnel placement.
There is likely a narrow immersion in the rock surface or a more fractured rock present
which the prediction could not pick up due to the resolution of the method. As a result of
these early insights into bedrock depth, the tunnel alignment was shifted for a third time
nearly 50 m deeper and even further west, where sufficient bedrock cover was present
for efficient and safe tunnel construction. The main effect of changing the alignment so
early in the project was that it allowed rapid advancement into a more detailed planning
phase. Without AEM, investigations would have continued along the more costly alignment. Therefore, time and money were saved on avoiding unnecessary drilling. It has been
estimated that construction along the initially planned alignment would have led to a cost
overrun in the order of €25 million.
REFERENCES
Auken, E., A. V. Christiansen, C. Kirkegaard, et al. 2015. An overview of a highly versatile forward
and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric
data. Exploration Geophysics 46(3):223–235.
Auken, E., A. Viezzoli, and A. Christiansen. 2009. A single software for processing, inversion, and
presentation of AEM data of different systems: The Aarhus Workbench. ASEG Extended Abstracts.
Vol. 2009. Melbourne: CSIRO Publishing, pp. 1–5.
Christensen, C. W., G. H. Skurdal, A. A. Pfaffhuber, S. Rønning, A. Lindgard, and K. C. Sellgren.
2021. Airborne geoscanning and efficient geotechnical ground investigation workflows: A roadbuilding case study from Central Norway. Proceedings of the 18th Nordic Geotechnical Meeting,
Helsinki, Finland, 25–27. https://iopscience.iop.org/issue/1755-1315/710/1
Christiansen, A. V., and E. Auken. 2012. A global measure for depth of investigation. Geophysics
77(4):WB171–WB177.
Christiansen, A. V., E. Auken, and K. Sørensen. 2006. The transient electromagnetic method. In Groundwater Geophysics. Berlin, Heidelberg: Springer, pp. 179–225. https://doi.org/10.1007/3-540-29387-6_6
Harrison, E. J., G. H. Skurdal, C. W. Christensen, A. A. Pfaffhuber, A. K. Lund, and B. Second. 2021.
Applying machine learning on airborne geophysical models to map bedrock topography and its
lithological boundaries. Intended for Presentation at ITA-AITES World Tunnel Congress, WTC2022
and 47th General Assembly Bella Center, 22–28 April 2022, Copenhagen.
Depth to bedrock detection
179
Lysdahl, A. K., L. Andresen, and M. Vöge. 2018. Construction of bedrock topography from AirborneEM data by Artificial Neural Network. In Numerical Methods in Geotechnical Engineering IX.
London: CRC Press, pp. 691–696. https://doi.org/10.1201/9780429446931
Lysdahl, A. K., C. W. Christensen, and A. A. Pfaffhuber. 2022. Integrated bedrock model combining
airborne geophysics and sparse drillings based on an artificial neural network. Engineering Geology
297:106484.
Sørensen, K. I., and E. Auken. 2004. SkyTEM – A new high-resolution helicopter transient electromagnetic system. Exploration Geophysics 35(3).
Chapter 25
Case: delineation of aggregates
gravels, sands, and silts using
electrical resistivity tomography
Christopher Phillips
25.1
BACKGROUND AND OBJECTIVE
The Vancouver Island area in British Columbia, Canada, is a desirable location for an aggregate operation as it has easy access to the entire western coast of North America and several port facilities. The aggregate deposits on Vancouver Island result from the last glacial
retreat in the area and are a rich resource. Still their distribution is often complex, grading
from coarse to fine materials and with varying thickness across a site. Therefore, an accurate understanding of the subsurface distribution and composition of the aggregate deposit
is crucial to determining whether a site is economically viable and for planning aggregate
extraction.
25.2
SURVEY PLANNING AND CONSIDERATIONS
25.2.1
Site conditions
The Vancouver Island area studied was generally forested with a mix of old-growth and
new-growth trees, which have been logged and replanted over the years. The forest floor
generally consists of very dry and loose material. Access to the site was obtained by various
old exploration paths and logging roads. Bedrock was typically deep in the area, ranging
up to a depth of 160 m below ground level. The overburden comprises quaternary deposits,
generally moraine, fluvial, and glaciofluvial in nature.
25.2.2
Geophysical planning
The geophysical survey in the case of aggregate exploration and delineation usually follows
a field mapping exploration program, which indicates the presence of aggregate potential at
the site. The geophysical investigation is carried out prior to a geotechnical drill program,
which uses the geophysical results to target drilling and to identify the resource potential.
Survey methods in the Vancouver Island area for aggregate resource evaluation consisted
entirely of the electrical resistivity tomography (ERT) method. ERT was selected for this
purpose because the measured resistivity of the subsurface generally correlates well with
the grain size of the subsurface media. The smaller the average grain size, such as clays, the
lower the resistivity in contrast to well drained sand and gravel deposits, which have much
higher electrical resistivities.
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25.2.3
Geophysical surveys
ERT has provided a relatively rapid and reliable approach for delineating aggregate sources in
many areas in North America, including the Vancouver Island area. Generally, 5 m electrode
spacing has been used for the investigation to provide efficient coverage with appropriate resolution. IRIS Syscal systems with 72–120 electrode capacity have been used to cover 0.35–
1.0 km spreads. Line locations, as shown in the example in Figure 25.1a, are generally oriented
Figure 25.1 (a) Typical line layout across a site (brown dashed lines). Line spacing is 50 m, and contours
indicate ground topography changes across the site. (b) and (c) Typical ERT results across
the site: (b) feld collected data, (c) interpreted results with borehole correlation.
Case: Aggregates gravels, sands, and silts
183
in a series of parallel lines across the site to be investigated. We typically utilise Wenner arrays
for investigation purposes to increase the signal-to-noise ratio of the collected data. As the forest floor is often dry with tree litter, the location of each electrode must be dug to expose good
soil in which to plant the electrodes to ensure low electrode contact resistance.
Initial processing of the ERT data is completed using PROSYS© software provided by IRIS,
and inverse modelling of the resistivity data is conducted using RES2DINV© (Geotomo). In
general, less than 5% of the data is rejected because most of the surveys are completed in
areas remote from cultural interference and in soils with low electrode contact resistance.
The geophysical interpretations provided stratigraphic information for aggregate deposit
mapping and, when shallow enough, depth to bedrock.
25.3
RESULTS AND CONCLUSIONS
Figure 25.1a shows a typical geophysical line layout of a property. In the case of this site, no
access pathways or roads were present, so the lines were laid out in a simple grid with lines
spaced 50 m apart from one another. Figure 25.1b and c shows typical ERT model results.
Figure 25.2 (a) Correlation of ERT-modelled resistivity versus borehole and grab sample results to
develop a site-specifc correlation of aggregate potential. (b) 3D map showing the interpreted extent of the aggregate resource at the site.
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Figure 25.1b shows the interpreted results of a single ERT line through the site. The overlaid borehole results show good agreement with obvious transitions between different aggregate materials. Along this line, a massive gravel deposit is shown to be present.
As resistivity of the subsurface can be affected by the depth to the water table, conductivity of the groundwater, presence of contaminants, and/or saltwater impact, the use of
boreholes and/or grab samples to correlate the ERT results is vital for this non-invasive geophysical investigation. A correlation plot of measured ERT conductivity versus lab results
of aggregate potential from boreholes and/or grab samples can be used to interpret the ERT
results better. An example correlation plot is shown in Figure 25.2a.
The results of the ERT survey and correlation to lab results were used to create a threedimensional model of the sand and gravel distribution along the site, as shown in Figure
25.2b, which could be used to determine the aggregate potential at the site and to plan
extraction.
Chapter 26
Case: delineation of material
type for use in ready-mix
concrete
Barry Balding
26.1
BACKGROUND AND OBJECTIVE
ERT is widely used to assist in determining variation in overburden material type, depth to
bedrock, bedrock type, and extent of weathering/alteration in bedrock.
The objective of this investigation was to differentiate between variations within sand and
gravel units and glacial “boulder clay” in order to enable specific material types on site to be
targeted for use in the manufacture of ready-mix concrete.
26.2
SURVEY PLANNING AND CONSIDERATIONS
26.2.1
Site conditions
A Quaternary-aged sedimentary deposit was in excess of 70 m thickness, consisting of sand
and gravel-rich lenses of varying quality and thickness, overlying and alternating with layers
of glacial boulder clay.
The underlying bedrock is well understood and consists of rocks from the Upper Carboniferous. The site consists of gently undulating agricultural fields used for grazing.
26.2.2
Geophysical planning
To help identify and distinguish variation within the sand and gravel units, in particular where an increase in clean, coarse stone occurred, a sub-parallel grid of lines ca.
50 m apart was marked out using a differential GPS (DGPS) for the collection of the
ERT data.
The results of the ERT survey were used to target the locations of trial pits and boreholes
for sampling and PSD (particle size distribution) analysis.
26.2.3
Geophysical survey
An ABEM Lund Imaging System was used to collect the data with a gradient array electrode
spacing of 2.5 m. Following acquisition, the recorded data were pre-processed to remove
noisy data by de-spiking before inversion processing using RES2DINV©, which provided a
contoured inverted section of apparent resistivity.
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Figure 26.1 Resistivity section with 100 Ωm contours showing variation in material types.
26.3
RESULTS
Results are displayed by calibrating the range of the colour scale and contouring interval to
the resistivity interval of interest at the site, based on results from intrusive investigations
(Figure 26.1). Figure 26.1 shows that a distinction in material types can be recognised along
the line, from a “cleaner” (less silty/clay-rich material) coarser material with resistivities
of >1400 Ωm, to a “dirtier” (increased silty/clay-rich material) finer material (with resistivities of <200 Ωm overlying boulder clay [also < 200 Ωm]).
26.4
CONCLUSIONS
The careful calibration of ERT data with targeted intrusive investigations provided the client with information on variation in the underlying sand and gravel units and allowed for a
targeted and efficient extraction plan to be developed for the deposit.
Chapter 27
Case: mapping railroad ballast
and geology using ground
penetrating radar
Jaana Gustafsson
27.1
BACKGROUND AND OBJECTIVE
Old railroads often have old electrical transmission installations in the form of overhead contact line posts, which may need an upgrade. Furthermore, the ballast may have deteriorated
during the years of use. In order to decide how and when an upgrade of the railroad embankment and the line posts should be made, information is needed on the geological conditions
related to the foundation for line posts and the thickness of the ballast. As railroads tend to
be long, the geotechnical drillings are often very sparsely distributed, providing incomplete
information for decision making.
To obtain a better basis for decision making related to both of these topics, a GPR investigation was carried out along an old railroad in need of maintenance.
27.2
SURVEY PLANNING AND CONSIDERATIONS
The railroad stretch to be investigated was 44.7 km long, and input was needed from both
sides of the embankment. Over almost 45 km, the geological conditions can and do change
a lot. The only on-hand knowledge when planning the investigation was from large-scale
geological maps, indicating mostly coarse-grained soils and a few areas with soft soils such
as clays and silts. For the foundation of the new overhead contact line posts, it was important
to distinguish whether the bedrock was above or below 3 m depth below ground level, as this
would affect the type of foundation needed.
GPR was considered the most efficient tool for the task, as it is fast in data collection, and
profiles are easy to interrupt while trains are passing. However, walking all 89 km would still
take plenty of time. The railroad had ongoing traffic, and the use of a locomotive that, despite
numerous stops, would cause traffic interruptions was discarded. Instead, the GPR was mounted
on a railway trolley so that measurements could be carried out at a bike pace. See Figure 27.1a.
Furthermore, the trolley was light enough to easily be carried off the tracks while trains passed.
In order to decide the correct antenna frequency for the survey, the following points were
considered:
•
•
•
Investigation depth of around 5–6 m was needed to determine whether bedrock was
above or below 3 m.
Resolution should be high enough to resolve even thin ballast layers.
The antenna size should be suitable to fit on the trolley setup and narrow enough for the
existing signal posts.
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Figure 27.1 GPR equipment and results. (a) The used GPR equipment, a MALÅ ProEx system from
Guideline Geo with a 250 MHz antenna.The GPR was attached with a simple ladder to a
railroad trolley as the antenna needed to be outside the rails and preferably also outside
the crossties (especially if these are reinforced concrete). (b) Example of ballast (in green)
and bedrock (in red).The ballast thickness is quite even in the shown example, around 50
cm thick. Underneath the ballast, till is found and areas with shallow bedrock. (c) The GPR
also revealed several areas of the railroad where fascine work had been used.This is seen
in the data as an even layer of hyperbolas, where each hyperbola represents a timber log.
One is marked with a red arrow. In the example, several layers can be identifed.
Mapping railroad ballast and geology
189
This led to a choice of a 250 MHz antenna, suitable for measurements down to around 5–7
m in these types of geological conditions and the possibility of resolving objects or layers
of around 10 cm.
During measurements, the position was logged with a simple DGPS. Markers were also
set in the GPR data for each railroad metre sign found on the line post. These markers were
later used to adjust the length of measurements and resulting radargram to fit the railroad
length system.
In total, the field investigation took 2 field days for 2 persons, including setup of the trolley, tests, measurements on two sides, and pauses for passing trains. Interpretation work was
done within 3–4 days.
27.3
RESULTS AND CONCLUSIONS
The results of the railroad investigations are presented in Figure 27.1b and c. As the measurement was made outside the crossties of the railroad, there were no disturbances from
these. Depending on the tie type (wood/reinforced concrete) and the distance between the
ties, GPR results are affected as each tie causes a hyperbola. In some cases, the distance
between ties and the size of the GPR antenna create too much noise and thereby less usable
data.
In Figure 27.1b, the ballast is, however, clearly seen as a top layer of around 50 cm thickness. In parts, it is hard to detect the bottom of the ballast, which is most probably due to the
age and mixing with the subsurface.
During measurements, a number of unknown, evenly distributed hyperbolas were
detected, even though measurements were made outside the crossties. The location of these
was interpreted as soft soil conditions, and as the railroad was old, the conclusion was that
the clear pattern in the radargrams corresponds to fascine work, i.e., old timber logs used to
increase the stability of the ground. This was later confirmed by test pits.
To conclude, GPR proved to be an efficient way of resolving both the ballast and the
subsurface conditions along a 45 km railroad. This, together with the very spare drillings,
gave input on ballast conditions as well as the type of foundation needed for new overhead
contact line posts.
Chapter 28
Case: assessing loose soils for
tower cable anchors using
electrical resistivity
Fern Webb
28.1
BACKGROUND AND OBJECTIVE
A radio communication tower was to be installed on an elevated silty soil bench. Variable
soil compaction and erosional features, including retrogressive gullies and sinkholes, were
important factors in selecting the location and design of the tower foundation and the ground
anchors for the cable guy lines supporting the tower. A geophysical survey was conducted
using the electrical resistivity tomography (ERT) method to delineate potential sinkholes,
loose soil, or both under the proposed locations of the anchors and tower foundation. Loose
soils and voids are expected to show higher resistance than more compact soils. Geophysical
profiling was augmented with borehole soil logs.
28.2
SURVEY PLANNING AND CONSIDERATIONS
The soft silty lacustrine soils formed an elevated bench above a river in a semiarid landscape. Figure 28.1 shows an overview of the site and a photo of the typical landforms and
surface soils, as well as a sketch map showing the layout of the survey lines and boreholes
and other relevant landscape features. The specific location of interest was bounded on three
sides by eroding slopes dissected by dry gullies and minor scarps. The landscape features
surrounding the site included several interpreted sinkholes and an incipient gully, which suggested these were preferred pathways for drainage and, consequently, erosion.
The ability of soil to conduct electrical current is dependent on both the texture of the soil
and the soil moisture content, with loose and or dry soils associated with higher values of
electrical resistivity.
In dry soils, there can be high electrical resistance at the contact between the soil and the
metal electrode stakes, which can have a negative effect on the signal-to-noise ratio. This
contact resistance is checked at the start of each data collection episode, and poor resistance
can be improved by wetting the soil around the electrode.
Two ERT profiling lines were selected to intersect with borehole locations. Electrodes
were spaced at 2 m intervals to provide good resolution from 1 m to 30 m depth of investigation. Data were collected on both lines using both the dipole–dipole and Wenner–Schlumberger array types.
Line 1 extended in a NE-SW orientation passing through BH10-4 and 6 m from BH10-1.
Due to steep and inaccessible topography at the south end of the line, it was not possible to
exactly intersect both boreholes without introducing a significant curve to the array of 115
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Figure 28.1 Overview map of the area, Survey lines marked in red, boreholes shown as yellow dots.
Pictures from the area showing the situation.
electrodes. Line 2 extended in a NW-SE orientation passing through BH10-2 and BH10-3.
Steep topography at either end of the line limited the array to 90 electrodes on this line.
Total ERT coverage was approximately 306 line-m. A total station survey was conducted
to measure the variations in topography along the ERT profile lines and the locations of the
boreholes.
28.3
RESULTS AND INTERPRETATION
The ERT data were processed using the RES2DINV software and inverted using robust
(L1-norm) data constraints. The L1-norm model fitting allows for sharper contour boundaries compared to smoother contours resulting from least-squares fits. Features in the resulting model profiles were correlated with the available geological control from the borehole
program. The inverted profiles are presented in Figures 28.2a and b.
Boreholes have been overlaid on the profiles with colour-coded stratigraphic units as indicated in the legend.
In general, the inverted resistivity profile for Line 1 shows decreasing resistivity with
depth. This correlates with the general stratigraphy observed in the boreholes, where a very
loose colluvial silt unit overlies a more compact layered lacustrine silt. High resistivity
Assessing loose soils for tower cable anchors
193
Figure 28.2 Results: inverted geophysical sections with overlain boreholes.
anomalies interpreted as representing loose soils are laterally discontinuous and appear in
the upper 10 m below ground surface. A connected series of high-resistivity anomalies from
X = 180 m to X = 230 m corresponds to the location of the West Gully.
The location of BH10-4 appears to intersect a zone of locally low resistivity; however, we
note the occurrence of two significantly deeper high-resistivity zones bracketing this borehole. The high-resistivity zones may indicate discrete and laterally discontinuous regions of
loose or disturbed silts.
The inverted resistivity profile for Line 2 shows numerous high-resistivity anomalies, typically occurring in the upper 6–8 m below ground surface. Several of these high-resistivity zones
extend deeper. Thus the profile does not show the same general decreasing resistivity with depth
as seen in Line 1. Both BH10-2 and BH10-3 intersect the edges of high-resistivity anomalies
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that may be related to zones of loose soil. The soil log for BH10-3 reports loose to very loose
silts to a depth of 5 m. This agrees well with the depth extent of the high-resistivity anomaly
adjacent to the mapped location of BH10-3. The soil log for BH10-2 reports 3 m of loose to
very loose silts above a more compact lacustrine unit. The inverted resistivity profile indicates
a deeper extent to the high-resistivity anomaly at this location, approximately 6–8 m. However,
BH10-2 is located on the margin of this feature.
28.4
CONCLUSION
The geophysical investigation successfully identified lateral and vertical variations in the
resistivity of the soil at the site. Numerous zones of high resistivity, which appear as yellows,
pinks, and greys, indicate potential looser or drier soils. In general, the ERT profiling shows
a decreasing resistivity with increasing depth below the surface, which agrees with the soil
log results showing a deeper soil unit of more compact, clay-bearing layered lacustrine silts
below the upper loose colluvial silt layer.
Chapter 29
Case: delineation of soft soils
and bedrock depth using
integrated methods
Michael Maxwell
29.1
BACKGROUND AND OBJECTIVE
The Prince Rupert area in mid-northern British Columbia, Canada, has been the site of port
and transportation infrastructure development for the past few decades. Geotechnical investigations, including geophysical, hydrological, and hydrogeological components, have been
completed for various clients at over 20 proposed facilities. The objectives of the investigations have been to characterise soil and bedrock conditions for the construction of onshore
and offshore infrastructure. The work has included soil and bedrock strength analyses, seismic assessment, soil and bedrock physical and chemical analyses (acid rock drainage), and
characterization of surface and subsurface water conditions.
29.2
SURVEY PLANNING AND CONSIDERATIONS
29.2.1
Site conditions
The geology of Prince Rupert sites is dominated by sub-parallel NNE-SSW trending bedrock ridges separated by narrow valleys with overburden typically dominated by poorly
drained soils mixed with upland organics, scattered till, thin and discontinuous active fluvial deposits, and weathered bedrock. It is likely that the ridge and valley topography has
developed due to glacial ice scour, along weaker shear zones that run sub-parallel to the valleys, followed by subsequent erosion and sediment transport along the valleys. The bedrock
ridges are irregular and undulating, leading from the narrow valley bottoms to narrow ridge
tops. Site topographies include gentle to moderately steep side slopes transitioning to gently
rolling upland bog plateau with small lakes and areas of standing water. The bedrock is typically banded grey schistose rock, with mineralogy consisting primarily of quartz and micas
with various accessory minerals. Sulphide mineralisation is present to varying degrees, with
pyrrhotite, chalcopyrite, and pyrite being the predominant sulphide minerals.
29.2.2
Geophysical planning
Geophysical surveys are typically used either before geotechnical drill programs to help
locate areas of interest for drilling or as a follow-up to boreholes to provide continuation
of stratigraphic layers between boreholes. Initial surveys in the Prince Rupert area included
electromagnetic (EM31/34), seismic refraction (SR), electrical resistivity tomography
(ERT), and ground penetrating radar (GPR). Subsequent work has additionally utilised
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multichannel analysis of surface waves (MASW) and borehole geophysics. We also note
that offshore surveys have combined single- and multichannel seismic reflection and refraction. The purpose of multi-methodology was to establish efficient programs for coverage of
large areas or long corridors.
EM surveys using Geonics EM31/EM34 instrumentation were planned and used at
many sites, particularly along linear infrastructure corridors, to provide rapid screening
for delineating shallow or deep bedrock and faults based on conductivity variations. GPR
was also used at some sites where penetration through sediments was sufficient to delineate deeper bedrock stratigraphy, especially in areas of soft peaty soils. Typically, we have
used low-frequency shielded or unshielded antenna GPR systems from any of the commercial manufacturers.
The most common and useful approach has been to use ERT with coincident or follow-up
SR to delineate soil/bedrock stratigraphy and to characterise soil and bedrock physical properties. Follow-up drilling is used to calibrate the geophysical models. In addition, targeted
MASW (active and passive) is often used in areas of thicker soils to evaluate shear velocities
for establishing soil stiffness classification. Data can be collected using SR layouts for efficiency. MASW provides average shear velocity information in the upper 30 m (Vs 30) values
that can be used for classifying soil stiffness, as noted next.
29.2.3
Geophysical surveys
ERT has provided a relatively rapid and reliable approach in the Prince Rupert area. Generally, 5 m electrode spacing has been used with occasional 10 m spacing surveys for faster
coverage and where a lower resolution was sufficient. IRIS Syscal systems with 96–120
electrode capacity have been used to cover 0.5–1.0 km spreads. Line locations, as shown
in the example in Figure 29.1a, are generally oriented across the expected orientation of
the parallel NW-SE ridges with occasional lines in the valleys along the strike of the structures where deeper bedrock is expected. We typically utilise Wenner–Schlumberger (WS),
dipole–dipole (DD), and/or reciprocal Schlumberger (RS) arrays depending on the depth of
investigation, resolution, and time available. DD surveys take the least amount of time and
provide good horizontal and vertical resolution but are more prone to noise interference. WS
surveys are robust but take more time, and RS surveys provide a compromise between DD
and WS. Follow-up SR and MASW surveys are typically done at 3–5 m spacing, dependent
on the desired depth and resolution requirements.
Initial processing of the ERT data is completed using PROSYS© software provided
by IRIS, and inverse modelling of the resistivity data is completed using RES2DINV©
(Geotomo Software). In general, less than 10% of the data are rejected because most surveys
are completed in areas remote from cultural interference and in soils with low electrode
contact resistance. Often, L1 norm criteria are used for more accurate modelling where sharp
boundaries between soil and bedrock layers are expected.
The geophysical interpretations have provided:
•
•
•
•
Stratigraphic information for soil layering and depth to bedrock, including weathered
and competent bedrock;
Soil engineering properties and seismic site classification;
Location of fractures and conductive rock deposits; and
Location of subsurface drainage channels and wet zones.
Delineation of soft soils and bedrock depth
197
Figure 29.1 (a) Typical line layout. (b) Seismic refraction profle. (c) Typical ground penetrating radar
profle in peaty soils.
We provide an overview of the results of geophysical surveying over several decades of
investigations in the Prince Rupert area for multiple projects and sites.
29.3
RESULTS AND CONCLUSIONS
Figure 29.1a shows a typical geophysical line layout of the Prince Rupert surveys. Linear
corridors have also been surveyed with geophysical lines laid along the proposed pipeline,
rail, or road alignments. Figure 29.2 shows typical ERT model results with alignments across
the typical bedrock structures. The overlaid borehole results show good agreement with
obvious transitions in the resistivity structures from conductive overburden to resistive bedrock. The conductive structures that appear to dip to depth (e.g., near BH3-1 in Figure 29.2c)
or underlie resistive bedrock are interpreted as conductive bedrock or fractures, which are
common given the area geology.
For comparison, Figure 29.1b shows the seismic refraction line with the bedrock interpretation mainly along the 4000 m/s contour and in good agreement with borehole BH2-2 and
the ERT results in Figure 29.2b.
In Figure 29.1c, we show a typical GPR profile from an area where depth penetration of
the overburden is sufficient to image deeper bedrock. Test pit results are also shown and
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Engineering Geophysics
Figure 29.2 ERT model results for lines 1,2, and 3 shown in Figure 29.1a. Red polygons indicates
areas that are unreliable near edges of the models. Red horizontal line indicates the
location where the profle in Figure 29.2b overlaps with the refraction seismic profle
in Figure 29.1b.
were used to calibrate the GPR data. In one survey, a 100-ha site was surveyed using GPR
to produce a 3D model of bedrock elevation/overburden thickness.
Finally, in Figure 29.3, we show MASW data from a deeper area of unconsolidated
sediments that have been interpreted using Geometrics Seisimager/SWTM (surface wave).
MASW data are typically collected using the same layout as used for seismic refraction,
thereby providing efficient data collection. The Vs 30 value provides seismic soil classification using the standard National Building Code of Canada (NBCC) table. Figure 29.3a
shows shallow soft soils with shear velocities below 180 m/s underlain by stiff soils with
shear velocities between 180–360 m/s based on the NBCC classifications.
As previously noted, EM surveys have been used at several sites in the Prince Rupert area,
and results have shown good agreement between geophysical, and borehole data for delineation of areas of shallow or deep bedrock. They have been used to extend ERT and borehole
results along linear corridors or over survey areas.
Delineation of soft soils and bedrock depth
199
Figure 29.3 (a) MASW model for unconsolidated soils. (b) Dispersion curve.
29.4
CONCLUSIONS
Geophysical surveys have been a part of over 20 geotechnical and environmental site investigations in the Prince Rupert area. We provide examples of the geophysical results in the
typical geology encountered in the area. In particular, we focus on ERT results used to interpret overburden thickness and depth to bedrock. The ERT results have been used to target
areas for borehole and test pit placement, as well as additional seismic and MASW surveys
to characterise soil and bedrock properties. The geophysical results are used for seismic site
characterisation in the seismically active area, delineating soft and thick soils and areas of
acid rock drainage concern and evaluating bedrock rippability.
Chapter 30
Case: high-definition bedrock
depth and conditions for urban
construction project site evaluation
in Switzerland using seismic
refraction with combined GRM
and tomographic approach
Pier Vittorio Radogna, Christophe Vergniault,
and David Dupuy
30.1
BACKGROUND AND OBJECTIVE
The purpose of this measurement campaign was to define the depth to and quality of the bedrock (Molasse). In order to achieve these objectives, we realised seismic refraction tomography (SRT) images for two acquired profiles. For the acquisition, we used a mixed source
system (Gun and Hammer). The results were calibrated using nearby boreholes, and the
image accuracy was increased with the generalised reciprocal method (GRM) technique.
30.2
GEOLOGICAL CONTEXT
Overburden fill and Moraine (glacier deposits: boulder, sand, and clay) overlay a Molasse
bedrock (sandstones and shales).
30.3
SUMMARY OF OPERATIONS
The data were recorded overnight using a 3 Geode Geometrics system (Figure 30.1a) with
72 active channels and an active spread length of 355 m (Figure 30.1b). We used a geophone
spacing of 5 m. The source points were shot every 60 m using either a seismic gun (Figure 30.1d)
or a seismic mass depending on the estimation of what would produce the best signal-tonoise ratio. Offset distance was set around 60 m, depending on the terrain obstacles.
30.4
PROCESSING
The data was processed with Geogiga Seismic ProTM software (Geogiga Technology Corp.).
Here we summarise the processing flow applied to the data:
•
•
•
Reading the SEG2 format
Insertion of the geometry (X-Y-Z for geophones and shots)
Picking of the first arrivals, setting up travel time curves
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P2
Investigation site in Switzerland
(a)
(b)
(ms) 0 (m)
distance
360 m
(ms) 0 (m)
100
100
200
200
300
300
400
400
500
500
600
600
700
700
800
800
900
900
1000
(c)
(d)
1000
distance
360 m
(e)
(f)
(g)
Figure 30.1 Overview map. (a) Seismic spread cables, geophones, and seismographs. (b) P2 seismic
array containing 72 geophones 5 m spaced. Shot interval is 60 m. (c) Traces recorded from
5 stacked sledgehammer shots. (d) Seismic gun energizer suitable for 8-bore industrial
type cartridge. (e) Traces recorded from a single seismic gunshot. (f) Depth to bedrock
(Molasse) by generalised reciprocal method (GRM). (g) Seismic refraction tomography is
an image of the subsoil distribution of seismic P-wave velocity.Two boreholes validate the
interpretation of the top of Molasse.
Urban construction project site evaluation
•
•
•
•
203
Assignment of layers
Application of the GRM technique to define the depth of each layer
Definition of the starting seismic model (from GRM) and calculation of the tomographic
inversion
Graphic formatting of the section by superimposing the tomography and the interpretation of the Molasse roof using the stratigraphy of the drillings
30.5
RESULTS
Two different types of processing were performed: the classical GRM method and the tomographic method. The GRM method (Figure 30.1f) allows identification of the boundary
between the fill (400–500 m/s) and the moraine (>2000 m/s). The location of this boundary
is uncertain with the tomographic approach (Figure 30.1g).
The tomographic model allows identifying the Molasse bedrock (> 2700 m/s) at about 25
m depth. The obtained interfaces correspond to the ones found in two calibration boreholes
(BH1 and BH2).
30.6
FINAL CONSIDERATIONS
During the planning stage of any geophysical survey, a series of realistic assumptions must
be made concerning:
•
•
•
•
•
Target depth;
Geological settings;
Urban noise;
Terrain impediments;
Etc.
These premises help select the best trade-off for acquisition geometry, seismic sources, and
processing steps.
In this case, for instance, near the survey area, there was a very noisy road with a high
amount of daily traffic. Consequently, we performed the seismic survey during the night, and
we deployed a seismic gun (Figure 30.1d) that is a slightly more powerful source (Figure
30.1e) than the standard hammer (Figure 30.1c). More powerful sources were discarded
because they were far too expensive or too loud for night-time measurements.
In addition, the choice of the array length was conditioned by the bedrock depth (assumed
to be a few tens of metres deep) and the moraine P-wave velocity (assumed to exceed 2000
m/s, close to molasse P-wave velocity). This geological setting suggested an array spread
of at least 240 m to reach the survey’s objectives. Finally, as far as the bedrock depth is
concerned, the modelling of the top of the moraine and of the molasse met the customer’s
requirements perfectly.
Chapter 31
Case: paleo-channel investigation
for seepage pathway potentials
Daniel E. Kramer
31.1
BACKGROUND AND OBJECTIVE
In the spring of 2021, Petralogix Engineering, Inc. (a California-based geophysical consulting company) was asked to discuss and present potential geophysical methods that may be
useful for the review of an environmental remediation project required for dewatering well
design and placement on a site in northern California. The project area had documented
contamination plumes moving on a south-south-west migration from a designated upgradient site towards a populated region within a highly permeable gravel zone. Based on preliminary geotechnical data, this gravel zone extended roughly 9–12 m (30–40 feet) below
ground level (bgl). As depicted on geotechnical cross sections, derived from boring data,
there existed less permeable lower and upper zones around a more permeable region. This
more permeable area was considered a potential paleo-channel pathway, which allowed for
unimpeded contaminant flow and migration.
Based on this information, Petralogix performed a field approach that allowed for critical
and detailed site characterisation in a pilot project, using minimally invasive geophysical
techniques. These techniques included P-wave refraction, refraction micro-tremor (REMI –
a passive source, surface wave method), and vertical electrical soundings (VES). This
approach aimed to develop a comprehensive geologic model for a pilot study area of interest using non-invasive geophysical methods. If successful as a pilot study, an overall larger
scope of work could then be performed regionally to follow and observe paleo-channel pathways of concern for contaminant migration. Using this method and approach, the geotechnical engineer of record would be able to better position extraction wells and other mitigation
measures. Further, mitigation measures such as grout curtains, dewatering and extraction
wells, and grout injection operations could be performed to minimise and modify plume
migration impacts.
31.2
SURVEY PLANNING AND CONSIDERATIONS
The project had unique challenges since the area was a fully developed residential neighbourhood, with streets and houses covering the entire region. The complex array of underground utilities that criss-crossed the area of interest hindered traditional test drilling from
locating the paleo-channel and identifying potential extraction well locations. Traditional
methods lack the ability to characterise the complex fluvial environment properly and were
too expensive and risky considering the nature of the contaminated plume’s capability to
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cross-contaminate other upper and lower aquifer layers. For this reason, the use of more
standard drilling techniques was not an option. The only well suited method for drilling was
sonic drilling, which was not only expensive but also extremely disruptive to residents and
other sensitive receptors. Consequently, the use of non-invasive geophysical techniques to
observe and characterise the subsurface soils was proposed. Petralogix proposed resistivity
and seismic methods that would be well suited for both environmental survey limitations and
that could provide sufficient depth and resolution to give adequate site characterisation. It
was key that any methods used had the ability to define softer sediment layers and the water
table and to paint an accurate geologic picture for the site suitable for geotechnical use and
engineering mitigation development.
For this project, Petralogix performed a total of 3 individual P-wave refraction and 3 refraction micro-tremor (REMI) survey lines (Figure 31.1). In addition, Petralogix performed 5
Figure 31.1 (a) Site map showing selected survey lines for the refraction seismic survey. Refraction
micro-tremor surveys were positioned at the centre of the lines.The site map illustrates
the complex nature of the site characterisation process due to sensitive receptors such
as houses, streets, utilities, and residents. (b) P-wave refraction data and interpretations
showing site characterisation.All observable geophysical data was indicative of a suspected
paleo-channel.
Case: Paleo-channel investigation for seepage pathway potentials
207
vertical electrical soundings (VES), using the Schlumberger array survey method. Latitudes
and longitudes for transect lines and VES soundings were taken using a Trimble® GeoXH
6000, and locations were differentially corrected for accurate site characterisation.
The seismic refraction method of geophysical testing was used to obtain vertical P-wave
profiles for soil property characterisation. The shear wave refraction micro-tremor technique
of geophysical testing (or REMI) was applied to obtain vertical S-wave profiles. Testing
using this method was convenient since it could be performed using the same equipment as
used for standard refraction surveying. For VES surveys, an Ultra MiniRES resistivity meter
was used to measure and record the resistance of the subsurface materials.
For refraction and REMI surveying, 2 Geode 24-Bit Acquisition Systems were used.
Refraction surveys consisted of 4.5 Hz geophones spaced 5 feet on centre. For VES surveys,
an Ultra MiniRES resistivity meter was used. For the refraction survey, data records were
input and analysed using SeisOpt® @2D 6.0 software (Optim Software). SeisOpt® @2D
uses only the first-arrival travel times and the survey geometry to derive subsurface velocity
information. For the REMI survey, data were collected on a field computer and then converted into a spectral energy shear wave frequency versus shear wave velocity (or slowness)
image using both SeisOpt® RemiTM 4.0 software (Optim Software). For VES modelling, the
entire set of apparent resistivity values was modelled via INTERPEX IX1D software. Multiple modelling techniques and iterations were ultimately explored to reduce errors between
modelled and observed field data to obtain the most likely case seismic and geo-electrical
parameters. This allowed for proper final geological layer modelling and interpretations.
31.3
RESULTS AND CONCLUSIONS
Based on the reviewed data, Petralogix concluded that a possible (and likely) paleo-channel
existed at the site. An example of the data output is seen here in one of the final cross section models for the project. This model shows refraction data; combined individual cross
sections were utilised to better interpret the larger regions’ complex fluvial environment.
While refraction demonstrated the site’s geology well, REMI and VES data were less useful,
which is of particular interest since multiple methods were allowed in this case for proper
site review. Where one method lacked the ability to resolve the site geology, another one
did. Resistivity VES soundings were difficult to complete due to surface restrictions during
surveying (including sidewalks, utilities, and onsite traffic issues. Likewise, REMI methods
were less useful (when compared to standard refraction) because they lacked proper resolution for very detailed subsurface mapping. However, REMI was useful in that it allowed for
reinforcement of findings in the refraction data.
From the data, Petralogix estimated the approximate depth and width of the paleo-channel, as based on a review of the geophysical data and comparison to drill logs provided by
the client. Petralogix was able to estimate the overall larger historic paleo-channel to be 220
feet wide by 100 feet deep, with an emphasis on the focused more modern paleo-channel at
90 feet wide by 40–60 feet in depth. The use of geophysical methods as a means for proper
site characterisation on this project not only allowed for proper mitigation design by the geotechnical engineer of record but also was completed for a fraction of the cost of traditional
drilling, with little to no disturbance to the area’s sensitive receptors.
Chapter 32
Case: near-surface electromagnetic
survey to support the design
of climate adaptation in urban
development plans
Peter Thomsen
32.1
BACKGROUND AND OBJECTIVE
As a consequence of climate change, extreme weather patterns have become more frequent. Extreme precipitation events become a challenge, particularly in urban environments,
because of the large fraction of paved surfaces. Present sewage systems are often unable to
convey the water away, resulting in flooding and a destructive overload on water treatment
plants. Implementing a two-string piped solution requires enormous investments, so there is
an increased focus on finding alternative solutions like sustainable urban drainage systems
based on natural infiltration capacity in selected areas. Solutions are often combined with
considerations of recreation and biodiversity.
Information about the hydrogeological conditions is crucial in choosing an optimal solution to handle rainfall in an area, whether infiltration basins or other infiltration solutions are
considered. Hydrogeological conditions include lithological characterisation and knowledge
of potential shallow perched aquifers. Boreholes provide a lithological characterisation at a
point location. Combined with additional information from detailed geophysical investigations, a more reliable extrapolation of the spatial distribution of lithological units is possible.
In addition, measurements of hydraulic head and the estimation of hydraulic conductivities
at locations selected based on the geophysical results provide the basis for correlating geophysical and hydrogeological conditions in a survey area.
In Esbjerg Municipality in Denmark, a DUALEM survey was conducted in 2020 near the
village of Hjerting, covering an area of 27 ha. The main objective of the geophysical survey
was to determine whether local infiltration of rain and stormwater would be a viable option,
as Esbjerg Municipality would prefer to avoid investing in a two-string sewage system if
possible.
32.2
METHOD
To be efficient, infiltration of rainwater requires a relatively thick unsaturated zone and a relatively high hydraulic conductivity. The hydraulic conductivity affects the dimensioning of
infiltration installations and their operating costs. In order to determine whether infiltration is
feasible for a specific area, consideration should be given to the variations in groundwater level
throughout the seasons and how future climate change might affect the groundwater levels.
The potential for infiltration of captured water can be evaluated by conducting a detailed
electromagnetic investigation of the topsoil. For the Hjerting study, a ground conductivity
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meter (GCM) method was applied to map the near-surface sediment variations. GCM was
chosen since the method is suitable for very detailed and high-resolution mapping of the
near-surface covering large areas.
The DUALEM421s is an advanced GCM sensor that includes 6 different combinations
of coil separations and orientations operated at a fixed frequency of 9 kHz. The 6 coil configurations consist of 3 horizontal coplanar configurations with distances of 1, 2, and 4 m
plus 3 perpendicular configurations with distances of 1.1, 2.1, and 4.1 m. With this instrumentation, the topsoil electrical resistivity is mapped to a depth of approximately 6 m. The
spatial density of the survey lines can be adapted to the specific target or objective for the
survey varying from 1 m to 10 m allowing detailed or regional mapping. A non-metallic
sledge for fast and smooth data acquisition, where pitch and roll, as well as vibrations, are
kept to a minimum, was used for the data acquisition. The collected data were processed
and inverted in the Aarhus Workbench software package using a dedicated module for GCM
data. After filtering and removing noisy and coupled data, the data set was inverted using a
smooth spatially constrained inversion scheme (Vignoli et al. 2015). Inversion is conducted
to convert the data from measured responses to a subsurface resistivity distribution. For
visual inspection, results are displayed as resistivity sections, mean resistivity maps, and 3D
visualisations.
32.3
HJERTING
In the Hjerting area, data were collected using a line distance of 5 m. A total of 60 line-km
DUALEM data were collected in 1 day. Based on the results from the DUALEM survey, the
locations for a total of 9 boreholes were chosen. The boreholes were drilled and cored to a
depth of 6 m, followed by a lithological description indicating varying clayey and sandy sediments. Mean resistivity intervals were generated for the inverted DUALEM results, providing a spatial image of the resistivity distribution in 0.5 m depth intervals (Figure 32.1). These
Figure 32.1 Mean resistivity in depth intervals of 1/2 m thickness.
Near-surface electromagnetic survey
211
were correlated with the lithological logs from the 9 boreholes. The near-surface interval of
0–0.5 m shows two areas of high resistivities of >1000 Ωm correlating well with previously
mapped Aeolian sand. In contrast, the area to the southeast with resistivities below 1000 Ωm
corresponds to diluvial sand. In the deeper intervals (> 1 m), the middle part of the survey
area shows lower resistivities, which are interpreted as till clays. To the south-east and in the
north-western part of the survey area, the resistivities are increasing with depth, indicating
coarse, dry, sandy deposits.
Figure 32.2 shows model sections along the survey lines closest to and centred at the
boreholes. A comparison between the lithological description of the 9 boreholes and the
Figure 32.2 Model section from A to Aʹ.The model section shows mean resistivity in the upper 7 m.
The lithological description of the boreholes is superimposed on the model sections. Red
is diluvial sand, orange is sandy till, brown is till/clay, and white is topsoil. Upper right:The
rigid DUALEM system towed by an ATV.
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resistivities reveals a clear correlation between low resistivity and clay sediment and high
resistivity and sandy sediment. The correlation indicates that the mapped resistivities can be
used to extrapolate soil characterisation to the entire area.
32.4
CONCLUSIONS
The results from the DUALEM survey, along with borehole information and estimates
of hydraulic conductivities from grain-size analyses, were used to support the physical
planning of the infiltration site. In the southeastern part of the area, a high resistivity corresponding to sandy sediments combined with an unsaturated zone with a thickness of at
least 6 m provide a good case for infiltration of rain and stormwater, whereas the zone with
low resistivities in the middle part of the survey area is less useful for infiltration. Water
from the area with the alluvial clay could potentially be transported to the high resistivity
area to the north.
Conducting a detailed hydrogeological investigation in the initial phase of the development of the area ensures that the solution for the handling of rainwater is adapted to the shallow hydrological conditions. Local sustainable urban drainage systems solutions are chosen
where possible, and in areas where infiltration is not an option, alternative discharge options
must be found.
REFERENCE
Vignoli, G., G. Fiandaca, A. V. Christiansen, C. Kirkegaard, and E. Auken. 2015. Sharp spatially constrained inversion with applications to transient electromagnetic data. Geophysical Prospecting
63(1):243–255.
Chapter 33
Case: geophysical investigation
of slope stability using electrical
resistivity tomography, seismic
refraction, and surface waves
Joanna Hamlyn
33.1
BACKGROUND AND OBJECTIVE
In an area known for unstable soils, a geophysical survey was carried out as part of a ground
investigation into the stability of a slope. The survey results were to be used in conjunction
with an invasive investigation to provide detailed information on the thickness of superficial
deposits and identify any potential slip surfaces for remediation work. An integrated survey approach consisting of electrical resistivity tomography (ERT), P- and S-wave seismic
refraction, and MASW (multichannel analysis of surface waves) was used. Previous geological mapping and intrusive investigations in the area mapped superficial deposits on top
of interbedded siltstones and sandstones.
33.2
SURVEY PLANNING AND CONSIDERATIONS
The survey strategy utilises ERT data to provide an electrical cross section from which
geological boundaries and lateral heterogeneities, including variation in material composition and weathering, can be inferred. P-wave seismic refraction was used to provide seismic velocity sections that indicate the thickness of superficial deposits and the depth to
the non-rippable bedrock. S-wave seismic refraction provides seismic velocity sections that
indicate the depth to compacted sediments, weathered and more competent (higher-shearstrength) bedrock. MASW derives the shear velocity from surface waves, which is useful
where velocity inversions are encountered.
Despite using multiple techniques to reduce the amount of non-uniqueness in the models, some limitations remain. As with all geophysical surveys, there must be a measurable
response between the target features and host material. For ERT to detect a slip plane,
a sufficiently altered resistivity must be detectable, i.e., if a clay-rich material or water
has infiltrated a weak zone or if loss of fine-grained sediment has caused voids. The Pand S-wave refraction surveys describe the bulk physical properties of a layer. Therefore,
small local variations within interpreted layers, e.g., due to weathering, lithology, or fracturing, are not detectable. So, in some cases, it can be difficult to correlate point source
data (e.g., boreholes or trial pits) where the underlying material is variable. In areas where
the bedrock is particularly shallow, MASW is not a suitable method. However, this was
not the case for this site.
Using these four techniques increases the certainty of the interpretation as they exploit different physical processes. Profiles perpendicular and parallel to the slope were acquired by a
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team of 3–5 people over 4–6 days. Rope access is often required on land stability investigations, but it was not needed in this case. The team were able to use ATVs to mobilise the kit
to central locations and then proceed on foot.
A 72-channel IRIS Syscal resistivity system was used to acquire 6 profiles across the
survey area. The ERT profiles were acquired using a standard Wenner–Schlumberger with a
2 m spacing, yielding data to approximately 20 m below ground level. The positions of the
electrodes were surveyed using a dGPS.
P-wave seismic refraction data were acquired along the same 6 profiles using a 72-channel
geometrics geode seismic system. To coincide with the ERT profiles, 10 Hz geophones were
deployed with a 2 m spacing. The seismic waves were generated by a seismic impulse device
(SID) firing a sequence of 12 gauge black powder cartridges. Seismic shots were taken at
every 6 geophones along the spread and at set distances beyond the geophone spread, to a
maximum distance of 60 m in both forward and reverse directions. The positions of the geophones were surveyed using a dGPS.
S-wave seismic refraction was also acquired along the same profiles using a 72-channel
geometrics geode seismic system. Horizontally mounted geophones were deployed at the
same intervals as the P-wave survey. S-waves were generated by striking a weighted metal
plate sideways with a sledgehammer. The plate has spikes along the underside to help couple
it to the ground. At each shot location, the plate was aligned perpendicularly to the profile
and subsequently struck on both ends to generate two sets of shear wave recordings with
opposite polarities. Seismic shots were taken at several positions along the geophone spread
and at set distances beyond, to a maximum distance of 60 m. The positions of the geophones
were surveyed using a dGPS.
To acquire the MASW data, a spread of 18 10-Hz geophones were located along the same
profile lines. A sledgehammer and plate were used as a seismic source, and shots were taken
at every other geophone. As only 18 geophones were used, the MASW section is shorter than
the other refraction data sets.
33.3
RESULTS, INTERPRETATION, AND CONCLUSIONS
Selected profiles of ERT and seismic data acquired perpendicularly to the slope are shown in
Figure 33.1. The ERT profile shows an undulating top layer of conductive material, suggesting that this material has increased moisture content, clay content, or both. It is interpreted
as superficial material. It lies on top of a more resistive layer, likely a dry, granular bedrock
such as sandstone.
The interpretation of the seismic sections is based on the widely understood and published
velocities of typical subsurface materials.
The P-wave refraction data for this site suggest there are up to 4 layers of contrasting
velocities (Layers P1–P4). Layer P1 has a distinctly low velocity and is expected to reflect
loose near-surface superficial material (i.e., soft soil). The range of velocities exhibited by
Layer P2 reflects an increase in consolidation of the dry superficial material. Layer P3 is
more difficult to discriminate as the overlap in velocities means that it can represent both
sediments (dry compact or wet) and bedrock strata (weathered/weak/fractured). Layer P4
represents the highest (and deepest) velocity unit and is likely to reflect a competent boundary within the bedrock strata.
The S-wave refraction data also suggests a 4-layer velocity model. Layer S1 has a
low velocity relating to soft soil, while layer S2 reflects stiff soil and poorly consolidated
Geophysical investigation of slope stability
215
Figure 33.1 Results from a selected ERT profle and corresponding seismic velocities acquired perpendicularly to a potentially unstable slope. (a) ERT profle. (b) P-wave seismic refraction velocity
profle. (c) S-wave seismic refraction velocity profle. (d) Seismic MASW refraction profle.
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sediments. The shear wave velocity values for S3 are to the lower end of the published
velocities for very dense soil, or soft (weak) rock, suggesting this layer comprises dense soil.
Layer S4 is representative of rock.
Shear wave velocities are more sensitive than P-wave velocities to abrupt changes in
ground stiffness due to changes in rock strength. This is particularly useful for differentiating the P-wave of layer P3 to be sediment rather than bedrock. A further advantage of shear
waves is that they are unaffected by the groundwater table.
The MASW survey results are presented as a colour-contoured S-wave velocity panel
showing a change in velocity and are inferred as changes in ground stiffness below the surface. The MASW panel appears consistent with the geology and seismic refraction results
and has not revealed any velocity inversions but does suggest that the ground is more heterogeneous than observed in the S-wave refraction profile.
The good correlation between the higher-resistivity material in the ERT profile and the
lower S-wave seismic boundary supports the interpretation of superficial material over bedrock. The ERT data also suggest that moisture or clay content varies laterally across the
superficial material. All P-wave refraction layers consistently thin upslope, interpreted as
thinning of superficial deposits. S1 correlates with the made ground topsoil layer, and S2
correlates with the transition between gravel- and clay-dominated made ground as suggested
by the P-wave data. The localised depression in S4 at a chainage of 80 m may be a controlling factor on a potential slip surface. This also correlates with a change in topographic relief
as the slope angle starts to increase downslope.
The geophysical surveys have provided a non-invasive means of investigating the subsurface with a high spatial resolution across the ground, which is difficult to access with drill
rigs and heavy plant equipment. By considering the combined results, it is possible to define
the extent and depth of the superficial material. The seismic data have identified distinct
velocity layers that provide detailed information to assist with the bulk characterisation of
the shallow subsurface. No obvious slip planes have been observed. However, the lateral heterogeneities identified by the MASW and ERT have the potential to influence slope failure.
Chapter 34
Case: integrated geophysical
investigation to map a landslip
Joanna Hamlyn
34.1
BACKGROUND AND OBJECTIVE
A geophysical survey was carried out as part of a ground investigation for the proposed
widening of an existing road. The work was designed to provide detailed information on the
geology and ground conditions with particular concern for potential landslips. The geophysical survey consisted of an integrated approach utilising electromagnetic ground conductivity, Electrical resistivity tomography (ERT), and P- and S-wave refraction and multichannel
analysis of surface waves (MASW) along all ERT lines.
34.2
SURVEY PLANNING AND CONSIDERATIONS
The intrusive investigation has logged granular overburden underlain by interbedded mudstones and sandstones. All material overlying the bedrock is interpreted to be bedrock erosion material from steep slopes and escarpments transported by weathering processes and
landslides. The topographic relief across the site suggests it is an area of active deformation.
The subset of data shown in this example covers an area with a small slip escarpment.
Electromagnetic ground conductivity was used to characterise shallow overburden deposits and identify any preferential water pathways. ERT was used to provide electrical cross
sections along selected survey profiles that allow the identification of geological or hydrogeological boundaries. P-wave seismic refraction provided seismic velocity sections indicating the thickness of superficial deposits and the depth to non-rippable bedrock. S-wave
seismic refraction provides seismic velocity sections that indicate the depth to compacted
sediments and weathered or more competent (higher shear strength) bedrock. MASW was
used to identify any velocity inversions that are invisible to the refraction techniques.
As with all geophysical surveys, there must be a measurable response between the target
features and host material. ERT can only detect a slip plane if its presence is related to a sufficiently altered resistivity. This may be the case where either a clay-rich material or water
has infiltrated a weak zone or a loss of fine-grained sediment has caused voids to form. The
P- and S-wave refraction surveys describe the bulk physical properties of a layer; small local
variations within interpreted layers exist due to weathering, lithology, and fracturing. Seismic refraction requires that the velocity of the materials in the subsurface increases with the
depth of burial. This is usually the case since the degree of compaction typically increases
with depth, and the amount of bedrock weathering tends to decrease with depth. Therefore,
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a limitation of the seismic refraction method is the inability to resolve localised weak zones
within rock where it resides below competent bedrock; hence the need to include MASW.
An electromagnetic ground conductivity meter maps the electrical conductivity of shallow subsurface materials. Areas of increased clay and moisture are evident as regions of
high values, whereas coarse-grained sediments and many bedrock types appear as areas of
low conductivity. The data were acquired by walking a multi-frequency Geophex GEM-2
instrument along 1 m spaced traverses under the control of a dGPS system. The instrument
was configured to investigate up to 5 m below ground level.
A 72-channel IRIS Syscal resistivity system was used to acquire two profiles across the
survey area. The ERT profiles were acquired using a standard Wenner–Schlumberger array
but had either an electrode spacing of 1 or 2 m, depending on site constraints. The positions
of the electrodes were surveyed using a dGPS.
P-wave seismic refraction data were acquired along the same two profiles using a 48-channel GEODE seismic system. To correlate with the ERT profiles, 10 Hz geophones were
deployed at 1 or 2 m intervals. The seismic waves were generated by a sledgehammer striking a nylon plate. Seismic shots were taken at several positions along the geophone spread
and at set distances beyond the geophone spread, to a maximum distance of 30 m due to site
constraints. The positions of the geophones were surveyed using a dGPS.
S-wave seismic refraction was also acquired along the same two profiles using a 48-channel GEODE seismic system. Horizontally mounted geophones were deployed at the same
intervals as the P-wave survey. A weighted S-wave plate struck sideways with a sledgehammer was used as the energy source. At each shot location, the plate was aligned perpendicular to the profile and subsequently struck on both ends to generate two sets of shear wave
recordings with opposite polarity. Seismic shots were taken at several positions along the
geophone spread and at set distances beyond, to a maximum distance of 30 m due to site
constraints. The positions of the geophones were surveyed using a dGPS.
In order to acquire the appropriate data for MASW, during the P-wave seismic refraction
setup, shots were acquired at every other geophone position, and the system recording time
was set to 1 s to fully capture the frequency dispersion. The data were acquired by a team of
3–4 people over 3–5 days.
34.3
RESULTS AND CONCLUSIONS
A subset of the acquired data is shown in Figure 34.1. The ground conductivity survey was
used to map the extents of moisture and clay-rich material in the shallow subsurface, as
these factors often control landslip instabilities. High conductivity (low resistivity) shown
by blue and green colours indicates a high clay or water content or both. Conversely, the low
conductivity (high resistivity) shown by the red and yellow colours indicates granular and/
or drier content.
The survey has mapped low ground conductivity across the east and south of the survey
area, along the top and upper-middle part of the embankment. This is likely related to more
granular material of overburden deposits with low moisture content. In the vicinity of a preexisting landslip, there appears to be a correlation between the western edge of the scar and
the boundary between the top resistive region and the bottom conductive zone. It is possible
that there may be an increased likelihood of instability where these two zones meet.
The ERT profile shows a highly resistive unit, likely caused by dry, granular overburden
sitting at the top of the slope and thinning out underneath the landslip. At the toe of the
Geophysical investigation to map a landslip
219
Figure 34.1 Results from an electromagnetic ground conductivity survey across a landslip and a
selected ERT profle and corresponding seismic velocities acquired perpendicular to the
slope. (a) Ground conductivity map. (b) ERT profle. (c) P-wave seismic refraction velocity
profle. (d) S-wave seismic refraction velocity profle. (e) Seismic MASW refraction profle.
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landslide, intersecting the surface is an abrupt transition to low resistivity values caused by
increased moisture and/or clay, possibly an unmapped superficial deposit. This is likely to be
related to the slip plane controlling the slide. Intermediate, laterally varying resistivity values at the bottom of the profile are likely to represent the bedrock geology and the heterogeneity associated with weathered mudstones interbedded between more competent sandstone.
The P- and S-wave refraction data can be considered as having four layers, strongly correlating to units observed in the ERT profiles. All layers are sub-parallel to the topography of
the slope. Shallow layers have a low velocity and are interpreted as unconsolidated soft and
stiff soil, equivalent to the upper resistive unit observed in the ERT profiles. The intermediate velocity layers (light green) are interpreted as stiff soil material and correspond to the
low-resistivity values. This could represent a previously unmapped boulder clay deposit. At
the base, the higher velocity layer is related to the bedrock geology, likely to represent the
competent bedrock boundary.
Seismic layers 1 and 2 suggests that the top of the slope comprises a soft, unconsolidated
overburden. Further, the ERT data suggests this is dry or granular material. The ground conductivity shows the extent of this material to the north and south of the landslide. This layer
sits on top of a stiff, highly conductive zone, likely to be a till deposit. Directly beneath the
landslide, there appears to be a low-velocity weak zone (F1) between seismic layers 2 and 3
identified in the MASW data. This could act as the slip plane, along which the made ground
has moved. The MASW data also suggest there is some variability in the strength of the
bedrock, which is likely related to interbedded mudstones (F2).
Chapter 35
Case: mapping of quick clay
risk by electrical resistivity
tomography (ERT)
Torleif Dahlin
35.1
BACKGROUND AND OBJECTIVE
Quick clay is a type of highly sensitive marine clays found in Sweden, Norway, Finland,
Russia, Canada, the United States, and other locations around the world. Quick clay occurs
when salt ions in saltwater-deposited clays with a high salinity are leached out. When disturbed, the structure of a quick clay can collapse completely, and the shear strength be
reduced to almost zero. This means that the clay’s solidity changes to flowing/liquid. Quick
clays involve considerable risks in terms of stability problems, where small initial slips may
evolve into large landslides involving the entire quick clay formation. The sliding masses
have consistencies of heavy liquids, which can flow over large distances and cause extensive damage to anything in their path. Quick clay occurs below the highest coastline in
areas where sediments have been deposited in saltwater in a marine environment. A clay is
defined as quick clay based on its sensitivity, which is the ratio between the undisturbed and
agitated shear strength of the clay. Quick clay landslides often occur in connection with or
after heavy rains or construction works with excavations where the excavated masses are
put close to the excavated pit, thereby creating an unstable situation. Because of the potentially catastrophic landslides that can occur without prior warning, it is important to map the
extent of quick clay occurrence in order to be able to take relevant preventive measures (e.g.,
Rankka et al. 2004).
Traditionally the predominant method for detecting quick clay in Sweden was to
take undisturbed samples and perform fall-cone tests on the clay in its undisturbed and
remoulded state. This is reliable but rather costly, and it only provides point information.
In highly sensitive clay and quick clay, the salt content is generally lower than in unaffected marine clay due to the leaching of salt in the pore water. Since the resistivity of the
clay is linked to the salt content, it may be possible to distinguish the presence of leached
clay from unleached. In Sweden, clay that is not leached typically has a resistivity of a
few Ωm, whereas leached clay may have resistivities around 10 Ωm or higher. However,
the limit between leached and unleached clay varies depending on the origin of the clay,
among other factors. Furthermore, other soils than leached clay can exhibit similar resistivities, for example, silty and sandy sediments. Resistivity mapping can therefore generally be used to delineate areas where the resistivity is sufficiently low (so that there is no
risk for leached clays) from those where there is a risk for leached and thereby potentially
quick clays. In this way, the investigation with slower and more expensive methods can
be focused on the critical areas while not wasting further efforts and resources on the riskfree areas.
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SURVEY PLANNING AND CONSIDERATIONS
Several different geophysical methods that can be used for mapping the resistivity distribution of the ground have been tested successfully for mapping quick clay risk, including
electrical resistivity tomography (ERT), radio-magnetotelluric (RMT), and transient electromagnetic (TEM). The latter has also been applied as helicopter-borne surveying allowing
efficient mapping of large areas but with gaps in the data cover in connection with infrastructure such as roads, railways, power lines, and areas with buildings.
In the example presented here, the demands for improved highway and railroad standards
were the reason for performing a large number of geotechnical field investigations. The site
is located in close proximity to existing infrastructure, where RMT and TEM would be ruled
out because of the electromagnetic noise. Therefore, ERT, which is more robust against such
noise, was applied and followed up by cone penetration tests (CPT) with in situ measurement of resistivity (CPT-R).
ERT measurements were performed as five parallel profiles between the existing railroad
and the Göta River (Göta älv) (Figure 35.1). The resistivity profiles were 400 m long with a
line separation of 5 m, where the short distance between the railroad and the river prevented
measurement lines perpendicular to the parallel lines. Line 1 was located approximately
8 m away from the existing railroad track. The ABEM Lund Imaging System was used for
the surveying with a multiple-gradient array protocol and an electrode spacing of 5 m and
in addition with 2 m electrode spacing for Line 2. A total of 11 CPT-R soundings were carried out to depths varying between 12 and 32 m, placed on the resistivity profiles close to
undisturbed sampling points (Dahlin et al. 2013; Schälin and Tornborg 2009).
35.3
RESULTS AND CONCLUSIONS
The inversion results (L1-norm robust inversion) are presented in Figure 35.2 for ERT Line
2 and Line 4. Notice the strong vertical exaggeration and that only part of the depth coverage
of the ERT survey (around 75 m) is shown. Sensitivity values derived from previously made
undisturbed samplings are superimposed on the ERT sections, and the black marker lines illustrate interpreted levels for the top of friction material. No drillings have been made to confirm
the location of the bedrock surface. The coarser material underneath the clay probably consists
of till with varying thickness. The sensitivity values have been divided into three categories,
according to the Swedish definition: St > 50 quick clay, 50 > St > 30 highly sensitive, and 30 >
St > 8 moderately sensitive (Dahlin et al. 2013; Schälin and Tornborg 2009).
Figure 35.3 shows examples of measured resistivity from CPT-R along with extracted
resistivity from the inverted ERT sections, displayed together with corresponding sensitivity values from undisturbed sampling. The sensitivity values are derived from undisturbed
sampling located near the CPT-R soundings. The columns display the generalised soil classification and the colour descriptions from laboratory protocols. When comparing resistivity
values from CPT-R and ERT measurements, they agree very well in the upper 10–12 m, as
can be seen in Figure 35.3, whereas CPT-R tends to give lower values at greater depths.
CPT-R gives the same resistivity trends as ERT surface measurements but with high resolution maintained at depth, yet with limited consistency with the soil classification. Below
depths of 15 m, there is, in several cases, a significant difference in measured and interpreted
Mapping of quick clay risk
223
Figure 35.1 Overview of profle and CPT locations. (a) Site situation; yellow lines are survey lines.
(b) Location of CPTs in relation to survey lines. Modifed from Dahlin et al. (2013).
values between CPT-R resistivity and ERT inversion results. It coincides with a sulphide
content horizon. The differences may be due to limited resolution of the ERT with depth and
deviations from the 2D assumption used in the inversion of data (e.g., so-called 3D effects).
Furthermore, the differences may be due to underestimation of the resistivity by the CPT-R
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Figure 35.2 ERT results for Line 2 and Line 4, with sensitivity values from laboratory analyses of soil
samples plus interpreted level of the top of the friction material marked. Modifed from
Dahlin et al. (2013).
measurements caused by IP effects (the subsurface acts as a capacitor that introduces a delay
in the built-up/decay of the signal), possibly caused by the sulphide content. The CPT-R
probe uses a much higher frequency of the measurement signal than the ERT instrument,
200 Hz versus 1 Hz. This could lead to a severe underestimation of the DC resistivity if there
is significant IP effect (the signal may not reach the equilibrium level corresponding to the
emitted current).
An evaluation of the measured resistivity from all CPT-R tests and their corresponding
sensitivity values from undisturbed sampling located near the CPT-R soundings was made.
It showed that, for this site, for resistivity values below 2–4 Ωm, the corresponding values of
sensitivity lay solely below the limit of quick clay (St > 50) according to the Swedish definition (Dahlin et al. 2013; Schälin and Tornborg 2009).
In conclusion, resistivity mapping with ERT can be used for screening the risk for quick
clay occurrence in areas with marine clay. If the resistivity is sufficiently low, then the salt
Mapping of quick clay risk
225
Figure 35.3 Examples of CPT-R results with extracted resistivity values from the corresponding
inverted ERT models (L1 norm and L2 norm inversion).The grey column in the diagrams
shows laboratory classifcation of soil samples according to the Swedish standard (SGF
2013), with explanation as follows. Left: Cl pr (sh) = clay with plant remains and shells,
Cl = clay. Right: Cl sh = clay with shells; (su) Cl sh = clay with shells with some sulphide;
Cl = clay; su Cl = sulphide clay; su si Cl sa (gr) = silty sulphide clay with sand and gravel
inclusions. Modifed from Dahlin et al. (2013).
content is sufficiently high to prevent the occurrence of quick clay, and if the resistivity is
sufficiently high, it is another type of geological material than clay. For intermediate resistivities, it could be quick clay, but it could also be material with coarser fractions or clay that
is leached but not quick. By conducting such screening, follow-up investigations with geotechnical soundings and sampling with following laboratory analyses can be focused on the
high-risk areas. Note, however, that the exact threshold between quick and non-quick clay is
site dependent and needs to be established for the investigated area in question.
An advantage of ERT compared with inductive electromagnetic methods is that it is much
more robust against electromagnetic disturbances. It can therefore often be used with good
results close to infrastructure such as powerlines, railways, metal fences, etc.
ACKNOWLEDGEMENTS
The resistivity results presented in the article were acquired, processed, and compiled by
David Schälin and Johannnes Tornborg within the framework of their MSc thesis work.
Håkan Garin (Geoverkstan) initiated the project and provided supervision, together with
Claes Alén (Chalmers University of Technology).
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REFERENCES
Dahlin, T., D. Schälin, and J. Tornborg. 2013. Mapping of quick clay by ERT and CPT-R in the Göta
Älv River valley. In Landslides in Sensitive Clays, From Geosciences to Risk Management, eds.
Jean-Sébastien L’Heureux, Ariane Locat, Serge Leroueil, Denis Demers, and Jacques Locat. Cham,
Switzerland: Springer, pp. 217–228. ISBN 978-94-007-7078-2.
Rankka, K., Y. Andersson-Sköld, C. Hultén, R. Larsson, V. Leroux, and T. Dahlin. 2004. Quick Clay in
Sweden, Statens geotekniska institut, Linköping, Sweden, SGI Rapport 65, 145 p.
Schälin, D., and J. Tornborg. 2009. Evaluation of CPT-R and Resistivity Measurements in Quick Clay
Area. Master’s Thesis 2009:32, Chalmers University of technology, Göteborg, 77 p.
Swedish Geotechnical Society. 2013. Geoteknisk Fälthandbok Version 1.0. SGF Rapport 1:2013,
Swedish Geotechnical Society, Linköping, Sweden, 211 p.
Chapter 36
Case: quick clay volume delineation
based on AEM resistivity,
geotechnical soundings, and
lab samples
Craig W. Christensen, Andi A. Pfaffhuber,
Guro H. Skurdal, and Martin Panzner
36.1
BACKGROUND AND OBJECTIVE
Geological risk is frequently a critical factor leading to significant schedule delays and cost
overruns in large-scale infrastructure projects like roads, railways, and tunnels (Beckers et al.
2013). In the early planning stages, finding a suitable corridor or locality to build in is critical. Large areas must be investigated to compare the risk of choosing between different route
alternatives for either surface constructions or tunnels on or within different soil and rock types.
For this study, we focus on mapping sensitive glaciomarine clay, otherwise known as
quick clay. In high latitudes, quick clay poses a unique challenge due to the risk of landslides. In order to secure construction projects in such areas, significant ground improvement
is required. In Norway, clay is usually defined as quick at a remoulded shear strength of less
than 0.5 kPa and is defined as brittle when under 2.0 kPa. (Note that quick clay is a subset of
brittle material; all quick clay is brittle, but not all brittle clay is quick.) Glaciomarine clay
turns into quick clay after extensive pore water leaching by ground- and rainwater. Thus
salinity is a standard indicator for the probability of clay to be quick. Extensive studies on
samples from sites in Norway have shown the relationship between electric resistivity, salinity, and remoulded shear strength (Long et al. 2017). Quick and brittle material has usually
resistivities of between 8 and 100 Ωm in Norway (Bazin et al. 2013; Solberg et al. 2008).
Therefore, these types of sediments can be mapped with airborne electromagnetic (AEM)
methods, which can survey large areas in a timely and cost-efficient manner. Some of the
content presented here has previously been published by Christensen et al. (2020).
36.2
SURVEY PLANNING AND CONSIDERATIONS
Our field data example comes from a proposed expansion project for the E16 highway, approximately 60 km east of Oslo (Figure 36.1b). This area has postglacial geomorphology that is typical for the eastern Norwegian lowlands. Large expanses of glaciomarine clay occur along the
planned road alignment (very low electrical resistivity), some sections of which are quick (low
resistivity). To a lesser extent, exposed metamorphic and igneous bedrock (high resistivity),
moraines (high resistivity), and glaciofluvial deposits (medium resistivity) are also found.
AEM data were acquired for 178 line-km along the planned road alignment and on additional
grids at two river crossings (Vorma to the west and Uåa to the east; Figure 36.1b). For this study,
a time domain electromagnetic system – SkyTEM302 (Sørensen et al. 2004) – was suspended
below a helicopter and flown at a nominal height of 30 m above the ground (Figure 36.1a).
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Figure 36.1 (a) Schematic sketch of the airborne time domain electromagnetic SkyTEM304 system.
(b) Geological map of the survey area in southern Norway, west of Oslo.
Source: Figures modifed after Christensen et al. (2020)
System altitude, roll, and pitch were continuously measured during the survey. The system operates with 2 different dipole moments, a low moment of ~3000 Am2, to ensure high data quality
at early times and high resolution of the shallow subsurface, and a high moment of about 75,000
Am2, for deeper penetration and resolution of the resistivity structures in the deeper subsurface.
In addition, more than 1300 geotechnical soundings were performed during and after the
AEM survey along the planned road alignment. Only 1105 soundings had some measurement of depth to bedrock, and a subset of 98 soundings were drilled at least 3 m into the
rock to confirm that the material was bedrock and not hard moraine or a glacial erratic. The
remaining 1007 holes were rotary pressure soundings that reached a hard layer assumed to
be bedrock. The soundings were also used to locate quick or brittle clay by analysing the
feed force necessary to drive the probe into the subsurface. Of the soundings, 762 were
within 75 m of an AEM sounding location.
36.3
RESULTS AND CONCLUSIONS
The AEM data processing was done using the software package Aarhus Workbench (Auken
et al. 2009) and consisted primarily of altitude processing, stacking of the acquired transient
measurements, and the removal of data affected by noise and coupling effects from interference with existing infrastructure. The AEM soundings were inverted simultaneously using
the Gauss–Newton type inversion code with a Marquard modification, AarhusInv (Auken
et al. 2015). The algorithm seeks a pseudo-3D resistivity model that varies smoothly in both
the vertical and lateral directions, referred to as spatially constrained 1D inversion (SCI). The
vertical resolution of the resistivity model ranges from metres near the surface to several tens
of metres at depth, and the lateral resolution varies between 50 and 150 m. Based on the data
sensitivities computed by the inversion algorithm, the depth of investigation (DOI) (Christiansen et al. 2012) was estimated to be 50–200 m, depending on geological conditions.
Quick clay volume delineation
229
Even though the inverted resistivity models resolved subsurface resistivity variations
well (Figure 36.2b), there is only an imperfect correlation between resistivity and shear
strength (Figure 36.2a) and hence the presence of brittle clay. That makes it difficult to
Figure 36.2 (a) Scatter plot of resistivity extracted from the AEM data inversion result and remoulded
shear strength as measured in a laboratory. (b) 3D view of the resistivity model from AEM
data inversion, together with satellite imagery and bedrock topography. (c) Portions of the
model where the probability of brittle clay was estimated as high. (Figures modifed after
Christensen et al. [2020].) (d) Example output of the general geotechnical sounding classifer where 75% of input lab samples were used as training data.
Source: Figure modifed after Christensen et al. (2020)
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delineate areas with a high risk of brittle clay from the resistivity models alone. Therefore,
we use a two-step process to compute the probability of brittle clay occurrence throughout
the surveyed area.
First, we classify the geotechnical drilling data with a supervised machine learning algorithm, using co-located lab samples as training data. That results in a data set, providing
a probability for the occurrence of brittle clay as a function of depth for each of the 762
geotechnical sounding locations along the survey line (Figure 36.d). Second, we use this
information as input training data to classify the AEM-based resistivity model, again using
a supervised machine learning algorithm. This second classification utilises both local information about the probability of brittle clays at the drilling locations, the weak correlation
between resistivity and remoulded shear strength, and the lateral and vertical trends in the
resistivity model to estimate a 3D probability distribution for the presence of brittle clay
(Figure 36.2c).
These probability estimates allow us, even in an early project phase, to eliminate zones
where brittle clay is unlikely, confirm where it is very likely, and indicate where there
is a high degree of uncertainty. These are all helpful insights for the planning of followup site investigations. The method’s performance increases markedly with new training
data. Being an automated method, these interpretations can be easily and quickly updated
over a project’s lifetime, i.e., as new geotechnical soundings become available. Further,
this machine-learning-based interpretation of geotechnical and geophysical data is data
driven and not biased by a possibly subjective human interpreter. The ANN training process finds patterns and trends in the resistivity model that correspond with the presence of
brittle clay at the drilling locations. These patterns are then used to predict the probability
of brittle clay throughout the entire 3D resistivity model in a systematic and unbiased
manner.
REFERENCES
Auken, E., A. V. Christiansen, C. Kirkegaard, et al. 2015. An overview of a highly versatile forward
and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric
data. Exploration Geophysics 46(3):223–235.
Auken, E., A. Viezzoli, and A. Christiansen. 2009. A single software for processing, inversion, and
presentation of AEM data of different systems: The Aarhus Workbench. ASEG Extended Abstracts
2009.
Bazin, S., and A. Pfaffhuber. 2013. Mapping of quick clay by electrical resistivity tomography under
structural constraint. Journal of Applied Geophysics 98:280–287.
Beckers, F., U. Stegemann, N. Chiara, A. Flesch, J. Maly, and E. Silva. 2013. A risk-management approach
to a successful infrastructure project. McKinsey Working Papers on Risk, Number 52. https://
www.mckinsey.com/business-functions/operations/our-insights/a-risk-management-approach-to-asuccessful-infrastructure-project?cid=eml-web
Christensen, C. W., A. A. Pfaffhuber, G. H. Skurdal, A. O. K. Lysdahl, and M. Vöge. 2020. Large scale
and efficient geotechnical soil investigations: Applying machine learning on airborne geophysical
models to map sensitive glaciomarine clay. Proceedings of the 6th International Conference on Geotechnical and Geophysical Site Characterization, Budapest, Hungary, 7–11. http://isc6.org/images/
Cikkek/Sessions/ISC2020-468.pdf
Christiansen, A., and E. Auken. 2012. A global measure for depth of investigation. Geophysics
77(4):WB171–WB177.
Quick clay volume delineation
231
Long, M., A. Pfaffhuber, S. Bazin, K. Kåsin, A. Gylland, and A. Montaflia. 2017. Glacio-marine clay
resistivity as a proxy for remoulded shear strength: Correlations and limitations. Quarterly Journal
of Engineering Geology and Hydrogeology 51:63–78. http://doi.org/10.1144/qjegh2016-136
Solberg, I. L., J. S. Ronning, E. Dalsegg, L. Hansen, K. Rokoengen, and R. Sandven. 2008. Resistivity measurements as a tool for outlining quick-clay extents and valley-fill stratigraphy: A feasibility
study from Buvika, central Norway. Canadian Geotechnical Journal:210–225.
Sørensen, K. I., and E. Auken. 2004. SkyTEM – A new high-resolution helicopter transient electromagnetic system. Exploration Geophysics 35(3).
Chapter 37
Case: depth to bedrock and weak
zone detection for tunnel design
under water passages
Torleif Dahlin, Mathias Ronczka, and Roger Wisén
37.1
BACKGROUND AND OBJECTIVE
A number of tunnels that will pass under water in Stockholm are under construction or being
planned, some of them crossing under the freshwater of Lake Mälaren and some under the
brackish water of Saltsjön. In this case, we present a combined survey with underwater electrical resistivity tomography (ERT) and seismic refraction tomography (SRT) layouts that
were carried out in Lake Mälaren in central Stockholm as part of the pre-investigations for a
sewage tunnel crossing under the water passage. It was conducted to map variations in depth
to bedrock and to find water-bearing fracture zones and variations in rock quality.
The survey was located where an engineering geological map indicates that several tectonic zones cross. Since the water passage is located over a very prominent tectonic feature,
stretching from central Sweden to St. Petersburg, the tunnel can be expected to pass through
rock that is highly fractured and weathered in places. This means unstable and water-bearing
rock, with a risk for infinite amounts of water that can enter the tunnel from the water body
above. Offshore geotechnical drilling is complicated and expensive to carry out. It requires
relocating the traffic of ferries and other vessels that operate on the waters and only provides
punctual information. The percussion drilling commonly used for depth to bedrock mapping
can be difficult to interpret due to blocks as well as fractured and weathered transition zones
or underlying till that can be blocky as well as firm. There have also been problems in carrying out horizontal core drilling due to highly fractured and collapsing rock.
37.2
SURVEY PLANNING AND CONSIDERATIONS
The surveying environment in the water passages in Stockholm is challenging because of
varying water depths ranging up to around 30 m, with either fresh or brackish water depending the side of the sluice Slussen. Furthermore, the urban location with various noise sources
in terms of electrical fields, vibrations from traffic systems, and industry can be problematic.
Various cables and ship wrecks on the bottom restrict spread locations and shot positions,
and in addition, gas in the bottom sediments is common.
The urban environment and the existence of electrical power cables on the bottom in
critical zones excluded electromagnetic methods. ERT, which is relatively robust against
electromagnetic interference, was hence chosen for the surveying. The large water depths
excluded a floating electrode cable as an option. Thus bottom deployment was chosen. This,
in turn, makes it essential to carefully record the depth of each electrode position, which,
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together with the water resistivity, is required in the inversion in order to recover the subsurface resistivity distribution. The water resistivity was measured by dangling a borehole
electrode cable with short electrode-spacings in the water column.
Previous experiences of seismic surveys have shown that the gas in the bottom sediments
attenuated seismic signals recorded with towed hydrophone streamers to the extent that hardly
any useful depth penetration was achieved. SRT was therefore performed with a bottomdeployed hydrophone streamer cable for recording. Small explosive charges hanging 1 m above
the bottom were employed as the source, with one shot-point for every four hydrophones.
Data acquisition was carried out along 6 profiles, with a 5 m separation between electrodes and hydrophones. A layout consisting of two 64-electrode cables was used for the
ERT, resulting in a total electrode spread of 128 electrodes. The SRT was carried out with a
streamer cable with 48 hydrophones. Hence the longer lines were carried out in steps.
37.3
RESULTS
At first, the seismic measurements revealed an elevated noise level, mainly due to traffic,
which led to a poor signal-to-noise ratio and data quality problems. Furthermore, gas in the
bottom sediments attenuated the signal along parts of the lines, which led to data losses. In
order to improve the data quality, all further seismic measurements were conducted during
night-time when noise levels were lowest, which was necessary to achieve sufficient data
quality. ERT data were acquired during the daytime, mainly due to limited field resources
and because there was no time also to collect the ERT data during the night. ERT data collected during the day showed sufficient data quality. However, it could have been improved
by measuring during some hours after midnight since train traffic, which is the prime cause
of the problematic electrical noise, is minimal then. The model sections presented here were
inverted using the open-source library by Rücker et al. (2017) for both data types. The details
of the inversion are described by Ronczka et al. (2018).
ERT results for all six profiles are shown in Figure 37.1a. The results show consistent variations in a low-resistive top layer that corresponds to variation in depth of fine sediments.
The ERT lines are generally in good agreement at the crossing points except Line 6, which
shows low resistivities throughout the depth of the model section along the western parts of
the line. This appears to be caused by the generally gradual character of models created by
the inversion software, as inversion with other software resulted in a high-resistive bottom
layer to the end of the line. Apart from this, the bedrock is characterised by high resistivity,
except for a vertical zone most clearly visible in Line 2, which can be noted in Lines 1, 3,
and 6. It is interpreted as a weak zone in the bedrock.
The SRT results in Figure 37.1b show a picture largely in agreement with the ERT results
but with some important differences. The transition from the low-velocity fine sediment on
top of the underlying high-velocity bedrock layer is sharper than the corresponding transitions in the resistivity models. Line 1 lacks any useful depth penetration in the southern
end due to attenuation of the signal in the gas-filled sediments in combination with noise.
The low-resistivity zone in the bedrock, visible in Lines 1, 2, 3, and 6, is not seen in the
SRT results. This may be explained by a lack of data coverage, except for Line 6. The low
resistivity that extends to the full depth in the westernmost end of Line 6 is not matched by
a similarly great depth in the SRT model.
Joint inversion of the ERT and SRT data was applied to all the sections, in which the data
sets can help each other to better define the investigated ground. In this way, the seismic data
Depth to bedrock and weak zone detection
235
Figure 37.1 Overview: (a) Electrical resistivity tomography (ERT). (b) Seismic refraction tomography
(SRT) made as pre-investigation for a sewage tunnel in Lake Mälaren.
Source: Modifed from Ronczka et al. (2018)
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can contribute to more well defined depths of sediments, and the resistivity data can cover
up where seismic data are missing due to, for example, attenuation of the signal by gas-filled
sediments. Figure 37.2 shows the good agreement between the results for resistivity, seismic
refraction, and rock level probing for Line 6 in the middle of the sections, where both the
geophysical methods show increasing values at the depth where the rock level was interpreted from the rock level sounding. Due to the smooth character of the inverted sections,
resistivity and seismic velocity are not expected to match the actual physical properties but
be relative. Hence the threshold for the transition to bedrock is based on correlation to the
drilling results rather than a direct translation of geophysical values to the formations. The
upper low-velocity zone in the western end of Line 6 of the separate inversion is only partially matched by a low-resistivity zone, which indicates that it consists of coarse-grained
Figure 37.2 (a) Model sections for Line 6 showing cross sections through the ground for resistivity. (b)
Seismic P-wave velocity. Interpreted bedrock level for percussion sounding marked with
dots. (c) Conceptual geological model for Line 6 based on geological background material,
geophysical results, and geotechnical drilling.
Source: Modifed from Ronczka et al. (2018)
Depth to bedrock and weak zone detection
237
sediments rather than clayey sediments. The extension of lower resistivities towards depth is
not so clear as in the separate inversion section (Figure 37.1), but on the other hand, a lowresistivity zone that extends towards depth is visible around 300 m, which can be interpreted
as a tectonic zone. In the eastern part of the section, the combination with SRT has clearly led
to a clearer transition to higher resistivity, and the rock level from the percussion sounding
coincides with a transition in ERT and seismic results, however, to significantly lower resistivities and seismic velocities, respectively. It can be interpreted as the upper part of the rock
being fractured and permeable and perhaps also partially weathered, which would explain
the lower values. A conceptual geological model based on Line 6 is shown in Figure 37.2c.
37.4
ASSESSMENT AND CONCLUSIONS
As for land-based measurements, an advantage of geophysical surveys are continuous models that can be linked to soil depth, structures in the rock, and hydrogeological conditions
with the help of reference data from drilling and measurements in boreholes. The results
provide an overview of the complex geology that is not possible with other methods and
that forms a good basis for further investigations. The electrical and seismic models from
the combined survey complement each other, where any gaps in the coverage of one method
can be covered by the other. By using several methods, a more reliable basis is obtained for
interpretation and assessment of the depth to rock, of variation in rock quality, and of the
presence of weakness zones in the rock. The geophysical results provide a good basis for
placing detailed surveys so that these become representative, and the risk of missing zones
with deviating properties is minimised. Then the documentation from the drilling can be
used for calibration and refinement of the interpretation of the geophysical models.
The additional cost of using both geophysical methods together is significantly less than
conducting two separate surveys, as planning, preparation, logistics, etc. can be shared. It is
also quality enhancing because positioning errors between the methods can be reduced or
eliminated by bundling the measuring cables to lay them out together. The combined geophysical profile, as previously shown, was equivalent in cost to a single marine percussion
drilling. Therefore, it is very cost-effective to start with a geophysical survey, which can
form the basis for an optimised drilling program. The continuous structural (seismic) and
parametric (ERT) information are valuable assets to geotechnical investigations, providing
a more consistent subsurface model. However, noise levels are still potentially challenging
in urban environments but can be reduced by careful planning and shifting some of the measurements into night-time.
An engineering geological interpretation of the geophysical results, combined with strategically placed geotechnical investigations and core drilling, can provide a picture of the
variation in depth to rock, the location of tectonic zones and where the rock may be fractured
and clayey or consist of a different type of rock. Such information is, of course, of critical
importance for the planning of underground structures.
ACKNOWLEDGEMENTS
Thanks are due to Stockholm Water for making data from the field survey available and for
giving permission to present the results. Elisabeth Lindvall and Erik Warberg contributed
during the field campaign in Lake Mälaren and through their thesis work. Funding that made
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this work possible was provided by BeFo, Swedish Rock Engineering Research Foundation (refs. 314 and 331, plus MSc thesis support) and SBUF, The Development Fund of the
Swedish Construction Industry (refs.12718 and 12719), and Formas, The Swedish Research
Council for Environment, Agricultural Sciences and Spatial Planning, provided funding for
this presentation of the results (ref. 2012-1931) as part of the Geoinfra-TRUST framework
(www.trust-geoinfra.se/). Byggrådet provided additional funding that was essential for this
study.
REFERENCES
Ronczka, M., R. Wisén, and T. Dahlin. 2018. Geophysical pre-investigation for a Stockholm tunnel
project: joint inversion and interpretation of geoelectric and seismic refraction data in an urban
environment. Special issue on urban geophysics. Near Surface Geophysics 16:258–268. https://doi.
org/10.3997/1873-0604.2018009
Rücker, C., T. Günther, and F. M. Wagner. 2017. pyGIMLi: An open-source library for modelling
and inversion in geophysics. Computers and Geosciences 109:106–123. https://doi.org/10.1016/j.
cageo.2017.07.011
Chapter 38
Case: 3D model of depth to bedrock
for a new train tunnel under
the capital of Norway
Fredrik Olsen, Anders Almholt, and Roger Wisén
38.1
BACKGROUND AND OBJECTIVE
A new train tunnel is constructed under the capital of Norway. For this project, the client
needed detailed information about the depth to the bedrock and detection of weak zones in
the bedrock. Numerous geotechnical drillings have been performed for this project, but they
only provide information at specific locations. It was proposed to complement the geotechnical borehole information with a seismic refraction survey. The main objective was to create
a 3D model of the depth to bedrock based on a geophysical investigation and information
from the geotechnical drillings.
38.2
SURVEY PLANNING AND CONSIDERATIONS
The fieldwork assignment comprised the acquisition of seismic refraction data in three separate survey areas – all within an urban environment with a dense population and varying
traffic and terrain conditions. The survey plan was to acquire a total of ca. 3250 m of seismic
refraction data. Seven lines with a total of 850 m at St. Georg Vei, 12 lines with a total of
1500 m at Nedre Silkestrå, and 10 lines of a total of 900 m were collected on Oslo Tennis
Club’s (OTK) areas. See the overview map in Figure 38.1a. The fieldwork was performed
during 8 days with a crew of 3 persons.
For optimal data quality, a heavy accelerated hammer was used as the primary energy
source. However, in some locations, an ordinary handheld hammer was used instead due
to limited space. The accelerated hammer is permanently mounted onto the frame of a
purpose-built 6WD ATV. The survey lines were collected with a towed seismic cable with
48 channels and/or hand-laid geophones, both with a 2.5 m distance between geophones.
Shot point distance is short, 7.5–15 m, in order to obtain high vertical and horizontal
resolution. Figure 38.1b shows the setup with the heavy accelerated hammer on the ATV
in the background and the seismograph with the towed seismic cable drum in front.
When collecting data in an urban environment, one of the most critical quality requirements is attention to cultural noise. In an urban environment, it is important to identify the
quietest period of the day or night without heavy traffic, industrial noise, etc. Therefore,
planning is an essential part of collecting a seismic refraction survey since there are many
stakeholders with different interests, including businesses and citizens, daily rhythms of traffic, leisure, and sleep. Nearby construction sites can cause 3D effects in the data, and thick
asphalt creates high-velocity artefacts.
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Figure 38.1 (a) Overview of survey areas. (b) Setup of the data acquisition. (c) Example of frst arrival
picking of a single shot (left) and several shots (right). In this example (left), there is an
impact on data from high-velocity layers at the top. It is possible to see the frst arrival
of geological materials behind these but with an increased uncertainty. (d) Tomographic
inversion of a single survey line together with geotechnical information (yellow indicates
bedrock), layer-based bedrock interpretation (orange line) and combined bedrock interpretation (red dashed line).
During acquisition, there must be a continuous focus on monitoring noise levels and the
quality of the acquired data making sure it is interpretable. Furthermore, attention must be
paid to elements that may create artefacts in the data. External conditions have a tremendous
impact on data quality and consequently on the precision of the first arrivals to be picked on
the individual shots for interpretation.
3D model of depth to bedrock
38.3
241
RESULTS AND CONCLUSIONS
First arrival picking is performed on individual seismograms from each shot point and can
be difficult, for example, where the previously mentioned noise and artefacts overlay the
target data. In such areas, greater uncertainty is associated with velocity assessments both for
the overlaying looser materials and the bedrock. Therefore, a greater uncertainty must also
be considered when calculating the depth to the bedrock.
The picked shot gathers have been processed both with layer-based interpretation (ABC
or the plus-minus method, Sjögren 1984) and seismic refraction tomography (SRT). The
two methods complement each other very well. In a not too complicated environment, a
layer-based method is a robust tool for estimating the depth to bedrock and detecting vertical zones in a hard rock environment. For optimal results with this method, the ground must
have homogeneous layers, sharp boundaries, a velocity that strictly increases with depth, and
layer thicknesses that allow calculation of layer velocities.
The SRT method is more general and can sometimes be successful even if these conditions are not met. The result from SRT provides a gradually increasing velocity model with
the best possible fit to the actual data. Firstly, we use it for quality control of picks, even in
projects where the primary method is layer-based interpretation since it highlights picks that
are not in agreement with the majority of the data set. It is an excellent method in most “soft
rock” environments where a gradual vertical change in velocity is expected. However, it is
inherently inadequate when sharp layer boundaries are found, e.g., depth to crystalline bedrock and small, weak zones in bedrock. It is normally necessary to optimise the geological
interpretation of the velocity model with reference information.
We find the combination of these methods, aided by geotechnical/geological reference
information, a powerful tool. Usually, the layer-based method is used to calibrate the interpretation of depth to bedrock and to delineate weakness zones. Disagreement in the methods
highlights areas where special attention is needed and provides indications of the subsurface
complexity and a better understanding of uncertainty.
SRT of the collected data was performed with DWTOMO from Geogiga Technology
Corp. The software was used for both the first-arrival picking of all data (see Figure 38.1c)
and the SRT (see Figure 38.1d).
The inversion method provides a gradual velocity variation both horizontally and vertically. The number of iterations is primarily established based on how the model adapts to the
measured first arrivals. To a certain extent, a geological perspective has also been taken into
account when choosing the number of iterations. In the case of geologically unreasonable
models with many artefacts, the inversion is stopped manually. In general, extra horizontal
and vertical smoothing has been used, and a more conservative adjustment is given than
what the default parameter in DWTOMO recommends.
For this project, a 3D interpretation model was developed using the software Seequent Oasis
Montaj. This 3D interpretation was based on SRT, layer-based interpretation, and information
from the geotechnical boreholes. The software allows interpretation between the different data
types and allows for a link between 2D viewing and a 3D environment (see Figure 38.2a).
After interpretation of the individual seismic profiles, gridding of the bedrock surface has
been performed using all available data. The surface is depicted in the 3D environment. It
has been considered whether a greater or lesser emphasis should be placed on specific boreholes and whether interpretations of the seismic profiles should be changed. The gridding
process was repeated until all involved geoscientists agreed that it was acceptable.
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Figure 38.2 (a) 3D overview of results from Nedre Silkestraa. The overview contains SRT profles
(coloured surfaces), boreholes with depth to bedrock (purple symbol), and the combined
interpretation of the bedrock surface shown with transparent grey colour. (b) Level of tunnel extents (purple and red lines), terrain (blue) and interpreted bedrock (green). In this
specifc route, the planned tunnel was covered by more than 10 m rock.
All seismic profiles were interpreted subjectively, with the main emphasis on the tomographic velocities. During the interpretation, there was a focus on 3D geometry from
intersecting lines and neighbouring lines, as well as boreholes. By directly linking to a
3D environment, there were good conditions for assessing where 3D effects played a role,
thereby allowing an interpretation that corresponds better with reality.
The primary purpose was to provide information about the thickness of bedrock above
different tunnel routes. Figure 38.2b shows the final drawing made for the tunnel engineers,
where bedrock coverage on top of the tunnel is clearly presented.
ACKNOWLEDGEMENTS
The authors would like to acknowledge BaneNOR for their progressive thinking in this project and for allowing us to publish this material.
Chapter 39
Case: screening for ground risk
ahead of tunnel design and
construction activities
James Russell and Edward Cox
39.1
BACKGROUND AND OBJECTIVE
The construction of a roughly 250 m deep, 37 km long transport tunnel was planned as
part of a proposed polyhalite mine in North Yorkshire, UK. As part of the design process,
a geophysical investigation was identified as a mechanism to build a more detailed ground
model so that potential areas of subsurface risk could be considered well in advance of any
tunnel boring. The main objectives were to define the upper and lower boundaries of the
stratigraphic unit in which the tunnel was to be constructed and to determine the presence of
fault and deformation zones within the planned tunnel interval.
Discontinuities and irregularities within the subsurface need to be carefully considered
when planning a tunnel excavation. Failure to accurately define zones of risk and not fully
understanding the ground conditions can lead to significant unforeseen but potentially foreseeable issues, not only during the excavation of the tunnel but also for any secondary works,
such as side tunnels and ventilation shafts. These issues can result in substantial financial
losses, health and safety risks, and significant delays in the project.
39.2
SURVEY PLANNING AND CONSIDERATIONS
For a project of this size and in order to adequately survey the depths required, seismic
reflection was chosen as the most suitable method of acquisition. An initial ground model
was established using legacy data from the British Geological Society (BGS). The subsurface geology predominantly comprised sandstone and mudstone formations, with the Redcar
mudstone identified as the stratigraphic layer through which the tunnel would be bored.
From 2017 to 2018, Fugro acquired approximately 90 km of seismic data along discrete
profiles following the tunnel route, generally along roads, tracks, and fields. A light-footprint
vibroseis seismic source was chosen for acquisition (Figure 39.1a), as it provided a powerful
and repeatable source with sufficient energy and frequency content to provide suitable imaging at the target depths. Roughly comparable in size to a medium-sized agricultural vehicle,
the 8 t vibroseis unit is a low-energy-density, low-impact source activated over a long time
period (swept frequency). Compared to using explosives, which are a high-energy-density
source activated briefly, the vibroseis has a small footprint and causes little disturbance to the
surrounding environment. This was a significant consideration for this project as the survey
lines were to pass near residential and protected areas. In the end, vibroseis was used for
98% of all the survey lines, essentially eliminating the need for explosives, and was able to
provide enough power to reliably image up to 1.5 km below the surface.
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Figure 39.1 (a) Wireless seismic receivers capture the transmitted signal from a vibroseis seismic
source refected from the subsurface to yield seismic-refection imagery.The combination
of these technologies provided a low environmental impact approach for the tunnel investigation and minimal disruption to local communities.An equivalent investigation based on
a traditional explosives approach could have involved about 17,000 shot holes and up to
10 t of explosives. (b) Pre-construction geophysical screening can be invaluable in helping
manage risks of tunnel boring machine operations by confrming the relative position of
potentially challenging geological conditions such as fault zones, as interpreted from seismic data. Marker horizon in yellow is the base of the unit hosting the future planned tunnel
(Redcar Mudstone Formation).The interpreted seismic data examples show no evidence
of deformation (left) but evidence of minor deformation (centre) and signifcant deformation (right), corresponding to similar deep-seated features identifed in legacy seismic
exploration data. Signifcant deformation at the tunnelling interval was generally associated
with deeper-seated structure.Tunnel route is within plane of seismic profles (left, centre)
and oblique to plane of profle (right). (Images with permission of Sirius Minerals.)
Screening for ground risk
245
All the survey lines were collected using the same acquisition parameters. Wireless seismic receivers were deployed along the route with 5 m spacing. “Shots” were then acquired
between each receiver, yielding a maximum fold of 100 (100 × oversampling of each subsurface reflection point). The light-footprint, cable-free acquisition strategy meant that the
majority of fieldwork could be efficiently completed with small field crews (5–6 members)
with minimal impact on local communities and stakeholders.
39.3
RESULTS AND CONCLUSIONS
Processed, depth-migrated reflection data were imported into a 3D digital interpretation
environment.
An initial conceptual model, based on the British Geological Survey (BGS) surface mapping and existing deep exploration seismic data, highlighted the presence of faulting but
with only conjectural information relating to the tunnelling interval (Figure 39.2). The Redcar mudstone was estimated to be roughly 250 m thick, with the upper boundary around
50–300 m below the surface.
Key marker horizons were identified and traced along each seismic line with reference
to available legacy borehole information. Analysis of the data indicated clear and coherent
marker horizons at depths greater than the anticipated tunnel level. Interpretation of these
deeper interfaces proved key to gaining a better understanding of the geological structure,
particularly faulting. It supported the interpretation of shallower features and interfaces
within the tunnel zone of interest, where direct interpretation of the seismic was more challenging. This highlighted the benefit of an investigation strategy that yielded data at depths
beyond the initial target, a lesson learnt for future studies.
Particular focus was given to identifying structure, discontinuities, and zones of disturbance within the tunnelling interval. These zones were identified where the geometry of the
reflectors was disturbed, such as a break in the reflector. Another key indicator was a sudden
change in the dip of the reflectors. Even if the reflectors were still well defined and showed
no visual evidence of breaks, clear dip variations were considered likely to be deformation
zones (Figure 39.1b).
Once the integrated information had been fully interpreted, the route was categorised into
three classes:
1
2
3
No evidence of deformation within the tunnel horizon
Evidence of minor deformation
Evidence of significant deformation
Interpretation of the seismic data, coupled with legacy seismic data, was then used to
build a revised predictive ground model, which was significantly more representative
than the previous version. Alongside this, a linear risk map was built. This highlighted
the zones along the tunnel route that were interpreted to be most likely to comprise
disturbed rock strata. Once identified, further analysis was completed using boreholes
and wireline logging to thoroughly test the geotechnical properties of key intervals.
Delivering an integrated representation of the subsurface allowed the project designers
to be significantly more aware of the geotechnical risks along the route of the tunnel
and allowed for extra preparations to be made when tunnelling through zones of known
deformation.
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Engineering Geophysics
Figure 39.2 Initial ground model for the MTS tunnel route largely based on British Geological Survey information. Note the presence of largely vertical faults
throughout. (ARUP; image with permission of Sirius Minerals.)
Screening for ground risk
247
Figure 39.3 Upper part: Linear risk map of deformed strata based on the 2017/2018 Fugro seismic
campaign. Zoning of the tunnel route focused risk mitigation decisions on those parts of
the route with signifcant deformation within the tunnelling interval. Lower part: Revised
ground model and linear risk map derived from BGS mapping, existing exploration seismic data from a public database, and interpreted data from the 2017/2018 Fugro seismic
campaign.
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Engineering Geophysics
To summarise, the geophysical investigation (Figure 39.3):
•
•
•
Verified the client’s decision, prior to the current seismic investigation, to realign the
tunnel route to avoid specific fault systems;
Delivered significant reduction in investigative effort (cost and time) by the accurate
targeting of fault zones for drilling, followed by fault characterisation; and
Delivered significant uncertainty reduction relating to subsurface conditions, including
geotechnical properties below a proposed tunnel route through a predictive representative ground model calibrated to boreholes.
To conclude, the application of seismic reflection proved pivotal in the design and preconstruction planning phase of the Woodsmith Mine tunnel. The investigative approach allowed
for a much more thorough ground model to be established, identifying key boundaries and
zones of subsurface deformation, which allowed identification and categorisation of areas of
risk along the proposed route.
Chapter 40
Case: geometrical complex
ground model for large industrial
construction sites: ultra-highresolution with shear waves –
qualification flow and application
Pier Vittorio Radogna and Christophe Vergniault
40.1
BACKGROUND AND OBJECTIVE
For construction projects in active seismic regions, it is important to understand earthquake
hazards and the location of active faults, in addition to the thickness of layers of variable
strength for dimensioning. Somewhere, soft soils can be found at great depth and pose a
risk for shallow and deep foundations. The cases described here represent such complicated
cases and demonstrate how a careful testing phase and subsequent application of an ultrahigh resolution (UHR) seismic shear wave (S-wave) survey successfully resolved soil conditions down to 50 m depth.
40.2
PLANNING CONSIDERATIONS
For the construction sites located in active seismic areas, detailed information to a depth of
50 m was requested.
Seismic methods (e.g., reflection, refraction, and surface wave analysis) can provide
meaningful insights in the context of subsurface modelling. Each method has benefits and
limitations. Some provide attributes of the subsurface units, e.g., body wave velocities (seismic refraction), and others provide the geometry of discontinuities, like layer boundaries and
faults, due to acoustic impedance variations (seismic reflection).
Seismic refraction remains a good compromise between quality and price to define the
geometry of the substratum and identify the water table. However, it does not detect velocity
inversions (e.g., soft soil layers under hard ones) or sub-vertical discontinuities (e.g., faults),
both expected in the presented study areas. Surface waves (MASW) measures valuable
parameters for geotechnical analysis, such as shear wave velocity and detection of velocity
inversions. Nevertheless, the resolution is too low to finely image geological features or a
specific structure.
Near-surface seismic imaging (< 50 m) is an application domain of ultra-high resolution
(UHR) seismic. However, the difficulties encountered in separating the different types of
waves (reflected, refracted, surface, direct) in the first tens of milliseconds is still a challenge. In addition to these difficulties, a reflected seismic P-wave has a low vertical resolution (ability to distinguish two consecutive strata).
On the contrary, S-wave reflections have much shorter wavelengths (in the order of a few
metres), making them suitable for very shallow reconnaissance. Despite this advantage, their
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use has been much less frequent in Europe than that of P-waves due to the short spacing
required between geophones (which needs a large number of sensors, making surveys more
expensive), the use of horizontal geophones, and the use of polarised seismic sources. For
the presented surveys, the need for high resolution, though, called for S-waves.
40.3
PROJECT PLANNING
The Techniques of Realisation and Expertise in Geology and Civil Engineering Group
(TEGG) of EDF (the French multinational electric utility) started the method validation process for UHR seismic with shear waves in the horizontal plane (UHR-S) in 2019. The qualification process flow consists of three phases, setting up a numerical model, model test, and
field trial (Figure 40.1a). The model test was validated in 2019 in cooperation among EDF,
Realtimeseismic, Geo2X, and Cagliari University on a model in Cagliari (Figure 40.1b).
This study made it possible to generate a seismic image from a depth of 3 m on both a 2D
profile and a 3D mini block (Vergniault et al. 2019).
The practical testing of the UHR reflection seismic survey with shear waves in the horizontal plane was conducted for a project in central Italy as a field trial to detect faults in
quaternary sediments of the Fucino basin (Figure 40.1b), very well known for its recent
seismicity. The survey was carried out in November 2019.
(a)
(b)
FRANCE
Case 3
Fucino
ITALY
Cagliari
Figure 40.1 (a) Ultra-high-resolution refection seismic with shear wave qualifcation fow. (b) Model
test, feld trial, and application area.
Geometrical complex ground model
251
The Fucino basin (Central Italy) is one of the most extensive intra-mountain alluvial
plains in the Apennine Mountains. It has a tectonic origin related to the presence of important systems of faults located in its northern and eastern edges. Some of these faults are still
active and capable of generating strong seismic events (e.g., January 13, 1915, Avezzano
earthquake, Ms magnitude of 7.0). To increase the understanding of the risks related to seismic activity at the site, mapping of active faults is of interest. Furthermore, in a sedimentary
basin, the distribution of soft layers is relevant as they may constitute a risk for foundations.
40.4
ACQUISITION
UHR-S profiles have been located along well known recently active faults directly south of
the city of Avezzano (the area most damaged during the destructive earthquake of 1915) to
confirm the capabilities of the system and to obtain more information on the structure.
The shear waves were generated by a vibrator source mounted on a wheelbarrow (Elvis VII
© GEOSYM) (Figure 40.3d). The horizontal wavefield was captured by the 14 Hz horizontal
geophones and recorded with an 8 GEODE Seismograph (© Geometrics) system (Figure 40.3c).
To target a vertical resolution of around 50 cm and a depth of investigation of 50 m, the
SH (horizontally polarised) seismic array length was 192 m, geophone spacing (cross line)
was 0.5 m, and there was 1 m between shots. The sweep was linear (20–160 Hz). A total of 4
profiles were acquired. Six technicians were in charge of the acquisition, which took 4 days.
40.5
PROCESSING
Processing UHR-S data requires specific skills and software that are yet not widespread in
the context of large civil engineering projects and seismic hazard analysis. The workflow
is complex and may require numerous operations by specialists. An example is listed here.
The key points of processing are the static correction, locating data correctly relative to the
topography, and the muting of refraction and surface waves, which removes signals irrelevant to the S-wave reflection. The estimated time spent for the entire processing is 2 weeks.
1 Geometry building and QC
2 Definition of statics model using several ways: MASW analysis, refraction tomography,
geological information, and constant velocity stacks
3 Surface waves filtering in FK domain; variable inside and outside mute with offset
4 Stacking velocity picking
5 Surface-consistent reflection residual static corrections
6 Random and coherent noise attenuation
7 Amplitude balancing
8 Surface-consistent deconvolution (prediction lag: 2 ms, operator length: 60 ms)
9 Time variant spectral whitening (TVSW) (20–160 Hz)
10 FX Dip filtering (rejection velocity 100 m/s, spatial windows: 60 traces, F-X operator
length: 15 traces)
11 Bandpass filter (15–20 to 160–200 Hz)
12 AGC (100 ms)
13 RMS migration velocity picking
14 Kirchhoff Pre-STM (aperture: 40 m, offset 40 m, stretch limit: 80, mute angle: 88°)
15 Post-migration processing (trim statics, FX dip filter AGC) (trim: 4 ms, rejection velocity 100 m/s, spatial windows: 60 traces, F-X operator length: 15 traces)
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Engineering Geophysics
40.6
RESULTS AND CONCLUSIONS
40.6.1
Results Fucino
UHR-S images the upper 10 m of the subsurface, with a high resolution allowing the recognition of geological features related to the recent tectonic activity (Figure 40.2b). The
geophysical section corresponds well with the existing geological trench section across the
structure (Figure 40.2a).
meter
1
2
3
4
(a)
Legend: 1, ploughed zone; 2, clayese silt; 3, silt and sandy silt; 4, sand and sandy
silt; 5, sandy-gravelly debris; 6, gravel; 7, peaty silt; 8, peat; 9, lacustrine shell;
10, pottery and other artifacts; 11, bones; 12, fault zone; 13, normal fault; 14,
fracture; 15 unconformity; 16, dated sample; A and B, evidence for paleoseismic
events.
0
meter 0
ms
10
20
(b)
Figure 40.2 (a) Fucino: Trench log across rupture area (Michetti 1996). (b) Fucino UHR seismic with
shear waves stack.
Geometrical complex ground model
40.6.2
253
Results Case 3
In summer 2020, EDF acquired UHR-S profiles in France on 9 sites on the Cevennes Fault
System (Case 3, Figure 40.1b), suspected to be active. The purpose was to identify areas
to prioritise for trenching. Technical specifications as optimised on the Fucino site were
applied. Geometries and tectonic features of the resolved ground model obtained by UHR-S
(Figure 40.3a) correspond perfectly to the conceptual geological models of the area (Figure 40.3b) and allow for very precise dimensioning.
(a)
(b)
P.H. Leloup d’après la carte géologique Aubenas (Elmi et al., 1996)
(c)
(d)
Figure 40.3 (a) Case 3 UHR seismic with shear waves stack. (b) Site 3 (France) geological cross section.
(c) UHR-S requires exact location of geophones and shot points. (d) Mobile electrodynamic shear wave vibrator system Elvis VII – Geosym.
254
40.7
Engineering Geophysics
CONCLUSIONS AND RECOMMENDATION
Ultra-high resolution shear waves in the horizontal plane (UHR-SH) have proven capable
of producing a relatively fine image of the near-surface (<5–50 m). The cases presented
illustrate how UHR-SH can resolve geological structures, including faults, lateral variation
of layers, anticlines, and synclines. Erosional features would have been resolved as well
had they been present. The mapping of these structures, which are potential geohazards for
nearby developed areas, allows for proper measures to be considered in the pre-construction
phase of civil engineering projects.
However, to obtain a reliable result, it is vital to carefully analyse the acquisition parameter (offsets length) and to possess expertise in data processing (e.g., static corrections, NMO
velocity, and mute).
ACKNOWLEDGEMENTS
The authors are very grateful to Daniela Famiani (INGV) for supporting the Fucino survey.
All surveys were contracted by EDF and acquired by Geo2X.
BIBLIOGRAPHY
Ampuero, J. P., L. Audin, P. Bernard, et al. 2019. Rapport d’évaluation du groupe de travail (GT)
CNRS-INSU sur le séisme du Teil du 11 novembre 2019 et ses causes possibles. Rapport de recherche. Institut National des Sciences de l’Univers.
Deidda, G. P., and A. R. Balia. 2001. An ultrashallow SH‐wave seismic reflection experiment on a
subsurface ground model. Geophysics:1097–1104.
Fabien-Ouellet, G., and R. Fortier. 2014. Using all seismic arrivals in shallow seismic investigations.
Journal of Applied Geophysics 103: 31–42. ISSN 0926-9851
Famiani, D., F. Cara, G. Di Giulio, et al. 2016. Ambient vibration analysis as a proxy for the detection
of buried geological structures. 35th General Assembly of the European Seismological Commission
Conference, Trieste 2016. https://www.35esc2016.eu/
Kerrien, Y., S. Elmi, R. Busnardo, G. Camus, G. Kieffer, J. Moinereau, and A. Weisbrod. 1989. Carte
géologigue de France (1/50000), Feuille Aubenas (865). Notice explicative par S. Elmi, R. Busnardo, B. Clavel, G. Camus, G. Kieffer, P. Bérard, and B. Michaëly, 1996, 170 p. Orléans, BRGM.
Michetti, A., et al. 1996. Trench investigations of the 1915 Fucino earthquake fault scarps (Abruzzo, central Italy): Geological evidence of large historical events. Journal of Geophysical Research
101:5921–5936.
Park, C. B., R. D. Miller, and H. Miura. 2002. Optimum Field Parameters of an MASW Survey.
Expanded Abstracts: Society of Exploration Geophysicists-Japan.
Pugin, A., K. Brewer, T. Cartwright, et al. 2013. Near surface S-wave seismic reflection profiling – new
approaches and insights. First Break 31.
Vergniault, C., C. Strobbia, G. P. Deidda, and D. Dupuy. 2019. Surface seismic methods comparison
to image the near surface basement. Journées scientifiques de l’AGAP Qualité, du 28 au 30 octobre
2019.
Chapter 41
Case: identifying weakness zones
and geological boundaries across
tunnel alignments using airborne
electromagnetics
Anne H. Rasmussen, Giselle M. Linares,
Craig W. Christensen, and Martin Panzner
41.1
BACKGROUND AND OBJECTIVE
Unforeseen challenging ground conditions are a significant obstacle to infrastructure development, including tunnel construction (Beckers et al. 2013). Addressing this risk with traditional, intrusive ground investigation methods, like geotechnical soundings, can be costly,
time-consuming, and technically challenging. This section presents the use of airborne (helicopter-based) time domain electromagnetics (AEM) for more efficient site investigation prior
to large tunnelling projects. AEM can be used to map variations in electrical resistivity in the
subsurface, which often correlate with lithology, rock quality, and the presence of fractures
and weakness zones. When available, we combine the geophysical data with ancillary data
sets for a more sophisticated interpretation. Integration techniques range from simple clustering analysis to customised machine learning methods that automatically detect geological
interfaces like faults or the top of bedrock (Lysdahl et al. 2018). We illustrate the strengths and
weaknesses of airborne EM for tunnelling projects, using two case examples from northern
India. Further, we identify three key insights that airborne EM can provide to tunnelling engineers: detecting major fractured zones, weak rock units, and solid rock cover. These insights
can be highly valuable for tunnel design and construction projects worldwide. Some of the
content of this case study has previously been presented by Rasmussen et al. (2021).
41.2
SURVEY PLANNING AND CONSIDERATIONS
Airborne EM was employed for two high-elevation road tunnel projects in the Sind Valley
in the Jammu and Kashmir region in northern India. The first, a 6.5 km long tunnel, was
under construction, whereas the second, a 14 km long tunnel, was still in the planning and
design phase. AEM was undertaken in the first case primarily as a technology demonstration
and verification of the method, whereas in the second case, it was used to support ongoing
ground investigations. Ground truth data are sparse in both cases. At both sites, bedrock consists of low- to medium-grade metamorphic rocks. The Zojila Formation (slate, schist, and
phyllite) and Panjal Trap Formation (metabasite) occur at both sites, whereas agglomerative
slates (slates) only occur at the second site. In both cases, the primary application of airborne
EM was to map the occurrence of weak, phyllitic rock.
For this project, we utilised the SkyTEM-312 time domain electromagnetic (EM) system, acquiring data with 2 different source dipole moments (Sørensen and Auken 2004):
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a low moment with 4100 Am2 and a high moment with 450,000 Am2. That allows for a
more accurate resolution of the shallow subsurface using early time data measured at a
low moment. At the same time, we can achieve good depth penetration from the late time
data with good signal-to-noise ratios, as they were measured at the high dipole moment.
The EM system was suspended 40 m below an Indian Air Force (IAF) helicopter and
flown at an altitude of 30–150 m above ground at a speed of 80–120 km/h. System altitude, roll, and pitch were continuously measured during the survey. In total, 160 line-km
of EM sounding data were acquired along several semi-parallel lines with a line separation of 50–200 m. The survey region is dominated by a very steep topography making
ground-based geophysical or geotechnical investigations difficult to impossible. However,
the entire tunnel project area could be mapped within 3 days using an airborne system.
41.3
RESULTS AND CONCLUSIONS
The AEM data processing was done using the software package Aarhus Workbench (Auken
et al. 2009) and consisted primarily of altitude processing, stacking of the acquired transient
measurements, and the removal of data affected by noise and coupling effects from interference with existing infrastructure. The AEM soundings were inverted simultaneously using a
Gauss–Newton-type inversion code with a Marquard modification, AarhusInv (Auken et al.
2015). The algorithm seeks for a pseudo-3D resistivity model that varies smoothly in both
the vertical and lateral directions, referred to as spatially constrained 1D inversion (SCI).
The vertical resolution of the resistivity models ranges from metres near the surface to several tens of metres at depth, and the lateral resolution varies between 50 and 150 m. Based
on the data sensitivities computed by the inversion algorithm, the depth of investigation DOI
(Christiansen and Auken 2012) was estimated to be between 300 and 600 m, depending on
local geological conditions.
41.4
EXAMPLE 1
Results of the AEM data analysis and inversion revealed that the central portions of this
partially excavated tunnel consist of mainly very high-resistivity rock (>1000 Ωm), whereas
the portal areas contain either medium- (100–400 Ωm) or low-resistivity rock (< 50 Ωm)
as shown in Figure 41.1c and d. Low-resistivity zones correlated well with the major shear
zones detected near the boundaries between the Zojila and Panjal Trap Formations (Figure 41.1a
and b). The high-resistivity units detected in the central part of the tunnel corresponded with
the stronger rocks of the Panjal Trap Formation. A direct comparison of resistivities and geotechnical cross sections was impossible in some central parts of the tunnel (chainage 9000 m)
because the helicopter could not fly directly above the tunnel alignment due to very steep terrain. In addition, a near-surface transition from higher to lower resistivities is visible in some
areas and appears to correlate well with the expected water table in the area (Figure 41.1d).
The results also agree with observations made by geologists on site: in the western and eastern
portal areas, weak rock was encountered during drill and blast excavation, slowing down the
progress of construction. In contrast, excavation in the central part of the tunnel proceeded
well in good-quality rock.
Weakness zones and geological boundaries
257
Figure 41.1 Example 1: (a) Geological map of example area 1. (modifed after Grusamar [2017].)
(b) Geological cross-section along the tunnel alignment (modifed after Grusamar [2017]).
(c) Slice of the AEM-derived resistivity model at a depth of 200 m below terrain.
(d) Resistivity profle along the same section.
Source: Figures modifed after Rasmussen et al. (2021)
258
41.5
Engineering Geophysics
EXAMPLE 2
Based on the observed correlations in example 1, we visualise low- and high-resistivity
units as a proxy for zones of strong and weak rock at the second site (Figure 41.2). Specifically, we visualised volumes of material with >1000 Ωm and volumes with < 100 Ωm
alongside the tunnel alignment in 3D. We identified 4 units of high-resistivity (A, B, C,
Figure 41.2 Example 2: (a) 3D view of the entire resistivity model and the tunnel alignment (white line);
(b) zoom on the central section of the model. Resistivity volumes >1000 Ωm (yellow) and
<100 Ωm (dark purple); the discrete colour bar with rock units refers to lithology in the
boreholes. (c, top) Map of the maximum extent of the high-resistivity Units A and B and
(c, bottom) low-resistivity units R and Q. (d) Cross section of part of the resistivity model
and planned tunnel alignment.
Source: Figures modifed after Rasmussen et al. (2021)
Weakness zones and geological boundaries
259
and D [see Figure 41.2a]) and 3 low-resistivity units (Q, R, and S [see Figure 41.2a]). By
visualising these units alongside the planned tunnel alignment in 3D (Figure 41.2a and b),
we see the alignment appears to have a suitable horizontal placement. For instance, to the
west, between a chainage of 5–7 km, the tunnel avoids low-resistivity unit R (Figure 41.2c),
which is more likely to be weak rock. However, some adjustments to the vertical alignments might be warranted. The tunnel is positioned near the lower limit of Unit B and Unit
C (Figure 41.2d). Raising the tunnel elevation a bit may prove advantageous to avoid deep,
low-resistivity rock units.
REFERENCES
Auken, E., A. V. Christiansen, C. Kirkegaard, et al. 2015. An overview of a highly versatile forward
and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric
data. Exploration Geophysics 46(3):223–235.
Auken, E., A. Viezzoli, and A. Christiansen. 2009. A single software for processing, inversion, and
presentation of AEM data of different systems: The Aarhus Workbench. ASEG Extended Abstracts
2009.
Beckers, F., U. Stegemann, N. Chiara, A. Flesch, J. Maly, and E. Silva. 2013. A risk-management
approach to a successful infrastructure project. McKinsey Working Papers on Risk, Number
52. https://www.mckinsey.com/business-functions/operations/our-insights/a-risk-managementapproach-to-a-successful-infrastructure-project?cid=eml-web
Christiansen, A., and E. Auken. 2012. A global measure for depth of investigation. Geophysics
77(4):WB171–WB177.
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no. DNDA/ADV/1029-Z MORH/2017/ 6_2, DNDA/ADV/1029-Z MORH/2017/ 6_3. The original
drawings were prepared by Grusamar Ingeniería y Consulting for the National Highways & Infrastructure Development Corporation Ltd. (NHIDCL), Ministry of Road Transport & Highways, Government of India.
Lysdahl, A. K., L. Andresen, and M. Vöge. 2018. Construction of bedrock topography from AirborneEM data by Artificial Neural Network. In Numerical Methods in Geotechnical Engineering IX.
London: CRC Press, pp. 691–696.
Rasmussen, A. H., G. M. Linares, C. W. Christensen, S. Malik, G. H. Skurdal, and A. A. Pfaffhuber.
2021. Airborne geoscanning as a site investigation tool in large-scale tunnelling projects: A synthesis of case studies from Norway and India. Presented at the 11th Asian rock mechanics symposium.
IOP Conference Series: Earth and Environmental Science 861:042039.
Sørensen, K. I., and E. Auken. 2004. SkyTEM – A new high-resolution helicopter transient electromagnetic system. Exploration Geophysics 35(3).
Chapter 42
Case: delineation of soil type, dam
leakage, underground voids, and
water flow in tunnels
Michael Maxwell, Robert Eso, and Brodie Klue
42.1
GENERAL BACKGROUND AND OBJECTIVE
The 3D electrical resistivity imaging (ERT) method has been applied over the last two
decades to assist in solving complex engineering and environmental problems. The investigations have included delineation of soil stratigraphy and types, tailings and power dam
leakage paths, underground water- and air-filled voids and water flow. We initiated this
work to better resolve offline resistivity variations and to improve 3D volume imaging
due to the limitations of pseudo-3D using multiple 2D ERT parallel lines (see Sections 6.5
and 6.6 in Chapter 6). 3D distributed arrays allow the placement of electrodes “around”
features of interest either in the half-space (i.e., from the surface) or in the whole space
(underground or borehole deployment) by placing electrodes in x-y-z-planes. Modern multichannel electrical resistivity systems provide fast data collection rates which facilitate
3D surveys with large multiple electrode layouts (up to 400 and more electrodes in some
surveys). Furthermore, with improvements in inverse modelling, half space and whole
space model solutions are achievable in a reasonable time (days) using local computers,
cluster, or cloud facilities.
We first deployed 3D layouts in underground potash mines to delineate voids and water
flow within the salt body 1000 m below ground level (bgl). We had limited penetration
depth into Canadian prairie evaporites with the more standard approach of GPR in underground salt mines. Because of the high-resistivity contrast between wet and dry salt (orders
of magnitude), we deployed test 2D ERT layouts that were very successful in delineating
wet layers and zones within the salt (Maxwell et al. 2005). Typically, horizontal mine tunnels (drifts) are on a single depth plane and provide horizontal variation in one direction
(e.g., x) but are limited in the y-direction for electrode placement except when crossing
or parallel drifts are available. Vertical tunnels (raises) and boreholes can provide vertical
variation to improve the 3D geometry of data collection. With limited tunnel geometry, we
extended to 3D layouts to improve imaging around and between tunnels. We have placed
electrodes in available drifts on a single plane at the mine level using multiple parallel
lines in single tunnels with widths of up to 20 m, using rings of electrodes around tunnel
circumferences and parallel and crossing tunnels when available. As well, when possible,
we have used raises and available boreholes or drilled boreholes located to improve the
3D geometry of the layouts.
DCIP3D software (UBC-GIF) has good capability for whole space modelling and has
been used for the majority of whole space modelling that we have completed. We have used
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ERTLAB and RES3DINV for some work as well. The results have been very successful,
as confirmed by follow-up invasive investigation and grouting at numerous sites. With the
success of the underground surveying, which has continued for many years, we extended the
work to 3D layouts at the surface in the half-space and using boreholes and mine shafts to
image whole spaces. Examples of the investigations include surveying earthen power and
tailings dams to delineate leakage paths and dam materials and surveys of mine shafts and
surface sites. The approach has successfully clarified flow paths, contaminant plumes, and
voids at soil and hard rock sites.
In this case history discussion, we provide an overview of problems that have been investigated with 3D distributed arrays and the results from some of the surveys.
42.2
UNDERGROUND POTASH MINE WATER AND VOID
DELINEATION
42.2.1
Survey planning and considerations
Site Conditions: As noted, we have conducted 3D ERT surveys using available mine openings in underground salt mines. We deploy layouts using 96 to greater than 400 electrodes in
the back (ceiling), walls, floors, and shafts of mines. The reason for this work was to provide
better imaging of large volumes of salt deposits to delineate water flows and stratigraphy
around the openings. Considerations of the underground surveys included effects of metallic
infrastructure and cabling as well as high contact resistances for electrodes. Occasionally
metallic infrastructure affected localised data (e.g., electrical equipment or new metallic
pipes). However, most infrastructure appeared to be electrically isolated, especially due to
major oxidisation (rusting). It was also found that as long as contact resistances were within
two orders of magnitude for the complete set of electrodes, measured data were good even
when the contact resistances were as high as 100 to 400 kΩ. This result was calibrated in
areas of known geology and water inflow. Examples of the test data are included in separate publications (e.g., Maxwell et al. 2005). We consider three representative underground
examples, imaging of an overhead water flow in a potash/salt deposit, imaging of voids and
water surrounding a concrete shaft in sedimentary rock over 600 m below ground surface,
and imaging of water flowing behind a near-surface concrete shaft. All these sites have been
successfully mitigated with grouting using the geophysical data for guidance at a significant
saving to the mining companies.
Water Flow in Salt: Typically, we utilise electrodes spaced at 5 m along lengths of multiple tunnels to “surround” areas of interest, multiple parallel lines in wider tunnels, or multiple rings of electrodes placed around the openings. We use extension cables to connect to
sets of electrodes in parallel or nearby drift openings, raises, and boreholes to improve the
3D geometry. Surveyed volumes of interest have mostly been several hundred metres by
100–200 m × 50–100 m or cylindrically ~30+ m around tunnels or shafts. Stainless steel
electrodes are placed in small diameter drilled holes with wraps of cellulose “white wipes”
saturated with saltwater (easy to do in an underground salt mine). Data can be collected during mine operations and often on a 24 h basis over periods of 2–5 days. We generally collect
60,000–100,000 data points for large-scale surveys in comparison to 2D surveys with typically less than 5000 data points (see Table 6.1 in the Method section).
Delineation of soil type
42.2.2
Cases and results using underground 3D
ERT surveys
42.2.2.1
Overhead water flow above potash/salt deposit
263
Objective: An area of a potash mine at 1000 m depth experienced long-term water inflows
through overhead salt and sedimentary rock. Thus a more detailed mapping of the inflow
area was needed.
Acquisition: 2D and 3D surveys have been conducted over many years to assist in
grouting areas of inflow using the typical 5 m electrode spacing and arrays of 120 to 216
electrodes with multiple layouts in the backs and walls of available drifts, raises (vertical
opening above mining level), and often short boreholes into tunnel walls. The 3D surveys
(over 20 surveys) were completed as previously described, and DCIP3D software has been
used to interpret conductive zones.
Results: Figure 42.1 shows an example of interpreted conductive zones in a slice of a 3D
model volume. The blue zones are conductive features associated with water-filled voids or
Figure 42.1 Results of underground 3D ERT survey.The interpreted conductive zones are viewed in a
slice of a 3D model volume.The blue zones are conductive features associated with waterflled voids or wet salt within dry salt.The straight (red) lines indicate survey electrode lines.
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wet salt within dry salt. The straight (red) lines indicate electrode lines. Subsequent mitigation work initially included borehole grouting and eventually full-scale development of mine
openings into the conductive features confirming the geophysical results. More information
on this example can be seen in Maxwell et al. 2005 based on the initial 2D surveys and Eso
et al. (2006–1 and 2006–2) for additional 3D surveys.
42.2.2.2
Underground shaft in sedimentary rock
We have completed work in mine tunnels and shafts (near-surface and in deep underground
locations) using circumferential electrode layouts along lengths from 100 to 200 m. We provide an example of a deep underground shaft location.
Objectives: During the sinking of a shaft, a fractured rock aquifer was encountered at
approximately 800 m bgl. In order to mitigate this problem, an entry was cut from the shaft
above the aquifer zone, and a ring tunnel was cut around the shaft. A ring of boreholes was
drilled into the fractured rock aquifer, and the zone was partially grouted for approximately
2/3 of the ring circumference before the operation was stopped to deal with other mining
operations. A 3D ERT investigation was undertaken by placing electrodes within the shaft
to evaluate the success of the grouting and to delineate wet zones after approximately three
decades of operation.
Acquisition: Electrodes were placed at 3 feet spacing along 3 vertical lines spaced equidistantly around the shaft (approximately 120°), extending from the elevation of the grouting
opening to below the aquifer for 417 feet (approximately 370 electrodes). A steel liner had
been placed in the aquifer zone, and this section (50 feet) did not have electrodes. Electrodes
were 15 cm hollow steel rods with conductive grease injected in the middle, resulting in low
contact resistance in the concrete. The short electrodes also allowed continued operation of
the hoist in the shaft. Various combinations of the electrodes were used for current injection
and potential voltage measurement over approximately 5 days of 24-h survey. Results were
evaluated each day to design sequence files to optimise data collection. A total of approximately 140,000 data points were collected.
Results: DCIP3D software was used to interpret the data. Figure 42.2 shows interpreted
conductive and resistive features over the surveyed shaft section. The results indicated that
the third of the shaft section that had not been grouted showed a zone of higher conductivity
(yellow solids). In follow-up mitigation work, this section was grouted along with other features revealed in the area of original boreholes to have lost grout. The survey and follow-up
mitigation successfully reduced water inflow into the shaft.
Additional information can be seen in Cisyk et al. (2014), with an additional example of
a near-surface shaft in Cisyk et al. (2016).
42.3
SURFACE 3D ERT SURVEYS
3D ERT profiling has been used for surveying from the surface using a range of layouts,
including regularly spaced grid arrays and layouts surrounding features of interest using
extension cables to link separated electrode sections. We have targeted earthen dams, lined
ponds, and landfills in this work to delineate potential leakage paths and for due diligence
to evaluate whether leakage is occurring. We provide examples of an investigation under a
lined landfill, an earthen power dam, and a former coal mine.
Delineation of soil type
265
Figure 42.2 Results of underground 3D ERT survey showing the interpreted conductive and resistive
features over the surveyed shaft section.The results indicated that the third of the shaft
section that had not been grouted showed a zone of higher conductivity (yellow solids).
42.3.1
Lined landfill example
Objective: High conductivity in one monitoring well below an HDPE lined landfill indicated high conductivity at 10 m bgl and deeper. ERT was selected to identify possible causes
and conductive flow paths.
Acquisition: An electrical resistivity survey was planned to image bedrock and conductive soil or groundwater. The site soils and bedrock consisted of fluvial or drift deposits and
argillite rock. Data were collected on three 2D ERT profiles using 120 electrodes at 3 m
spacing over the approximate 400 m × 300 m survey area. One line was aligned between
two wells, including the high conductivity well, and additional lines were perpendicular and
adjacent to the boreholes. The 2D inversion models revealed a conductive zone between the
wells and extending upslope. 3D data were collected to assist in defining the extent of the
conductive zone. 60 electrodes were placed in three lines spaced at 10 m with 20 electrodes
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Figure 42.3 Results of 2D-3D ERT surveys including results from the 3D survey indicated by two slices
of the 3D Model showing the up-slope extent of the elevated conductive zone.
on each line at 5 m spacing and oriented perpendicularly to the profile connecting the two
wells (20 m × 100 m rectangular area). This approach can provide a relatively rapid method
to image a subsurface volume. The data collection was completed in less than 2 h. Pole–pole,
pole–dipole, and dipole–dipole data were collected using the 60 electrodes and remote electrodes. Remote electrodes (poles) were placed at the recommended 10 times the maximum
separation of electrodes on the lines (1 km).
Results: Interpreted results using RES3DINV (Loke) are shown in 2D slices of the 3D
model in Figure 42.3. The results reveal an extended conductive zone between the two wells
and the limit of the up-slope conductive zone providing the client with information for mitigation if required.
42.3.2
Earthen dams
3D surveys were completed at several large earthen power and tailings dams to delineate
potential or existing leakage paths. Some of the tailings facilities have been lined or partially
lined. We have deployed lines of electrodes along upstream and downstream surfaces and in
some cases have been able to use existing boreholes/monitoring wells. The areas surveyed
Delineation of soil type
267
Figure 42.4 Results of surface 3D ERT surveys showing an interpreted model of one earthen dam that
does not indicate any unexpected fow other than normal dam seepage and confrmation
of expected soil types in the dam.
typically span 200–400 m × 200 m with elevation changes of 40–100 m. We use 120 electrodes often redeployed to increase the area of coverage and with multiple extension cables
to connect electrode lines.
The work has been very successful at delineating potential flow paths as well as confirming the successful operation of lined facilities with no connection to external soil. Figure 42.4
shows an interpreted model of one earthen dam, which does not indicate any unexpected
flow other than normal dam seepage and confirmation of expected soil types in the dam.
This survey used 120 electrodes extending 400 m along the dam, 200 m across the dam, and
a 30 m elevation difference from the top of the dam to the lower reservoir (river). Multiple
surveys were completed using electrodes in parallel lines starting in the upper reservoir near
the dam face, on the upstream dam face, the top of the dam, the downstream dam face, and
the toe of the dam with some electrodes in the river below the dam. Other examples of delineated flow paths have not been published due to confidentiality.
42.3.3 Voids at former mine workings
Objective: Extensive geophysical surveys were completed over former coal mine workings
to delineate voids in the overburden and historic underground rock workings for the redevelopment of the former underground mine into an open-pit mining operation.
Acquisition: These surveys included 3D ERT surface arrays and cross-hole boreholes
where improved resolution with depth was required. Electrodes were placed at the surface
in grid or loop patterns, and cables with exposed electrodes were grouted into available
boreholes. The pole–dipole array type was primarily used as this allowed for optimal 3D
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Engineering Geophysics
Figure 42.5 Results of 3D ERT surface surveys combined with cross-hole results giving improved
resolution at depth. The anomalies (red) were correlated to borehole laser scans (grey),
as shown by the example of G01.The investigation results have been reported in Taylor et
al. (2010) and Taylor et al. (2011).
imaging with a large number of possible electrode quadrupole configurations. Grids used
96–240 electrodes with surface arrays allowing up to 1.5 ha of coverage with a single grid.
Results: The surface arrays were found to be most effective in the upper 15 m for imaging
shallow voids that pose a hazard to mining equipment. Figure 42.5 shows anomaly volumes
in plan and 3D model views. The anomalies (red) were correlated to borehole laser scans
(grey), as shown by the example of G01. The investigation results have been reported in
Taylor et al. (2010) and Taylor et al. (2011).
42.4
CONCLUSIONS
3D ERI/ERT surveys have seen increased use with successful delineation of 3D geological
complexity. The results improve subsurface conductivity/resistivity delineation compared
to 2D and pseudo-3D surveys, which have limitations for out-of-plane characterisation. The
results have been used to successfully mitigate water leakage and mine inflows over the past
20 years.
Delineation of soil type
269
REFERENCES
Cisyk, D. E., M. Maxwell, and R. A. Eso. 2014. Paper 154: Characterization of a water inflow fracture
system into a concrete mine shaft using geophysical and geological information. 1st International
Conference on Discrete Fracture Network Engineering, Vancouver, BC.
Cisyk, D. E., M. Maxwell, and R. A. Eso. 2016. ERI: A geophysical method for evaluating rock porosity behind concrete mine shafts. CIM Conference, Vancouver, BC.
Eso, R., D. Oldenburg, and M. Maxwell. 2006–1. Application of 3D electrical resistivity imaging in an
underground potash mine. SEG Expanded Abstracts 25(1):629.
Eso, R., D. Oldenburg, and M. Maxwell. 2006–2. 3D DC resistivity imaging in an active mineshaft.
18th International Workshop on Electromagnetic Induction in the Earth, El Vendrell, Spain, September 2006.
Maxwell, M., J. Unrau, R. A. Eso, D. W. Oldenburg, and L. P. Song. 2005. Advancement of 2D and
3D electrical resistivity techniques for underground applications in a potash mine. Technical Paper,
International Symposium on Mine Planning and Equipment Selection, Banff, 2005. https://www.
canadianminingjournal.com/news/proceedings-14th-mine-planning-and-equipment-symposium/
Taylor, J. B., W. de Beer, G. Morelli, H. Harris, B. Klue, M. Beck, C. Langford, and S. Bonham-Carter.
2010. Geophysical trials to reduce probe drilling at the millerton block of stockton mine. Proceedings of the 43rd Annual Conference, New Zealand Branch of Australian Institute of Mining and
Metallurgy, Auckland, New Zealand.
Taylor, J. B., B. Klue, M. Harris, and W. de Beer. 2011. Geophysical techniques for underground coal
mine hazard mapping. Proceedings of the 44th annual conference, New Zealand Branch of Australian Institute of Mining and Metallurgy, Queenstown, New Zealand.
Chapter 43
Case: pre-investigations for
horizontal directional drilling
in Copenhagen
Ole Frits Nielsen, Søren Wedel Nielsen,
and Søren Stricker Mathiasen
43.1
BACKGROUND AND OBJECTIVE
Energinet were conducting an upgrade of the 132 kV power cable system in the greater
Copenhagen area, for which multiple horizontal directional drillings (HDDs) were planned.
Limestone was expected near the surface in parts of the area and considered a risk for HDDs.
This case considers 1520 m of geophysical investigations requested by Energinet to obtain
a better lateral understanding of the depth to the limestone along two of the longest and
deepest planned HDDs. COWI suggested the seismic refraction method, which mapped the
limestone surface well as a supplement to the planned geotechnical boreholes. Further, a test
was performed to investigate whether electrical resistivity tomography could map the top of
the limestone surface.
43.2
SURVEY PLANNING AND CONSIDERATIONS
The HDDs were planned close to the Copenhagen Harbour area and in an area with heavy traffic (Figure 43.1a). The HDDs were initially planned to stay above the known but unmapped,
shallow limestone and locally to reach a depth of 15 m below ground level. Before survey
execution, the conceptual model was used to establish the survey layout, the spread length,
and geophone distance. The limestone was expected to be overlain by a variable thickness
of till, fillings, or both.
Initially, Energinet requested an electrical resistivity tomography (ERT) survey, but as the
area is close to the harbour, a test was run before making a final decision. The test showed
that the resistivity contrast between the limestone and the overlaying till was negligible due
to saline groundwater in the limestone. Therefore, the ERT survey could not resolve the top
of the limestone.
Two seismic P-wave refraction lines were acquired: Line A1/A2 was 600 m long and located
along the harbour pier. Line A5/A6 was 920 m long and crossed a golf course. The acquisition
took place during wintertime but with no frost in the ground. Data acquisition, including the
ERT test, was conducted by a field crew of 3 people on foot over a period of 2 weeks. Acquisition time was relatively slow due to short hours of daylight. A day of high winds caused
unacceptable noise, and there were noise issues related to nearby industrial sources and traffic.
The shot point distance was 10 m and geophone spacing 2.5 m. The seismic source was
either a sledgehammer or shotgun. The shotgun gave, in general, the better signal, but the
sledgehammer was applied near buried cables to avoid damage to the cables. Positioning
was performed in the field with a Trimble™ R8s RTK-GPS.
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Figure 43.1 (a) Line plan and stationing of two refraction seismic surveys and geotechnical positions
along the profles. (b) Results showing the velocity along the two profles and the interpretation of the seismic data and correlation to the geotechnical positions.
43.3
RESULTS AND CONCLUSIONS
Back in the office, inversion of the seismic data was performed using the software TomoPlus™ 4.5 from GeoTomo, and results for velocities were established to 30 m below ground.
During the inversion and interpretation, it was clear that the velocity of the limestone
and the till, to some extent, were overlapping (Figure 43.1b and Figure 43.2). This is a
known phenomenon in the Copenhagen area, where the top of the limestone is often fractured, weathered, and overlain by a generally strong but varying till with a high content of
Horizontal directional drilling in Copenhagen
273
Figure 43.2 Shot gathers from two nearby positions illustrating the picking of frst arrivals (red).The
shotgun (lower gather) gave a better defnition of the frst arrivals and allowed picking at
later times than the sledgehammer (upper gather).
limestone and sand. The results of field vane testing at the geotechnical positions showed
shear strengths of >700 kP in the till, confirming the strong till layer.
The results generally showed a relatively low velocity of 500–1000 m/s in the upper 2–5
m and a downwards increasing velocity to 2.500 m/s at around 15 m below ground level.
This was in accordance with the conceptual model and former investigations finding a till
with a seismic velocity of 1600 to 2000 m/s above the limestone. Limestone velocities were
generally found from 2500–2900 m/s.
The correlation to the geotechnical boreholes was generally good (Figures 43.1b). The
final combined interpretation established the top of the till at a velocity of 1500 m/s and the
top of the limestone at 2500 m/s. For the geotechnical position in profile A1/A2 at station
340, however, the limestone was found 10 m below ground level at a velocity of 2000 m/s,
which is 4–5 m shallower than the geophysical interpretation. The reason for this can be that
a slab of limestone is encaged in the till or that the limestone is more weathered or fractured
at this location. Relatively thin bands of high velocities in the till are interpreted to represent
a variation in the sediment content or different generations of till.
The results of the combined pre-investigations were used for the risk assessment workshop with the contractor prior to planning and execution of the HDDs; the depth to the limestone along each profile was especially valuable input.
ACKNOWLEDGEMENTS
The authors wish to thank Energinet for making this survey possible and allowing the results
to be published.
Chapter 44
Case: mapping depth to bedrock
along a planned cable route
Bo Bergman, Daniel Juul Okholm, and Anders Edsen
44.1
BACKGROUND AND OBJECTIVE
The responsible authority for electricity transmission in Sweden, Svenska kraftnät, is planning a new power line called Hansa PowerBridge between Hörby in the South of Sweden
and Güstrow in Northern Germany. The investigations presented in this case study covers 35
km out of a 55 km subsurface cable route between Hörby and Ystad in Sweden.
The objective of the investigations carried out by WSP is to map and document the
geotechnical as well as engineering geological conditions along the cable route. Investigations included various geotechnical methods (cone penetration test, auger drilling
and sampling, comprehensive soil and rock probing, etc.) combined with geophysical
measurements.
The geophysical investigations aimed to locate areas where the depth to bedrock was less
than 4 m. Thus measurements were concentrated in areas where the bedrock surface was
assessed to be shallow. Based on information from the Geological Survey of Sweden (SGU),
the SGU well database and pre-existing geotechnical drillings, approximately 35 km of the
cable route were chosen for geophysical investigations.
Generally, the terrain along the cable route has gentle varying topography and is characterised by open farmland, villages, rivers, and small water streams. The route intersects
different types of infrastructure. It passes through a roundabout, crosses highways, motorway E65 as well as a railway outside Ystad. In addition to infrastructure, the cable corridor
intersects or is parallel to several powerlines and cables, which must be accounted for in the
processing and interpretation of the geophysics.
44.2
SURVEY PLANNING AND CONSIDERATIONS
The instrumentation used for the investigations was the ground conductivity meter (GCM),
DUALEM instrument (version 421S), see Figure 44.1, and the geophysical survey was
undertaken from January 22 to January 26 2021. The speed during measurements was
adjusted to produce data points (soundings) with a 1 m spacing. For positioning, the instrument was connected to a GPS with an accuracy of better than 0.2 m.
Besides naturally occurring ground resistivities, the DualEM measurements are influenced by nearby metal objects, such as fences, metal service pipes, power- and telephone
lines, which causes disturbances in the data. In order to minimise the amount of disturbed
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Figure 44.1 The DualEM system during feldwork. Note the truck with a water tank used for washing
the instrument and ATV when crossing cadastral boundaries.
data, available information on presumed or known sources of disturbance was included in
the survey planning.
Further, the measurements were performed in three parallel profile lines within a corridor
of ±25 m (see Figure 44.2c) from the cable route, which on the one hand, improved the possibility of obtaining undisturbed data and, on the other hand, enabled results and anomalies
to be verified at neighbouring profile lines.
Prior to the DualEM survey campaign, test measurements were conducted using GPR
(ground penetrating radar) with a 160 MHz as well as a 450 MHz antenna. An evaluation
of the GPR data indicated an insufficient depth of investigation (DOI) with respect to the
survey objective. Most likely, the reason is a high content of fine material, e.g., clay or silt
in the sandy till, which is generally described by SGU to constitute the topsoils in the area.
Fine materials such as clay and, to some extent, silt dampen the signal and thus significantly
reduce the penetration depth of the GPR signal.
During the DualEM survey campaign, to prevent plant diseases or seeds from unwanted
plants from spreading between each property, the utilised system was washed/cleaned every
time a cadastral boundary was crossed. In total, the system was washed 91 times during the
data collection campaign (Figure 44.1).
Mapping depth to bedrock
277
Figure 44.2 (a) Distribution of geological bedrock lithologies along the cable route, (b) Exaggerated
section of the cable route. ([a,b]: © Svenska kraftnät, Geological map ©SGU.) Note
that the presented section crosses three different bedrock lithologies. (c) Survey strategy used during data collection with the DualEM instrument (Technical map © Svenska kraftnät.) In order to prevent unwanted plant diseases or seeds from spreading
between properties, the system was washed/cleaned every time a cadastral boundary
was crossed. (d) DualEM resistivity section with interpreted bedrock surface covering the section indicated in the top right geological map. Note that the Hardeberga
Formation has a high-resistivity signature, whereas the Jurassic strata have signifcantly
lower resistivity.
278
44.3
Engineering Geophysics
GEOPHYSICAL DATA PROCESSING AND
INTERPRETATION
As mentioned, the DualEM instrument is sensitive to the influence of metal structures above
ground level as well as buried cables. Thus the first step in the data processing is to go
through the complete data set and remove data points that, for any reason, show sign of influence from external sources of disturbance. Finally, data are stacked into soundings that can
be subjected to inversion in order to result in a 1D model at each sounding location.
Following the data processing, 1D resistivity models fitting the geophysical data were
determined through inversion and presented as resistivity sub-sections along the measured
profile lines. In each section, the bedrock surface was interpreted and verified against neighbouring or overlapping subsections. Figure 44.2d presents the results for the section in Figure 44.2b.
Further, the interpretation was checked against known depths to bedrock based on available information from geotechnical drillings as well as the SGU well database and other
geological information. During this process, the results were continuously used to identify
locations along the surveyed cable route where near-surface bedrock could be expected and
where additional geotechnical drillings could be required to confirm the interpretations.
44.4
GEOLOGICAL INTERPRETATION
Geologically, the conditions along the cable route generally consist of soils of sandy till
with varying parts of finer sediments, e.g., silt or clay, overlaying sedimentary bedrock of
different ages and compositions. Interpretations have generally been carried out based on
the experience that soil resistivities tend to be lower than the resistivity values measured in
bedrock.
However, geological conditions in both soil and bedrock vary a lot along the cable route,
with the result that soil and bedrock may exhibit similar resistivities. Further, in some areas,
the bedrock has a low-resistivity signature, whereas, in other areas, bedrock is characterised
by high resistivity values. This implies that the general rule of thumb cannot be applied to
this data set unconditionally and that the geological interpretation and thus the interpretation of the depth to bedrock depend on a detailed geological understanding and widespread
borehole control.
44.5
RESULT AND CONCLUSION
The result of the investigation, presented to the client, is a combination of the interpretation
of bedrock level presented in the resistivity sections, the corresponding geological descriptions of each section, and finally the soil parameters determined from the geotechnical drillings and investigations.
The results are used to pinpoint locations for further detailed investigations on a local
scale and to determine the design of excavations or methods to be used at excavation-free
passages.
Chapter 45
Case: mapping bedrock profiles
for cable landings using seismic
refraction and surface waves
(MASW)
Joanna Hamlyn
45.1
BACKGROUND AND OBJECTIVE
A geophysical survey was commissioned as part of the ground investigation for a provisional
landing location for cables associated with an offshore wind farm. The survey objective was
to provide information on ground conditions and subsurface geology in order to assess the
suitability of horizontal directional drilling (HDD).
The survey area comprises a shingle beach, cliff line, a gently sloping grassed area and a
stubble field. Superficial marine beach deposits (shingle, sand and silts) sit along the coast,
underlain by stratified coarse-grained sands and till members. The quaternary geology is
underlain by mid-Cretaceous chalk, which contains marl seams.
45.2
SURVEY PLANNING AND CONSIDERATIONS
Given the survey objective, P- and S-wave seismic refraction was used to determine seismic
velocities to infer rock characteristics. This was combined with multichannel analysis of
surface waves (MASW) used to derive S-wave velocity from rolling surface waves, which
can provide good results even if velocity inversions are encountered.
In total, 10 profiles were acquired across the area of interest. These were positioned in a
regular grid format whilst avoiding certain obstructions and minimising the need for vegetation clearance. The P- and S-wave refraction and the MASW were all acquired along the
same profile lines using a 48- or 72-channel GEODE seismic system. For the P-wave refraction survey, geophones were deployed with a 2 m spacing over a spread length of 94 or 142
m. The seismic wave was generated using a seismic impulse device (SID) firing a sequence
of 12 and 8 gauge black powder cartridges. Shots were taken at every 6 geophones along
the spread and distances beyond), typically 30, 60, and 90 m. For some profiles, the off-end
shots were acquired from a small boat in the water. For the S-wave refraction survey, the
same geometry for the geophone spread was used (2 m spacing over either 94 or 142 m).
The seismic wave was produced by striking an S-wave plate sideways with a sledgehammer. Shots were taken every 6 geophone. The plate was aligned perpendicular to the profile
and struck on both ends to generate shear waves with opposite polarities.
The MASW was acquired using a towed array approach that employed a 2 m spaced
24-channel land streamer pulled by a 4×4 vehicle. This was limited to the relatively flat open
areas and to the data acquired using a sledgehammer and plate. Data acquisition of this scale,
conducted by 5–8 people, typically takes 15–25 days.
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RESULTS AND CONCLUSIONS
The interpretation of the seismic sections is based on the widely understood and published
velocities of typical subsurface materials.
Analysis of the P-wave refraction data for this site suggests up to five distinct layers
(Layers P1 to P5) of contrasting velocities; see Figure 45.1. Layer P1 has a distinctly low
Figure 45.1 Results from a selected seismic profle perpendicular to the shoreline. (a) P-wave seismic
refraction velocity profle. (b) S-wave seismic refraction velocity profle. (c) Seismic MASW
refraction profle.
Mapping bedrock profiles for cable landings
281
velocity usually related to loose near-surface superficial material (i.e., soft soil). Layer P2
and P3 reflect relative increases in consolidation or compaction, with the added possibility
of increased water content. Layer P4 can be more intricate to discriminate as the overlap
in velocities means that it can represent both sediments (dry compact or wet) and, in some
cases, bedrock strata (weathered/weak/fractured). The most effective way to differentiate
between sediment and rock-type material is to consider the corresponding S-wave velocity. Layer P5 represents the highest (and deepest) velocity unit and is likely to reflect a
competent boundary within the chalk bedrock. However, given the weak/variable nature of
the chalk and the relatively low P-wave velocity limit of this layer, compared to velocities
typically found to represent chalk, it is possible that the P5 layer could also represent a very
dense till unit.
By carrying out a similar analysis of the S-wave refraction data, the velocity layers (Layer
S1 to S5) were subsequently derived. Layer S1 suggests soft soils. S2 is representative of
stiff or dense soil. Layer S3 is indicative of dense soils, whereas S4 suggests weak rock. S5
is indicative of rock.
The lower P-wave refraction boundary is planar at elevation ~−4 m and dips slightly to
the south. Based on the velocity and local borehole information, this refractor is likely to
represent relatively competent chalk bedrock. There is a reasonable correlation between this
boundary and the lower S-wave refraction boundary of 700–750 m/s, implying a soft bedrock unit. The slight deviation in the depth profile between the boundaries can either be due
to local variations in the weathering profile, subtle changes in lithology, or groundwater. The
overlying layer (P4) exhibits a much lower-velocity material. When considered with the corresponding S-wave velocity (S4), this material is likely very soft, highly weathered bedrock.
This, in turn, is overlain by lower-velocity superficial deposits and soil-type material.
The cliff line prevented MASW acquisition between 0 and 55 m chainage. The data suggest a low-velocity zone extending from the surface to 1 m elevation, which correlates with
the seismic refraction boundaries. This is followed by a gradual increase in velocity with
depth, which matches the lower refraction boundaries.
The geophysical survey has provided a non-invasive means for investigating the subsurface with high spatial coverage. However, the shingle bank attenuated the S-wave signal
preventing any velocity analysis in this area. The refraction data analysis has provided information to assist with the bulk characterisation of the shallow subsurface and has identified a
series of distinct seismic velocity layers.
Chapter 46
Case: lake bottom investigations
with ground penetrating
radar (GPR)
Jaana Gustafsson
46.1
BACKGROUND AND OBJECTIVE
A mainline sewage pipe was planned in a municipality in southern Sweden, from the city
centre to the water treatment plant outside. The installation of the pipe would eventually
cross a lake, and approximately 1.5 km of the pipe was planned to be placed on the lake bottom (see Figure 46.1a) in addition to the 2.5 km on land. GPR was suggested as an investigation method during the planning stage to see whether the suggested location was feasible or
there were too many obstacles present in the lake or shallow bedrock on land.
46.2
SURVEY PLANNING AND CONSIDERATIONS
The main objective was to locate areas with a risk of hitting shallow bedrock during the
construction of the sewage pipe. Unexpected shallow bedrock constitutes a risk of both
additional costs and time delays in the construction phase.
In the planning of the GPR investigation, it was discussed whether GPR could also be used
on the parts crossing the lake. The lake was known to be overfertilised with a conductivity
undulating between 32 and 38 mS/m over the year. Most often, GPR is not recommended
if the conductivity is above 10–20 mS/m (or equivalently in resistivities less than 50–100
Ωm). The outcome of a GPR investigation on the lake was therefore considered uncertain.
However, the land investigations were carried out within a limited number of hours, so the
rest of the field days were used for lake measurements. To overcome some of the challenging
conductivity, a quite low GPR antenna frequency was chosen for the lake measurements.
The lake measurements were carried out with an unshielded, in-line antenna with a centre
frequency of 100 MHz shown in Figure 46.1b. The measurements were made pulling the
antenna behind a small boat with an outboard engine, and the measurements were triggered
by time. It is not necessary to keep the same pace during measurements, but when looking
for objects in the data (imaged by so-called hyperbolas), the shape of these are more coherent if the measurement speed is even. Therefore, the point interval for each measurement
was set according to the speed of the boat. In this case, 0.05 s between each collected trace.
For positioning, the breakpoints of the planned sewage line were marked with buoys, and
navigation in-between was made with a GNSS.
A GNSS was also connected to the GPR equipment to collect positioning data for all
traces. As the antenna was placed in the water behind the boat, the positions were corrected
afterwards with the offset between the antenna midpoint and the GNSS antenna location in
the boat.
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Figure 46.1 GPR survey overview, equipment and results. (a) Map of investigation area with investigated profle on the lake marked in green. (b) GPR equipment used: MALÅ ProEx system
from Guideline Geo and 250 MHz antenna, used for the land measurements. (c) 100 MHz
RTA-antenna.The RTA antenna was placed in a plastic tube, taped waterproof, and pulled
behind a small motorboat. (d) Example of GPR results. The bottom of the lake is more
or less smooth but contains areas with outcropping till and boulders above the loose
sediments.This is shown in detail in the blue box.The area where the conductivity of the
water damps the radar signal is indicated by a red line. This radargram corresponds to
approximately 100 m in length.
Lake bottom investigations
46.3
285
RESULTS AND CONCLUSIONS
The GPR results shown in Figure 46.1d clearly reveal the lake bottom conditions, even
though the conductivity of the lake was high. Results were gained from approximately 80%
of the investigated profile. In the remaining 20% of the lake, no information was gained due
to the larger water depth in combination with the high conductivity of the water. This is also
seen in Figure 46.1d, where the loose sediment layer suddenly disappears on the right-hand
side of the radargram.
The lake bottom had to a large extent a very smooth bottom of loose sediments, perfectly
suitable for a pipe construction without any complicated preparations. However, in parts,
the GPR revealed till and boulder areas, outcropping through the sediments. See detail in
Figure 46.1c.
To conclude, GPR gave clear information of the lake bottom conditions which were used
to plan the best position and installation of a submerged sewage pipe.
Chapter 47
Case: pre-investigations for
pipeline crossing of a stream
Ole Frits Nielsen, Søren Wedel Nielsen, and
Søren Stricker Mathiasen
47.1
BACKGROUND AND OBJECTIVE
Energinet performed pre-investigations prior to pipeline installation across Denmark. Horizontal directional drillings (HDDs) were planned for crossings of streams, roads, and areas
of protected nature. Combined geotechnical and geophysical investigations with electrical
resistivity tomography (ERT) were used in order to image the distribution of soils laterally
and to establish the geotechnical parameters. For one crossing, results were inconclusive
concerning the depth to the shallow limestone and detection of a fault zone. Therefore, further geophysical investigations were initiated to better understand the local geology and to
lower the risk for the HDD after multiple unsuccessful attempts to finalise the HDD. COWI
conducted a seismic refraction line of 850 m and approximately 1500 m of additional ERT
profiles and applied an advanced inversion technique combining the geophysical and geotechnical data. Despite the lack of resolution in the seismic refraction data, the ERT results
were able to image the top of the limestone, and an area affected by faulting was established
so that the pipeline installation could continue.
47.2
SURVEY PLANNING AND CONSIDERATIONS
From the geotechnical investigations and former ERT results, the geological layering was
known to consist of limestone overlain mainly by till but also by layers of meltwater
deposits.
The former ERT results lacked data below the stream itself. Thus, geophysical data collection crossing the stream was needed. It was expected that seismic refraction data would provide the best results. However, the risk remained that velocity contrasts would be very low
in the soils, resulting in a lack of resolution. Therefore, it was decided to collect additional
ERT data as well. The purpose was to supplement the original ERT data and close the data
gap across the stream. The project time schedule did not allow for additional fieldwork at a
later stage if the seismic refraction data failed to provide the needed clarification regarding
the depth to the limestone and detection of fault zones.
The seismic data collection was performed over 3 days during fine weather conditions in
early autumn. ERT was prepared parallel to the seismic acquisition, and the data were collected over 2 days. The ERT survey included 2 survey lines of 1460 m in total (Figure 47.1a).
The refraction seismic survey line (850 m) coincided with the position of the ERT Line 1
and followed the planned route of the HDD. The fieldwork was performed by a crew of 2
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Figure 47.1 (a) Line plan and geotechnical positions across the stream. ERT line 1 (850 m) and the
seismic line are positioned along the planned cable alignment. (b) Results showing the
SMCI interpretation results as resistivity. Boreholes are projected on to Line 1 (top) with
lithology as colors, see legend. Position of interpreted top of limestone (thin black lines)
and fault zones (black stippled lines).
Pre-investigations for pipeline
289
Figure 47.2 Fieldwork at the stream.
technicians. Data acquisition and equipment transport took place on foot to minimise the
surface impact in the protected areas. Equipment was distributed across the stream using
a small boat (Figure 47.2). Positioning was performed in the field with a Trimble™ R8s
RTK-GPS.
The seismic refraction setup consisted of an array of 24-channel geophone cables with a
geophone spacing of 5 m. For the stream crossing, a 24-channel hydrophone cable with a
geophone spacing of 1 m was used. Each geophone cable was connected to a 24-channel
geometrics seismograph.
A 12 gauge shotgun, “buffalo gun”, was used as a seismic source during the survey. The
so-called buffalo gun is specifically designed for shallow seismic surveys. Shallow boreholes were hand-drilled every 10 m along the survey line to a depth of 40–100 cm. The
borehole depth was determined by the substrate as the impact is dampened by gravel and
organic deposits above the saturated zone. Under these conditions, deeper boreholes were
required to ensure sufficient impact.
The ERT data were collected with an ABEM™ LS2 Terrameter (8 channel) using gradient configuration. This gives high lateral sensitivity to changes along the profile, such as the
transition from sandy to clayey sediments. Multi-cables with 5 m electrode spacing were
used, and the focus depth for the survey was between approximately 1.5 m and 55 m. The
survey was performed with a “roll-along” setup, making it possible to measure continuously
along profiles of optional lengths. All ERT equipment was tested before and after fieldwork.
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RESULTS AND CONCLUSIONS
The results from the refraction seismic were inconclusive most likely due to too small velocity contrast between the different geological layers. Typical seismic velocities in limestone
are seen in a large range of 2000–6000 m/s depending on strength, weathering, and fracturing. If the limestone is crushed or relocated, it lowers seismic velocities indicating low
strength. In this setting, the velocity of the overlaying till has only a small velocity contrast
to the limestone. Further, faulting was not imaged in the data, also indicating a small velocity
contrast between the layers.
The ERT data were generally of good quality, and only a minor part of the data was
removed during the data processing. Two minor couplings were identified in the data that
originated from the crossing of electric fences. The inversion technique spatial mutually
constrained inversion (SMCI), developed by COWI, was used for the combined inversion
of soil information from the geotechnical boreholes and the ERT data for lines 1 and 2. In
the SMCI, the ERT data are constrained along each ERT profile but are also constrained
to neighbour profiles. Furthermore, the starting model for the SMCI inversion is based on
information from nearby boreholes.
The SMCI inverted profiles indicate quaternary organic deposits, especially north of the
stream, characterised by resistivities between 10–40 Ωm, seen as green/bluish colours down
to approximately 10 m below ground, see Figure 47.1b. The interpretation of the top of limestone is on the figure shown as a thin black line approximately 10–25 m below ground and
is characterised by resistivities above approximately 100 Ωm. Resistivities in the 10–40 Ωm
range that are deeper than 10 m are interpreted as upwelling saline groundwater, which is a
strong indication of fault zones. A major fault zone is interpreted just north of the stream and
was seen on both ERT profiles. Above the limestone, the geology is characterised by shifting
layers of meltwater and till deposits.
The interpreted major fault just north of the stream fits very well the geology found once
the HDD was made. Reaching the location of the interpreted fault zone, the drilling crew
experienced a large loss of borehole mud, indicating a flow of borehole mud into the crushed
limestone in the fault zone.
In summary, the combined interpretation shows the top of the limestone to be relatively
horizontal along the planned route and to be found at a depth of 10–25 m below ground level.
One major fault with the upwelling of saline groundwater and possibly several minor faults
were detected on the northern side of the stream, confirmed on the two ERT lines. The orientation of the fault is identified from the position on the two ERT profiles to be approximately
parallel to the stream. The seismic velocity is low at the top of the limestone, indicating that
the top of the limestone is crushed or relocated, especially north of the stream. The result was
in agreement with the findings during the later HDD execution.
ACKNOWLEDGEMENT
The authors wish to thank Energinet for making this survey possible and allowing the results
to be published.
Chapter 48
Case: delineation of palaeokarst
features under a proposed
tailings facility using ERT, seismic
refraction, and micro-gravity
Barry Balding
48.1
BACKGROUND AND OBJECTIVE
The mine site, located in Europe, has had geophysical and geotechnical investigations carried out over it to characterise overburden and bedrock conditions for the construction of
a Tailings Storage Facility and subsequent extensions to the facility over the past 30 years.
The objective of this investigation was to identify the extent of possible palaeokarst
features below a proposed extension footprint to the Tailings Storage Facility. The target
of the detailed ground investigation was the Lower Carboniferous limestone units. Quantifying the extent of possible palaeokarst features associated with the limestones would
allow the geotechnical engineers to safely design the facility and provide for its integrity
in the future.
48.2
SURVEY PLANNING AND CONSIDERATIONS
Site Conditions: A Quaternary glacial till of ca. 3–6 m in thickness, consisting of grey silty
clay to clayey silt with sand, gravel, and some cobbles/boulders, overlies the bedrock in the
area. The underlying bedrock is well understood and consists of rocks ranging in age from
Lower Palaeozoic siltstones, sandstones, shales, and greywackes through to Lower Carboniferous limestones, consisting of bioclastic calcarenites and pale micritic limestone units,
which tend to be karstified.
Geophysical Planning: To help identify areas of possible palaeokarst for drill testing,
a combination of geophysical methods was employed: (1) seismic refraction, (2) microgravity, and (3) electrical resistivity tomography (ERT).
The nature of the site allowed for an irregular sub-parallel grid of lines to be marked out
(using a differential GPS [DGPS]) to collect the seismic data. By comparing the outcome
of the seismic refraction and the micro-gravity, anomalous areas were identified for targeted
follow-up with ERT to improve resolution. The results of the ERT were then used to target
the locations of geotechnical boreholes.
Geophysical Surveys: The seismic refraction was used to provide information on the
depth to and quality of the bedrock (Figure 48.1a), across the site and also on the potential
rippability of the material. The data were collected using a Geometrics Geode 24-channel
seismograph with 4.5 Hz geophones spaced at 3 m intervals, providing a depth of investigation of between ca. 25 and 30 m below ground level (bgl). The data were processed using
SeisOpt® Pro software.
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Figure 48.1 Results of geophysical mapping of buried karstic features. (a) Bedrock velocities from
seismic refraction (contours in m/s, inferred lithological contacts in red dashed lines). (b)
Gravimetric results: Bouguer anomaly map (mGals).
Delineation of palaeokarst features
293
A micro-gravity (10 m grid) survey was used to target the area of lower velocities identified
from the seismic refraction to better characterise the extent of anomalies; the micro-gravity
results are shown in Figure 48.1b. The data were collected using two Scintrex CG5 gravity
meters. Base station readings were measured at a minimum of every 2 hr so that the instrument
and diurnal drift could be calculated and removed from the data. The stations were accurately
surveyed using a TrimbleTM R8s/TSC3 system. A minimum of 3 measurements was taken at
each station to ensure repeatability. Drift corrected data were corrected for terrain, latitude,
elevation, and Bouguer (using a density of 2.67 g/cm3). Data were subsequently exported to
Oasis Montaj software, where gridding, filtering, and plotting routines were performed.
Two areas with low gravity results within the Lower Carboniferous limestone were targeted
for follow-up with ERT investigation (Lines 3 and 7 in Figure 48.2a and b, respectively). The
Figure 48.2 ERT results. (a) Inversion model Line 3 (looking west). (b) Inversion model for Line 7 (looking west). (c) and (d) Inversion models for Lines 3 and 7 showing locations and depths of
boreholes.
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orientation of the lines was chosen to be perpendicular to the known geological strike of the
site, with the locations chosen to allow safe access and line continuity.
As noted, the ERT was used to target a number of micro-gravity anomalies to improve
the resolution of the features. A gradient array using 5 m electrode spacing over 400 m long
lines was used to provide a depth of investigation of ca. 60–70 m bgl. An ABEM Lund Imaging System was used to collect the data, and processing was undertaken using RES2DINV©
software.
48.3
RESULTS
Figure 48.1a presents the interpreted bedrock velocity map for part of the site, showing the
inferred contact between the Lower Palaeozoic siltstones/sandstones and Lower Carboniferous limestone lithologies. The lower velocities for the bedrock (ca. <3000 m/s, the area
in blue in Figure 48.1a) are indicative of the underlying Lower Carboniferous limestones,
with the higher velocities (ca. >3000 m/s) reflecting the presence of the Lower Palaeozoic
sediments.
Figure 48.1b presents the Bouguer anomaly map for the site, with geological features
inferred from mapping on the ground.
Figures 48.2a and b present inversion models of the processed apparent resistivity (in
ohm-metres [Ωm]) for Line 3 and 7 with topography included.
Subsequent drilling of BH2 on Line 3 and BH5 on Line 7 confirmed the presence of palaeokarst features to depths of at least 40.3 m and 19.5 m, respectively (Figure 48.2c). BH2
encountered sands, silts, clays, and gravels/cobbles, while BH5 encountered sands, silts, and
clays. Both boreholes were terminated as the drill rods became stuck prior to reaching their
target depths.
48.4
CONCLUSIONS
The outcome of the geophysical investigations and follow-up intrusive investigations indicated that the “karst” features identified as underlying the proposed Tailings Storage Facility
extension area consisted of a palaeokarst system, with karst features infilled (“choked”) with
postglacial infill materials, including sand, silt, clay, and rock fragments.
The geological conditions encountered showed good agreement between the results of the
geophysical surveys (in particular the ERT) and the targeted follow-up boreholes.
In addition, the seismic refraction results provided an evaluation of bedrock rippability
across the site.
Chapter 49
Case: the identification of
leaks in tailings storage facility
impoundment dam walls using
ERT and IP
Barry Balding
49.1
BACKGROUND AND OBJECTIVE
The Tailings Storage Facility (TSF) under investigation is a fully lined facility constructed in
two stages (using an upstream method of construction) with locally derived glacial till and
graded rock fill. The liner system used consists of a linear low-density polyethene (LLDPE)
geomembrane over a geosynthetic clay liner.
During a routine inspection of the TSF, water was noticed seeping from the toe of the
facility (Figure 49.1a). It was initially thought that a portion of the welded LLDPE geomembrane had failed or was improperly installed.
An ERI /IP survey was subsequently commissioned along with other geophysical and
intrusive investigations to investigate the nature and source of the seepage water.
49.2
SURVEY PLANNING AND CONSIDERATIONS
49.2.1
Site conditions
A lined TSF dam wall ca. 8–10 m high, consisting of glacial till and graded rock fill, underlain by Lower Carboniferous limestone bedrock.
49.2.2
Geophysical planning
To better identify the location of the leaks in the LLDPE geomembrane and target intrusive investigation, an electrical resistivity tomography (ERT) and induced polarisation (IP)
survey was commissioned. A number of closely spaced parallel lines (3–5 m apart) were
marked out (both along the crest and slope of the dam wall) using a differential GPS (DGPS)
to ensure accurate positioning of electrodes and subsequent targeting of anomalies with
intrusive investigations.
49.2.3
Geophysical survey
An ABEM Lund Imaging System using a gradient array with a 1.5 m electrode spacing
was used to collect data along the closely spaced lines parallel to the crest of the dam wall
both inside and outside the liner. The ABEM system used allowed for the collection of
both resistivity and induced polarisation (IP) data. The IP method was used to help map the
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Figure 49.1 (a) Seepage water fowing from the toe of the TSF. (b) Corresponding resistivity and IP
inverted model sections along the dam wall.
potential pathway(s) and indicate the location(s) of possible leakage points, as ionised water
can appear chargeable under certain conditions. A Tigre system using a Wenner array with a
3 m electrode spacing was also used to collect resistivity data for comparison with the data
collected using the ABEM system.
Following the acquisition, the recorded data was pre-processed to remove noisy data by
de-spiking before inversion processing using RES2DINV©, which provided contoured sections of apparent resistivity and chargeability. The corresponding resistivity and IP sections
for a line surveyed along the slope of the dam wall parallel to the crest are shown in Figure 49.1b.
The IP section identifies two possible target areas for intrusive follow-up investigations,
at ca. 140 m and 165 m, respectively. The resistivity section identifies the presence of a
Leaks in tailings storage facility
297
rock layer used in the construction of the facility’s perimeter road (high-resistivity rockfill),
overlying glacial till used in the construction of the dam wall, which in turn overlies higherresistivity limestone bedrock (below the dam wall).
49.3
RESULTS
Extensive follow-up intrusive investigations uncovered a number of tears in the liner in the
vicinity of the IP anomalies, which were patched and made good.
49.4
CONCLUSIONS
ERT and IP were used to understand the nature of the materials used in the construction of
the facility’s dam walls and to successfully target and uncover a number of tears in a LLDPE
liner, thereby saving time and expense in costly intrusive investigations, to the satisfaction
of both the client and the regulator.
The facility continues to be monitored on an ongoing basis as it passes into aftercare.
Chapter 50
Case: groundwater vulnerability
assessment for new motorway
using ERT
Torleif Dahlin, Elisabet Hammarlund,
and Roger Wisén
50.1
BACKGROUND AND OBJECTIVE
A new stretch of 4-lane motorway was planned for E22 southwest of Kristianstad in southern Sweden. The sedimentary bedrock of the Kristianstad plain composes a large and vital
regional aquifer, and, due to the important groundwater resources in the area, a vulnerability
assessment was required. The government of Sweden had approved the construction of the
road on the condition that necessary protective action would be taken to assure that the aquifer is not polluted either in case of an accidental spill or by stormwater from the road. There
are means to design the road with groundwater protection, for instance, by an impermeable
membrane underneath the ditches along the motorway, but it is costly. Therefore, it was suggested to allow the clayey soils in the area to act as protection where it could be proved to
have sufficient natural protection.
The 11 km stretch to be investigated covers the northern slope of the Linderöd horst structure,
a part of the Kristianstad plain, and the glaciofluvial deposits of Helgeåsen. Figure 50.1a
shows a schematic map of the new road stretch (Wisén et al. 2016).
Figure 50.1 (a) Investigated stretch superimposed on map of bedrock and overlaying sedimentary
units. (Based on map from Swedish Geological Survey [Wikman and Sivhed 1987]).
(b) Interpreted geological section based on well records, geotechnical soundings, bedrock
map, and ERT results.
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Engineering Geophysics
SURVEY PLANNING AND CONSIDERATIONS
Information from the Swedish Geological Survey (SGU) was used to create an initial conceptual geological and hydrogeological model as a base for interpretation – specifically, the
geological maps of soil and rock as well as SGU’s borehole database. The thick layer of clay
till present in most of the area acts as a long-term protection of the aquifer. However, the
layer of interest here is the overlaying clayey sand till. If the permeability of this till is sufficiently low, it acts as a shallow barrier that allows immediate remediation of pollutants that
are accidentally spilled on or next to the road.
Electrical resistivity tomography (ERT) was used to acquire continuous information about
the stratigraphy and hydraulic properties along the planned road stretch. The measurements
were performed with a Lund Imaging System setup with 5 m electrode spacing utilising an
ABEM Terrameter LS. Four electrode cables with 21 takeouts each at 5 m spacing were used
to form a 400 m long electrode spread, and a roll-along was used to extend the investigation
line in steps of 100 m. The multiple gradient configuration applied gave a depth penetration
of about 70 m. Data were inverted as 2D resistivity tomography using the software Res2Dinv, after quality assurance and removal of noisy data.
Two parallel ERT lines were measured 25 m apart along the entire section in question,
supplemented by a few shorter cross sections. The ERT results were used for selecting
representative points for complementary geotechnical drilling and hydraulic tests.
50.3
RESULTS
Figure 50.2 shows the results from the resistivity survey carried out along a new section
of road E22. The first part of the section consists of a Linderödsåsen fault zone (zone A).
The lowest-resistive (blue) parts (zones B and C) consist of layers of clay and clay till.
If these are sufficiently low-permeable and have sufficient thickness and continuity, they
constitute a satisfactory natural barrier against contamination of the underlying aquifer.
On top of this rests clayey to sandy till. In the southern part (zone B), two different till
layers with high and intermediate resistivity (red and yellow, respectively) can be clearly
distinguished. In contrast, in the northern part (zone C), only one layer with high resistivity (red) is found. As shown in Figure 50.2, there is generally such a low-resistance layer
along large parts of the investigated section (zones B and C), while it is missing at both
ends of the area. At the northern end (zone D), the sediments are more heterogeneous,
and the limestone with higher resistivity (red) is more superficial there. Between 28/500
and 29/000 there is a crossing cable, and just before 30/000 there is a cased well next to
the northern line.
The two parallel lines show good coherence, where even smaller structures are clearly
visible in both, which increases the credibility of the results and facilitates interpretation.
The resistivity results were used as a basis for selecting representative points for drilling and
installation of piezometers in these. Slug tests were then performed to estimate the hydraulic
properties in the different parts of the area, together with grain size analyses of the sampled
material. Interpretation of the results (Figure 50.1b) showed that the geological materials act
as a sufficiently protective barrier against pollution along most of the section (zones B and C)
and that protective measures in the form of membrane are only necessary along other
parts.
Groundwater vulnerability assessment
301
Northern line, western part
Nivå[m.ö.h.]
Level[m.a.s.l.]
40
20
0
-20
19/500
20/000
20/500
21/000
21/500
22/000
22/500
23/000
23/500
24/000
24/500
Southern line, western part
19/500
20/000
20/500
Level[m.a.s.l.]
Nivå[m.ö.h.]
40
20
0
-20
21/000
21/500
22/000
22/500
23/000
23/500
24/000
24/500
Northern line, eastern part
25/500
26/000
26/500
27/000
27/500
28/000
28/500
29/000
29/500
30/000
Level[m.a.s.l
Nivå[m.ö.h.]
40
20
0
-20
-40
30/500
30/000
Level[m.a.s.l.]
40
20
0
-20
-40
30/500
Southern line, eastern part
25/000
25/500
25/000
B: Erosional valley with two different clay tills
A: Fault zone
25/000
25/000
26/000
26/500
27/000
27/500
28/000
28/500
29/000
C: Erosional valley with till
20
40
Resistvitet[˜
˜m]
60
29/500
D: Helgeåsens border zone
80 100
140
200
400
600
800 1000
Resistvity[˜m]
Figure 50.2 Results from ERT surveying along the planned new stretch of E22.The two parallel cross
sections are shown one over the other and show a very good agreement. Interpretations are listed underneath the sections.The arrow between stationing 28/500 and 29/500
marks a coupling. It is seen in the inverted sections as a vertical feature widening with
depth where the data are disturbed by a crossing conductor, probably a cable.
The combined results were used together with existing well records and geological maps
with descriptions as a base for creating an interpreted geological section. The section was
used to plan the protective measures required for the underlying aquifer.
50.4
CONCLUSIONS
In conclusion, ERT was used to assess the risk of pollutants penetrating into the precious
groundwater reservoir under the Kristianstad plain. Together with geotechnical and hydrogeological methods, ERT made it possible to achieve a continuous image that reflects the
hydraulic properties of the ground along a road stretch. The good agreement between the two
parallel ERT lines gave confidence in the results. The investigation showed that a large part
of the area is covered by impermeable layers, in the form of a thick clay till or a sequence of
sandy till over clay till, which was shown to have low permeability and to provide sufficient
protection for the aquifer below. Only in parts of the area, close to the Linderöd horst, where
the soil cover is thin or absent, and at the glaciofluvial deposits at and around Helgeåsen, it
302
Engineering Geophysics
indicates that the road should be constructed with a protective barrier. The savings resulting
from these conclusions are estimated to be in the order of €5 million.
ACKNOWLEDGEMENTS
Thanks to Conny Olsson, Trafikverket (the Swedish Transportation Administration) for
fruitful collaboration in connection with the surveys, Björn Holmqvist for his valuable work
with the geological interpretation, and Markus Nilsson for help with the graphics. We thank
Trafikverket for letting us present this material.
REFERENCES
Wikman, H., and U. Sivhed. 1987. Berggrundskartan 3D Kristianstad SV med flygmagnetisk och strukturgeologisk karta. Series Af nr.155. ISSN: 0586-1543, Swedish Geological Survey, Uppsala.
Wisén, R., E. Hammarlund, and T. Dahlin. 2016. Resistivity measurements as a tool for optimizing
groundwater protection at a highway construction in Sweden. Procs. NGM 2016, 17th Nordic Geotechnical Meeting. Reykjavik, Iceland, 25th–28th of May 2016.
Index
3D 5, 6, 8, 12, 13, 15, 70–72, 74, 77, 109, 112,
113, 119, 125, 137–140, 146, 157, 177, 183,
198, 210, 228, 229, 230, 239–242, 245, 250,
256, 258, 259, 261–268
aggregate 5, 67, 181–184
airborne TEM/airborne electromagnetic
methods/AEM/SkyTEM 82, 84, 175, 227,
228, 255
antenna 55, 87, 89, 90–93, 137, 157–159, 161,
167, 171, 187–189, 196, 276, 283
anthropogenic 6, 8, 21–28
archaeology/archaeological 22–28, 55, 59, 61,
63, 64, 77, 149–150
asphalt 87, 93, 171, 239
backfill 151, 153
bedrock 161–163, 175–178, 195–199, 201–203,
233–237, 239–242, 275–281
borehole geophysics 2, 5, 104, 196
boulder 75, 93, 162, 185, 186, 201, 220, 285, 291
cable 26, 75, 109, 112–113, 121, 143, 159,
165, 167, 191–194, 233–234, 239, 264, 271,
275–281
clay(s) 31–34, 36, 37, 39–47, 67, 69, 72, 83,
90, 104, 110, 133, 145, 153, 167, 175, 181,
185–187, 211–220, 276, 291, 294, 295, 300;
quick clay 221–225, 227–230
coarse grained soil(s)/granular soil(s) 31, 187
cohesive soil(s)/non-cohesive soil(s) 31, 33, 67
coil 55, 77–78
conceptual ground model/conceptual geomodel/
conceptual geological model/conceptual
understanding 6–8, 21, 237, 245, 253, 271,
273, 300
concrete 30, 62, 87, 93, 141, 185–186, 262, 264
conductivity 4, 11, 67, 77, 78–84, 90, 133–134,
141, 145, 151–153, 184, 196, 209, 217–220,
264, 265, 268, 283–285
coupling 10, 81, 83, 99, 102, 177, 228, 256, 290
crossings 138, 159, 227, 233, 234, 261, 276,
283, 287–290, 300, 301
dam(s) 5, 28, 261–270, 295–298
density 11, 18, 30–31, 36–39, 53–54, 56–57, 63,
96, 99, 104, 113, 117, 125, 151, 159, 210, 243,
293, 295
depression/valley/paleo-channel/anticline/
syncline 151–153, 178, 205–207, 216, 254
direct current resistivity (DC)/electrical
resistance tomography (ERT)/electrical
resistivity imaging (ERI) 17, 67–75, 165–167,
181–184, 191–194, 195, 213–216, 217,
221–225, 233, 235, 255, 261, 265, 271, 291,
295, 300
dispersion/dispersion curve 117–124, 199, 218
dry 4, 38, 67, 75, 110, 133, 162, 181, 183, 191,
211, 214, 218, 220, 261–264, 281
Earth’s total magnetic field (ETMF) 59–61, 133,
149, 151
electrode 18–19, 69–72, 75, 167, 182–183,
185, 191–192, 196, 214, 218, 222, 233–234,
261–268, 295–296, 300; electrode spacing
19, 70, 71, 182, 185, 196, 218, 222, 239, 263,
289, 294, 300
electrode configuration: dipole–dipole 71,
191, 196, 266; gradient 71; pole–dipole
71, 266, 267; pole–pole 71, 266; Wenner–
Schlumberger 71, 191, 196, 214, 218
electromagnetic(s)/EM,77–84, 145–148,
209–212, 255–259
engineering geology 3–4
environmental engineering/environmental
geophysics 2, 4
equivalence 9, 18, 81
Eurocode 29
fault/joint/fracture(d)/deformation 4, 6, 10, 29,
40, 53, 54, 67, 90, 95, 104, 107, 109, 117,
120, 125, 178, 196, 197, 214, 217, 233, 237,
243, 245, 248, 249–254, 255, 264, 273, 287,
290, 300
ferrous materials/metal 59, 62, 129, 131, 133,
150, 156
fine materials 181, 276
304
Index
flowchart(s) 2, 21–28
foundation(s) 1, 6, 24–25, 33, 39, 45, 62, 83,
137, 140, 155–156, 187, 191, 238, 249, 251
frequency domain 78–79, 83–84, 118, 133, 135,
145, 151
friction angle / ϕ’ 31, 32, 33, 42
frost 171–173
fundamental mode 120, 122, 124
geophone 102, 107, 109, 112–113, 165, 201,
207, 214, 218, 239, 250, 251, 271, 279, 289,
291; geophone spacing 112–113
gradiometer 63, 149
gravel(s) 181–184, 185, 205, 216, 289, 291, 294
gravimeter 53–55, 57
gravimetry/gravimetric 53–57, 151
gravity 11, 53–57, 151, 153, 291–294
ground anchor 157, 191
ground based TEM/towed transient
electromagnetic (tTEM) 82, 145–148
ground conductivity meter (GCM)/DualEM/
DualEM421 83–84, 141, 145, 209–210,
218, 275
ground penetrating radar/Georadar/(GPR)
87–94, 161–163, 187–189, 283–285
ground truthing 9, 162
groundwater 4–5, 31, 32, 67–68, 90, 93, 94, 159,
184, 209, 216, 265, 271, 281, 290, 299–302
higher mode 122, 124, 125
horizontal directional drilling (HDD) 271–273
induced polarisation (IP) 69, 295
infiltration 77, 209, 212
integrated methods 9, 195–199
inversion/inverse problem 2, 10–19, 72–73,
79–81, 83, 113–114, 118–119, 121, 125, 167,
177, 185, 210, 213, 217, 222–223, 228, 234,
241, 249, 256, 272, 278, 290
L1-norm 14, 192, 222
L2-norm 14, 15
lake 195, 233, 283–285
landfill/fill 133–135, 264–266
leakage 71–72, 77, 261–268, 296
limestone/chalk 67, 99, 110, 271–273, 287,
290–291, 294, 295, 300
liner 68, 74, 264, 295, 297
loose soil(s)/unconsolidated soil (s) 67, 109,
191–194
low-velocity 109–110, 112, 114, 220, 234,
236, 281
limitation(s) 1–2, 9, 21, 62, 90–91, 95, 98, 110,
112, 114, 120, 206, 213, 218, 249, 261
machine learning 10, 18, 175, 230, 255; artificial
neuronal network/ANN 177, 178, 230
magnetometer 62, 63, 151
magnetometry/magnetic 59–64, 129, 133, 151,
155–156
micro-gravity 64, 151–153, 291–294
mine/mine shaft 62, 72, 83, 151, 153, 243,
261–264, 267–268, 291
model norm 14–16, 18
model parameters 10, 13, 14, 81, 82–83, 112, 114
modulus/shear modulus/constrained modulus 35,
43, 45, 95, 109, 117, 125
molasse 201–203
moraine 165, 169, 181, 201, 203, 227, 228
mudstone 133, 151, 217, 220, 243, 245
non-uniqueness 9, 11, 16, 18, 62–63, 72, 114,
121, 125, 213
Palæokarst 291–294
permeable 205, 237, 299, 300
pipe(s)/pipeline 26, 62, 71, 93, 139–140, 143,
151, 157–158, 197, 262, 283, 287–290
Poisson’s ratio 45, 96, 109, 125
porosity 31, 36–38, 67, 90
pseudo-section 71, 72
P-wave 95, 107, 109, 113, 203, 205–206, 213,
214–216, 249, 271, 279–281
rails/railroads / railway 23, 165–169, 187–189,
197, 222, 225, 227, 275
reciprocal method/generalised reciprocal method
(GRM), delay-time method 67, 114, 201, 202
reflection seismic 95–106
regularisation 15, 16
reservations 3, 21
resistivity 16–19, 36–38, 67–75, 181–184,
191–194, 213–225, 227–230
resolution and uncertainty 13, 62–63, 68, 73–74,
79–83, 91–92, 102–104, 110–112, 121
resource (s)/resource mapping 2, 5, 56, 77, 181,
221, 234, 299
roads 23, 63, 133, 161–163, 171, 181, 187, 222,
227, 243, 287
saline groundwater 159, 271, 290
sand(s) 5, 8, 30, 32, 33, 36, 38–39, 42, 44, 67,
75, 83, 90, 104, 175, 181–184, 185, 186, 201,
211, 273, 279, 289, 291, 294, 300
saturated 36, 90, 175, 262, 289
screening (s) 6, 196, 224–225, 243–248
seepage 83, 205–207, 267, 295, 296
seismic refraction / refraction seismic / refraction /
sr 55, 56, 75, 107–115, 165, 167–168,
195–198, 201–203, 207, 213–214, 216–220,
233, 236, 239, 249, 251, 271, 279–281, 287,
289, 291–294
signal-to-noise 9, 69, 81, 92, 102–103, 109, 110,
113, 183, 191, 234, 256
Index
silt(s) 31, 33, 44, 181–184, 187, 191–194, 252,
276, 278, 279, 291, 294
site classification for seismic design 124,
126, 196
slope stability/landslide(s)/landslip 26, 33, 80,
107, 213–216, 217, 220, 221, 227
soft soil(s)/peat, 72, 107, 111, 165, 187, 189,
195–199, 214, 249, 281
source/seismic source 98, 99, 102, 117, 121,
124, 203; explosives 111, 113, 131, 165, 234,
243, 244; gun 111, 271, 273, 289; (sledge)
hammer 99, 111, 214, 271, 273, 279; vibrator/
vibroseis 243, 244; weight drop 107, 111, 113
stiffness 1, 5, 29–30, 35, 43–45, 109, 196, 216
stream 275, 287–290
strength 1, 5, 9, 29–34, 39–43, 59, 62–64, 67,
71, 109, 120, 177, 195, 213, 216, 217, 220,
221, 227, 229–230, 249, 255, 273, 290
stress/effective stress / in situ stress 30, 31–36,
39, 42, 44–45, 95, 109, 120
surface waves/multichannel analysis of surface
waves (MASW) 95, 117–126, 196, 198, 205,
213–216, 249, 251, 279–281
survey planning 8–9, 129, 133–135, 137, 145,
149, 151–152, 155, 157, 161, 165, 171–172,
175–177, 181–183, 185, 187–189, 191–192,
195, 205–207, 213–214, 217–218, 222, 227–
228, 233–234, 239–240, 243–245, 255–256,
262, 271–272, 275, 279, 283, 287–289,
291–297, 300
S-wave/shear wave 95, 97, 107, 109–110, 112–113,
207, 213–220, 249–251, 259, 280–281
tailings 5, 72, 83, 261, 262, 266, 291–297
till 30, 31, 67, 133, 145, 155, 161, 162, 195, 211,
220, 222, 233, 271, 272–273, 276, 278, 279,
281, 285, 287, 290, 291, 295, 300, 301
305
time domain 38, 78–79, 80, 84, 129, 131, 145,
147, 175, 176, 227, 228, 255
tomography / tomographic / ray tracing /
waveform inversion/ seismic refraction
tomography (SRT) 15, 67, 107, 114, 165,
167–168, 181–184, 191, 195, 201–203,
213–217, 233, 235, 241
topography 56, 75, 82, 88, 92–93, 115, 120, 149,
162, 175, 177, 191, 192, 195, 216, 217, 220,
251, 256, 275, 294
tunnel (s) 3, 25, 31, 62, 72, 104, 115, 137, 138–140,
175, 177–178, 227, 233–248, 255–268
undrained shear strength / su 31, 33
unexploded ordnance / UXO 6, 23–28, 59, 62,
64, 129–131
unit weight 30, 31, 36, 37
utilities 90, 137, 141–143, 151, 157–159, 205,
207, 250
vertical electrical sounding (VES) 205, 207
void (s)/cavity (-ies) 5, 31, 53, 54, 56, 90, 117,
120, 137, 151, 191, 213, 217, 261–268
void ratio 31, 36, 38, 39, 42
Vp/P-wave velocity 118, 125, 203, 216, 281
Vs/shear wave velocity/S-wave velocity 36–37,
39–47, 117–120, 125–126
Vs 30 196, 198
water/water content 4–5, 8, 30, 31–33, 36, 37,
38, 39, 72, 75, 77, 87, 89, 90, 107, 115, 143,
153, 158, 171, 175, 184, 195, 206, 209, 212,
213, 217, 227, 233–238, 249, 256, 261–268,
275, 279, 281, 283, 285, 295
waveform 15, 38, 78–79, 102, 113–114
weak zones 165, 213, 217–218, 220, 233–238,
239, 241