Geophysical Imaging Methods for Analysis of the
Krafla Geothermal Field, NE Iceland
by
Beatrice Smith Parker
B.E., Massachusetts Institute of Technology (2011)
Submitted to the Department of Earth Atmospheric and Planetary
Sciences
in partial fulfillment of the requirements for the degree of
ARCHNES
Master of Science in Geophysics
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June 2012
@ Massachusetts Institute of Technology 2012. All rights reserved.
1~1
77
11
A)
Author ....
Department of Earth Atmospheric and Planetary Sciences
June 1, 2012
Certified by..
.. .
... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michael C. Fehler
Senior Researcher
Thesis Supervisor
/
Accepted by ....
I
Robert D. van der Hilst
Schlumberger Professor of Earth and Planetary Sciences
Head, Department of Earth, Atomospheric, and Planetary Sciences
2
Geophysical Imaging Methods for Analysis of the Krafla
Geothermal Field, NE Iceland
by
Beatrice Smith Parker
Submitted to the Department of Earth Atmospheric and Planetary Sciences
on June 1, 2012, in partial fulfillment of the requirements for the degree of
Master of Science in Geophysics
Abstract
Joint geophysical imaging techniques have the potential to be reliable methods for
characterizing geothermal sites and reservoirs while reducing drilling and production
risks. In this study, we applied a finite difference tomography method and a doubledifference tomography method to image the P- and S-wave velocity structure of the
Krafla geothermal reservoir in Northeastern Iceland. We combined over 450 new
microearthquakes from a network of borehole seismometers from September 2008 and
June-July 2011 with over 800 events recorded by surface networks between 2004 and
2008 that were obtained from the Iceland Geosurvey. Starting event locations were
determined from the Joint Hypocenter Determination method. Absolute and relative
arrival times were used for the two tomographic inversions to jointly invert for event
hypocenter locations and velocity structure. Finally, we compared the final velocity
structures with a resistivity model determined from a magnetotelluric inversion.
Overall, the earthquakes were located in a tight cluster just south of the IDDP
well, concentrated into a horizontal layer centered at 2km depth, and oriented in
a NW-SE direction by both tomography methods. The main cluster of earthquake
locations is at a saddle point of the resistivity model and near regions of low resistivity.
Both methods yielded similar overall velocity models, with a locally low velocity
region near the surface and a locally high velocity region moving deeper into the
model. We have interpreted a high density intrusion, a gas-filled fracture zone, and a
possible partial melt region based on the P-wave, S-wave, and Vp/Vs ratio anomalies
in the velocity model. Synthetic modeling of the relationship between the resistivity
and velocity models demonstrated the complexities of integrating two data sets with
signals of different wavelength and resolution. However, with a better understanding
of this relationship and knowledge of physical parameters such as porosity, a better
constrained joint-inversion of magentotelluric and seismic data will be possible in the
future.
Thesis Supervisor: Michael C. Fehler
Title: Senior Researcher
3
4
Acknowledgments
The completion of this thesis would not have been possible without the help of collaborators from around the world. I would first like to thank my advisors, Mike Fehler
and Rob van der Hilst, for developing this project and bringing me into the Earth
Resources Lab (ERL). I would like to give a special thanks to Mike for spending many
hours discussing results, troubleshooting problems, and supporting my travels.
All of the students and researchers in ERL have been extremely helpful and supportive of my efforts over the last year. I would like to give a huge thanks to Tim
Seher, who started me off on this project by teaching me how to process seismic
data. Tim was always patient, responsive, and willing to help me on any problem,
no matter how small. Even after he left for London, Tim still helped me debug processing code and helped clarify my methods description. Pierre Goudard also spent
countless hours helping me better understand correlations and the steps required for
data processing, and debugging code. Bill Rodi provided essential help during the
interpretation phase of the results by providing direction and context for analyzing
the velocity-resistivity cross plot relationships. Without our conversations, I would
have had difficulty proceeding in the interpretation.
I would like to give a special thanks to my office mates Sudhish Bakku, Di Yang,
Andrey Shabelansky, Ahmad Zamanian, Jing Liu, Xinding Fang, and Alan Richardson for their support and valuable discussions throughout the year.
Noa Bechor
provided invaluable assistance in obtaining the digital elevation map of the Krafla
area. Jeff McGuire of the Woods Hole Oceanographic Institute introduced me to
the Antelope software, taught me how to pick earthquake phase arrivals, and had
provided continuing support for the various uses of Antelope.
I would like to thank Ari Tryggvason of Uppsala University for giving me his
PStomo-eq tomography program and hosting me in Sweden for three weeks. Over
those three weeks, we spent much time discussing the Krafla region, improving handpicked phase arrivals on seismic waveforms, and working through tutorials to better
understand PStomo-eq. The entire seismology group greatly assisted me, especially
5
Olafur Gudmundsson, Zeinab Jeddi, Samar Amini, and Asdis Benediktsdottir.
I
would like to thank the IT specialist of the group, Arnaud Pharasyn, for making
my network accounts and helping fix problems from across the Atlantic at all hours.
Overall, I had a very positive work and personal experience in Sweden and appreciate
the assistance of all who made it possible.
Thanks to Greg Newman of Lawrence Berkeley National Laboratory in Berkeley,
CA for providing the magnetotelluric inversion model and hosting Mike and I during
our visit. Our two days of meetings provided insight into the joint inversion method
and its potential uses.
Even though Erika Gasperikova was unable to make the
meetings, I would like to thank her for running the MT inversion and providing
background information on the model. I would also like to thank Haijiang Zhang for
providing his TomoDD tomography code, running the inversion during our Berkeley
meetings, and continuing correspondence about the code despite the 12-hour time
change from Boston to China.
Finally, I would like to thank my parents for their continued support throughout
my undergraduate and graduate career at MIT. I could not have done it without
them.
6
Contents
1
2
3
Introduction
13
1.1
M otivation . . . . . . . . . . . . . .
. . . . . . . . . . . . .
13
1.2
Structure of the Krafla Volcano . .
. . . . . . . . . . . . .
14
1.3
Previous Joint Geophysical Studies
. . . . . . . . . . . . .
15
1.4
Iceland Deep Drilling Project
. . .
. . . . . . . . . . . . .
17
1.5
Project Goals . . . . . . . . . . . .
. . . . . . . . . . . . .
19
Methods and Data Analysis
21
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
2.2
The Seismic Network . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
2.3
Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
2.4
Clock Errors and Correction . . . . . . . . . . . . . . . . . . . . . . .
27
2.5
Microearthquake Event Detection . . . . . . . . . . . . . . . . . . . .
30
Inversion Techniques
35
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . .
35
3.2
Seismic Inversion: Tomography
35
3.3
TomoDD, A Double Difference Tomography Algorithm
37
3.4
PStomo-eq, A Finite Difference Tomography Algorithm
41
3.5
Magnetotelluric Inversion . . . . . . . . . . . . . . . . .
43
3.6
Model Comparison . . . . . . . . . . . . . . . . . . . .
45
. . . . . . . . . . . . .
4 Results
47
7
5
4.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
47
4.2
Tomography Results
. . . . . . . . . . . . . . . . . . . . . . . . . . .
48
4.3
Comparison of Tomography with Magentotellurics . . . . . . . . . . .
66
Discussion and Conclusions
73
5.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
73
5.2
Borehole Network Coverage
. . . . . . . . . . . . . . . . . . . . . . .
73
5.3
Structural Interpretation . . . . . . . . . . . . . . . . . . . . . . . . .
76
5.4
Synthetic Modeling of the Velocity-Resistivity Relationship . . . . . .
78
5.5
Understanding the Complexities of the Velocity-Resistivity Relationship 84
5.6
Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
86
5.7
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
89
References
91
8
List of Figures
1-1
Resistivity structure and microearthquake locations at the Krafla volcano........
.....................................
16
1-2
Supercritial fluids.
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
18
2-1
Iceland study area. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
2-2
The Seismic Network . . . . . . . . . . . . . . . . . . . . . . . . . . .
23
2-3
Power spectral density of seismic borehole stations over the course of
data acquisition.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
2-4
Illustration of the clock resets. . . . . . . . . . . . . . . . . . . . . . .
28
2-5
Cross correlation plots show station health and time lag offsets.
. . .
29
2-6
Temporal distribution of events in the 2008 and 2011 data subsets. . .
32
2-7
Seismic waveform shows station ringing.
33
2-8
Seismic waveform demonstrates excellent signal to noise ratio and clear
p hase arrivals.
. . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
3-1
Double difference tomography. . . . . . . . . . . . . . . . . . . . . . .
39
3-2
Initial weighting parameters for TomoDD.
. . . . . . . . . . . . . . .
40
3-3
Initial layered velocity model for both TomoDD and PStomo-eq. . . .
41
4-1
Cross sections and depth slices of the velocity model. . . . . . . . . .
48
4-2
Histogram of depths of events located by TomoDD. . . . . . . . . . .
49
4-3
Earthquake locations from TomoDD follow a NW-SE trend.
50
4-4
NS cross section showing P- and S-wave velocities from TomoDD.
51
4-5
EW cross section showing P- and S-wave velocities from TomoDD.
52
9
. . . . .
4-6
Horizontal slices at constant depth of Vp from TomoDD.
. . . . . . .
54
4-7
Horizontal slices at constant depth of Vs from TomoDD.
. . . . . . .
55
4-8
Horizontal slices at constant depth of the Vp/Vs ratio from TomoDD.
56
4-9
Histogram of events located by PStomo-eq. . . . . . . . . . . . . . . .
57
4-10 Earthquake locations from PStomo-eq follow a NW-SE trend. ....
58
4-11 Earthquake visualization in three dimensions.
59
. . . . . . . . . . . . .
4-12 NS cross section through the IDDP well showing P- and S-wave velocities from PStom o-eq. . . . . . . . . . . . . . . . . . . . . . . . . . . .
61
4-13 Cross section through the IDDP well showing P- and S-wave velocities
from PStom o-eq. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
62
4-14 Horizontal slices at constant depth of Vp from PStomo-eq. . . . . . .
63
4-15 Horizontal slices at constant depth of Vs from PStomo-eq.
64
. . . . . .
4-16 Horizontal slices at constant depth of the Vp/Vs ratio from PStomo-eq. 65
4-17 Horizontal slices of resistivity at constant depth. . . . . . . . . . . . .
68
4-18 Horizontal slices of loglO(resitivity) at constant depth.
. . . . . . . .
69
. . . . . . . . . . . . . . . . .
70
4-20 Crossplots by slice. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
71
5-1
Station locations for the IMO seismic network. . . . . . . . . . . . . .
74
5-2
Seismicity detected on the Icelandic Meteorological Office SIL network
4-19 Crossplots of the entire model volume.
near Krafia from January 2010-September 2011. . . . . . . . . . . . .
75
5-3
Geothermal field locations in the inner Krafia caldera. . . . . . . . . .
77
5-4
The overall velocity-resisitivity relationship closely follows porosity models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
78
5-5
Effect of smoothing filter size on crossplot characteristics. . . . . . . .
81
5-6
Effect of noise on crossplot characteristics. . . . . . . . . . . . . . . .
82
5-7
Effect of resistivity anomalies on crossplot characteristics. . . . . . . .
84
5-8
Examples of possible anisotropy in S-wave arrivals.
. . . . . . . . . .
87
5-9
The joint inversion flow chart strategy from Gallardo (2007). . . . . .
88
10
List of Tables
2.1
Borehole Network Locations..
. . . . . . . . . . . . . . . . . . . . . .
11
24
12
Chapter 1
Introduction
1.1
Motivation
Joint geophysical imaging techniques have the potential to be reliable methods for
characterizing geothermal sites and reservoirs while reducing drilling and production
risks.
Geophysical methods can provide information about the subsurface where
borehole sampling is restricted. However, the degree to which a geophysical technique
can be used to infer reservoir properties (such as orientation and density of fractures,
temperature, and fluid saturation) relies on the uniqueness of the relationship between
the reservoir characteristics and the geophysical parameters. Since these relationships
are often non-unique (e.g, the trade off between spacial extent and density of a velocity
anomaly, and the trade off between brine saturation and clay content for an electrical
resistivity anomaly), integrating multiple geophysical techniques can be used to better
constrain reservoir parameters (Garg et al, 2007).
In this study, we invert microseismic data from the Krafla volcano in NE Iceland
using two methods to determine earthquake locations and a regional 3D velocity
model. We then look for correlations in images between our velocity model and a
resistivity model derived from magenetotelluric data in the same region. We hope to
determine if a combination of these methods could be used to better predict reservoir
properties and successfully determine locations for production wells in the Krafla
geothermal field in northeastern Iceland.
13
1.2
Structure of the Krafla Volcano
The Krafla volcanic system is located in NE Iceland along the Mid-Atlantic rift zone
and consists of two nested calderas with a 100km-long transecting fissure swarm
running 100 east of North. The region is volcanically active, with the most recent
period of basalt flow and gas release occuring from 1975 to 1984 (Sigmundsson et al,
1997).
Early seimic studies indicated the presence of two magma chambers beneath the
volcano from zones of high attenuation of S-waves (Einarsson, 1978). One zone was
found on the E/SE part of the caldera and the other zone was found on the western
side of the caldera. The top of both chambers was located at about 3km depth, and
the bottom of both chambers was not well constrained. Einarsson recognized that
S-wave attenutation is highly variable in the Krafla region and suggested that the
chambers were not actually separate, but merely divided near the surface. Another
seismic reflection study of the Krafla volcano found two P-wave shadow zones vertically stacked at 3 and 5km depth (Brandsdottir et al, 1997). The size and location of
the magma chambers has been extensively studied with geodetic techniques, which
constrain these sources more rigorously.
Studies of ground deformation from INSAR (Interferometric synthetic aperture
radar), geodetic data, and tilt measurements have provided further information about
subsurface structure. In our case, we are interested in the size and depth of the
magma chamber and the characteristics of the the associated geothermal reservoirs
underneath the Krafla volcano. If the surface of a volcano expands, we can infer that
the underground reservoir is filling with magma. If the surface of a volcano contracts
or deflates, we can infer that the reservoir is cooling, since the solidification of magma
is associated with a volume reduction, or that the magma in the reservoir is flowing
laterally from the caldera to the eruption site.
Numerous studies using radar interferometry have come to the consensus that
there are two magma chambers stacked vertically beneath the Krafla volcano, with
the possibility of an additional dike or sill (Tryggvason, 1993; Arnadottir et al, 1998;
14
Henriot et al, 2001; Dalfsen et al, 2004).
A small magma chamber has a Mogi
point source (Mogi, 1958) at a depth of 2.5 to 3km and is slowly contracting due
to cooling magma. The heat released during solidification fuels the geothermal field
(Sigmundsson, 1997). A larger magma chamber has a Mogi point source at a depth
of 21km, near the crust-mantle boundary, and is inflating due to an accumulation
of magma (Dalfsen et al, 2004). Presumably, this deeper source feeds the shallow
reservoir and is the primary source of magma during periods of volcanic activity.
Henriot et al (2001) suggested that two magma sills exist to the north and south of
the main chamber along the rift axis, while Arnadottir etal (1998) and Dalfsen (2004)
suggested a dike running along the rift axis. All reports show evidence of magma very
close to the surface of the volcano.
1.3
Previous Joint Geophysical Studies
The Krafla caldera and geothermal field have been previously studied using joint
geophysical methods. In 2005, Onacha et al published the results of an effort to use
seismic and electrical resistivity data to guide drilling of geothermal wells. A network
of 20 three-component seismometers detected an average of four microearthquakes
per day during periods of fluid injection, and fewer than four per day when injection
stopped. The earthquakes were clustered along a NW-SE trend at depths of 2.53.5km. Magnetotelluric data were used to dilineate low resistivity zones at the three
geothermal production areas.
Finally, the earthquake hypocenters were found to
occur along the boundary of low and high resistivity areas.
This study did not,
however, include a qualitative or quantitative comparison of the resulting velocity
and resistivity models of the region.
In 2009, Arnason et al conducted a study of the Krafla volcano using gravity,
microearthquake, and magnetotelluric data to understand the deeper structure of the
volcano. The gravity data showed a significant local high over the caldera due to
an extra high-density mass underneath the volcano, which was interpreted to be a
deep volcanic root or lava shield. Microearthquakes were monitored by two seismic
15
networks - one dense network on the surface of the inner caldera, and one network
with greater aperture. Most earthquakes were located at depths between 1 and 3km
and within the inner caldera. Magnetotelluric and transient electromagnetic sounding
data were used to construct a 1-dimensional resisitivity model and showed a highly
resistive core covered by a low-resistivity cap (presumably clay). Like Onacha et al
(2005), Arnason superimposed the microearthquake locations on top of the resistivity
model cross section and found that the majority of the events occurred along the
border of low and high-resistivity regions (see Figure 1-1). The three data sets were
interpreted separately for similar structures, and no quantitative comparisons were
made.
0
3162
1000
562
316
-5000
-7500
32
181
10
*5.6
3.2
*1.8
-10000
-12500
-15000
0
5
100b
Figure 1-1: An east-west cross section through the center of the Krafia caldera showing
the subsurface resistivity structure with the earthquakes superimposed (green stars).
Most earthquakes appear at the boundary of the low and high resistivity regions.
Figure taken from Arnason et al, 2009.
In 2010, Onacha et al again jointly analyzed microearthquake and magnetotelluric data to investigate seismic shear wave splitting and electrical resistivity polarization that could indicate the presence of aligned, fluid-filled fractures.
The
microearthquakes were located with a double-difference tomography algorithm and
mainly followed a NW-SE trend and were shallower than 3.5km. The northern section
of the geothermal field was associated with a high Vp velocity and high resistivity.
16
S-wave splitting and MT polarization were aligned in a NW direction, similar to the
alignment of the microearthquakes.
Based on these previous studies, we expect the earthquakes located in our study
to follow a general NW-SE trend, be located in a depth range of 1-3km, and fall
along a boundary of high and low resistivity. Since previous studies focused their joint
interpretation efforts qualitatively along 1-dimensional profiles, we hope to learn more
about the relationship between resisitivity and velocity with a 3-dimensional analysis.
1.4
Iceland Deep Drilling Project
The Iceland Deep Drilling Project (IDDP) is a result of the combined efforts of the
Icelandic government and energy companies to improve the economics of geothermal
energy production by drilling into supercritical zones of hydrothermal fluid (Fridleifsson & Elders, 2007). When subsurface fluid is above the critical temperature and
pressure, the resulting combination of low-density liquid and high-density gas phases
is called a supercritical fluid. If properly drilled, the steam from one well penetrating
a supercritical high temperature, high pressure zone can generate 40-50MWe of electricity, which is approximately ten times higher than current single-well production
levels.
Three active geothermal sites in Iceland have been selected for the IDDP - Reykjanes, Hengill, and Krafla (Fridleifsson & Elders, 2007). At each of these high temperature fields, it is believed that supercritical fluids can be reached by drilling to
the relatively shallow depth of 4-5km into rocks with a temperature of 400-600'C. If
a hydrothermal convection cell is located near the supercritical point, the upflow can
bring the fluids closer to the surface, possibly to a depth of only 3.5km (Fridleifsson
et al, 2003). Figure 1-2 shows the subsurface temperature-pressure relationship in the
supercritical zone. If the temperature and pressure of the system is too high, magma
will be present instead of supercritical fluid. Since the Krafla geothermal field currently produces 60MWe from an average of 15-17 drill holes at a time (34 drill holes
in total), the addition of one supercritical well could almost double the output of the
17
field.
Temperature
8600 C
400*C
Boiling point
curve
2km
(
U0
Hyrt
Hydrothermala [Supercritical point
convection cell
(-
6km
f
Magma
Figure 1-2: A pressure-temperature plot shows the optimal conditions for a supercritical zone. Note the convection cell brings the fluids closer to the surface. Figure
adapted from Fridleifsson et al, 2003.
The Krafla geothermal area has many sub-fields of production based on the
recharge region, the fluid content of the rocks, and the major anion in the fluid,
which dictates acidity. The Leirbotnar field is liquid dominated at 190-220'C to a
depth of 1000-1400m, with a two-phase system (liquid and gas) at 300'C underneath
it (Armannsson, 2010). Three fields follow a boiling point curve with a two-phase
system at 300 C. The Hvitholar field has boiling liquid to a depth of 1000m, with
a temperature reversal and cooler fluid underneath it. In general, the chemistry of
the overall Krafla geothermal area is dominated by a two-phase system, which occurs
in almost every production zone at some depth (Armannsson, 2010). A map of the
region is provided and further discussed in section 5.3.
Due to the shallow magma chamber heat source at the Krafla volcano, the supercritical zone was expected at a minimum depth of 3.5km where temperatures reach
18
450-550'C. However, when the test well was drilled in June 2009, an unexpected flow
of rhyolitic magma entered the well at a depth of 2104m and caused the drill bit to
become stuck (SAGA Report 8, 2009). The rocks were much hotter at this depth
than expected and the well had to be completed prematurely. Thus, the supercritical
zone was unfortunately not reached because the well was located too close to the
magma chamber. In this study, we aim to explore whether joint imaging methods
could have better predicted the location of the magma and possible outflow zones.
1.5
Project Goals
The overall goal of this project is to determine whether joint geophysical imaging
methods could be used to better image the magma chamber in the Krafla volcano
to guide drilling operations in the future. We focus mainly on interpreting seismic
data acquired from a borehole network from 2008 to 2011 and integrating it with
existing data sets from surface networks operated from 2004-2007. We then compare the velocity model from the seismic inversion with a resistivity model from an
electromagnetic inversion for a joint interpretation. The step-by-step approach is:
1. Process raw borehole network microseismic data and pick earthquake phases
2. Use two tomography methods to invert P- and S-phase arrivals for earthquake
locations and velocity structure
3. Study earthquake locations relative to velocity transitions or anomalies
4. Interpret velocity model in terms of subsurface structure
5. Compare the earthquake locations and velocity models of two tomography programs
6. Attempt to correlate the velocity model with a resistivity model derived from
magnetotelluric data to obtain further insights into subsurface structure
19
20
Chapter 2
Methods and Data Analysis
2.1
Introduction
Seismic data from three networks at the Krafla volcano in NE Iceland were used in
this study. Two networks consisted of surfaces stations and provided pre-picked Pand S-phases for events occuring between 2004 and 2007. The third network was a set
of borehole stations with raw field data and required processing. The borehole data
were filtered below the Nyquist frequency and then downsampled to 10 samples/sec
and 100 samples/sec. The 10 samples/sec data were used to analyze power spectral
density and station cross correlations and allow for a visual inspection of data quality.
These techniques were used to select two subsets of the borehole data, one from 2008
and one from 2011. Then, the 100 samples/sec version of the subset data was hand
picked for P- and S-phase arrivals.
2.2
The Seismic Network
Our study area is located in the northeast corner of Iceland at the Krafia volcano
and geothermal field (Figure 2-1). The seismic network consists of three separate
networks - two surface networks (one operated by the University of North Carolina
(UNC) and one operated by Duke University) and one borehole network. The UNC
and Duke University networks consisted of a combined total of 61 stations, though
21
all stations were never operating at one time. The borehole network consisted of
6 stations. The entire seismic network was approximately 4km in the North-South
and East-West directions. Cataloged picks and initial locations were obtained for the
UNC and Duke networks that were operated from 2004 to 2007, respectively, from
ISOR (Iceland Geosurvey). The borehole network data were continuously recorded
from 2006 to 2011 and were provided as raw, unprocessed field data in Reftek format
(proprietary data format by Refraction Technology, Inc.).
Krafia study region
Figure 2-1: Study area (from http://mmillericeland.wordpress.com/tag.iceland).
Figure 2-2 shows a topographic map of Krafla with the three station networks
and the IDDP well. The topography map is an ASTER Global Digital Elevation
Model (GDEM), with sampling every 30 meters and a vertical accuracy of 7 to 14
meters. The inner and outer caldera at Krafla are also outlined on the map, clearly
showing that the entire seismic network is contained in the inner caldera. The network
dimensions are approximately 4km in the north-south direction and 3.5km in the eastwest direction. The latitude, longitude, surface elevation, and station elevation of the
borehole stations are shown in Table 2.1.
22
900
65.78A
0
Borehole Stations
UNCDuke Stations
800
700
65.74 -
D
600
65.72 0
t
65.7*
I
500
00
0.1
65.68
300
65.66
200O
65.64--
65.62
-17
-16.9
-16.8
Longitude
100
:.pw
-16.7
-16.6
Figure 2-2: The basemap shows the location of the seismic networks (red triangles =
borehole network, blue circles = University of North Carolina and Duke University
stations, cyan star = Iceland Deep Drilling Project well) on top of the topography and
outlines of the two calderas at Krafla. The color bar refers to the topography contours
in meters above sealevel. ASTER Global Digital Elevation Model is a product of the
Ministry of Economy, Trade, and Industry (METI) of Japan and NASA.
23
Station
98CE
98D4
9AA3
9AE9
9BDA
9871
Latitude
65.7098
65.7247
65.7022
65.6908
65.6884
65.7172
Borehole Network Stations
Longitude Surface Elevation
0.517
-16.7629
0.579
-16.7545
0.622
-16.7301
0.473
-16.8072
0.454
-16.7603
0.545
-16.7819
Geophone Elevation
0.439
0.495
0.546
0.400
0.387
0.485
Table 2.1: Location of the borehole network. Surface elevation is the height in km
of the ground surface above sea level and geophone elevation is the height of the
seismometer in km above sea level. Note that the geophone elevation is always less
than the surface elevation because the seismometer is in the borehole.
2.3
Data Processing
The data from the additional borehole seismic network were converted from Reftek
to mini-seed format (a data format for continuous seismic data) and then processed
in Matlab. The data for each of the three seismometer components of each of the six
stations were saved in separate, one-hour long files, so the first processing step was
simply to construct day long files for each station component. The data were acquired
at 200 samples/sec, with brief and sporadic periods of 1000 samples/sec acquisition.
We corrected for these different sample rates by low-pass filtering the high sample rate
data below the Nyquist frequency (at 80Hz) before downsampling to 200 samples/sec.
Thus, we had a stream of one day files, sampled at 200 samples/sec.
The data were then resampled separately to 10 samples/sec for data quality control
checks and 100 samples/sec for earthquake event picking. We again low-pass filtered
below the Nyquist frequency (at 4 and 40Hz, respectively) prior to resampling. When
we previously downsampled the 1000 samples/sec data segments to 200 samples/sec,
we noticed that there were short periods of no data acquisition at the transitions
between the times when data were recorded at different sample rates. The time
vector did not increase at a regular rate during these times. Therefore, we created
a new time vector at the lower sample rates and linearly interpolated the data. A
spline interpolation was also tested, but required four times the computation time
of the linear interpolation method and did not result in a measurable difference in
24
output.
The power spectra of the 10 samples/sec processed data were analyzed to better
understand the frequency content of the data, noise levels, and overall station health.
The periodogram function in Matlab was used to return the power spectral density
(PSD) of the day-long data sequences at each station. The PSDs for each day were
plotted to show the power spectrum over the course of the acquisition period and to
allow better visualization of temporal changes in amplitude. Figure 2-3 shows three
examples of PSD results. Station 9AA3 showed the most stable power spectra of all
the stations - the amplitude of the spectrum is relatively constant over the entire
frequency spectrum and time period. Stations 9BDA and 98CE demonstrate some
of the problems revealed by the PSD analysis. Station 9DBA has a stable frequency
spectrum until early 2009, at which point the amplitude increases for all frequencies,
indicating high levels of noise. The noise does not quiet down until the middle of
2011, showing that it will be difficult to pick phase arrivals from mid-2009 to mid2011 at this station. Station 98CE shows a different problem - the horizontal bands
of high amplitude and constant frequency indicate ringing from mid-2008 to 2011. It
is possible that the station is not securely coupled to the wall of the borehole and
thus oscillates when it receives a signal. Again, this behavior implies that picking
phase arrivals may prove difficult. The navy blue vertical bands show times when the
stations were turned off. The combination of understanding when the stations suffered
from high noise, abnormal behavior, and lack of data (station turned off) helped us
select two time periods when the most stations were operational and acquiring high
quality data.
25
10-3
10-3
10-2
10-2
N
~
cc 10-1
100
101
0-1
100
101
Jul-19-08 Aug-23-09 Sep-27-10
Day
(a) Station 9AA3, component 1
Jul-19-08 Aug-23-09 Sep-27-10
Day
(b) Station 9BDA, component 2
High
10.3
10-2
N
0
(r- 10-1
U-
100
101
Low
Jul-19-08 Aug-23-09 Sep-27-10
Day
(c) Station 98CE, component 2
Figure 2-3: All plots show a log scale of frequency on the y axis versus time (day) on
the x axis. The color shows the amplitude of the power spectral density, with blue
showing a low amplitude and red indicating high. Navy blue vertical bands indicate
the station was turned off.(a) Station 9AA3 shows a relatively consistent amplitude,
indicating flat frequency content. (b) Station 9DBA shows high amplitude across all
frequencies, indicating high levels of noise contamination. (c) Station 98CE shows
horizontal bands of high amplitude, indicating station ringing.
26
2.4
Clock Errors and Correction
The internal seismometer clocks were regulated by GPS and reset when the internal
clock drifted from the GPS time. The internal clocks occasionally ran fast, so the
GPS timer would correct the value by resetting the clock back in time. Thus, we
had short periods in the data stream where time did not increase linearly. Instead,
data from before the clock adjustment overlaid data from after the adjustment for a
brief time. We assumed that the timing of the data directly after the time reset was
more accurate than that before the reset, and we simply deleted the first occurrence
of the overlapping data (see Figure 2-4).
Then, the station time would increase
monotonically, with one data point associated with each time point. Since the time
resets generally occurred when sensors were restarted from servicing, we assumed that
errors from deleting the extra data instead of compressing it would be negligible. This
clock correction was applied after the data were downsampled to 200 samples/sec but
before the resampling to 10 samples/sec and 100 samples/sec.
In addition, the original field data headers were examined to determine the level
of clock drift recorded by the seismometer. In the time periods of the data subsets,
the clock drift was on the order of picoseconds (10-12), much less than the aquisition
rate of the instrument (at most 10-3 seconds).
Pairwise cross correlation of waveforms can be used to retrieve the Green's functions and estimate the stability of timing in a seismic network (Sens-Schonfelder,
2008). We generated cross correlation plots of the waveform data between pairs of
stations in the borehole network to detect severe relative time lags. Station 9AA3
was selected to be the reference station for all of the correlations since the power density specrum analysis indicated that it had low noise levels for all three components
of motion and was more consistently operating than the other stations. The cross
correlations were performed over two frequency bands, 0.02 to 0.2 Hz and 0.1 to 1
Hz.
27
- -
c
0
-
Station clocks
match GPS
Station clocks
run fast
deleted
reset
reset
GPS time
Figure 2-4: Illustration of Attempt to correlate the velocity model with a resistivity
model derived from magnetotelluric data fthe clock resets. The station clocks ran
faster than the real, GPS time. Without a clock error, the station time curve would
fall along the dotted line. If the station clocks to follow the red curves, this indicates
a deviation from the GPS time and the clock is reset.
Ideally, we wish for the amplitudes of the correlation to be symmetric across the
time lag axis and constant across all days of aquisition. High amplitude noise across
the time lag axis at a given measurement time indicates high noise at one or both
stations. If the correlation data were noisy across both calculated frequency bands,
we can determine that the noise frequency spectrum is relatively flat (broadband)
and there should not be unusual artifacts introduced into waveforms.
Figure 2-5 shows an example cross correlation for the vertical component of motion
at stations 9AA3 and 9AE9. At the very beginning of 2008, a large amplitude -20s
time lag appears on the correlation plot, followed by a +60s time lag for a couple
months. These large amplitude time lags imply that the time on station 9AE9 was
operating incorrectly, since phase arrivals took 20 seconds less and then 60 seconds
more to arrive at 9AE9 with respect to 9AA3. We can conclude that the noise was
28
from station 9AE9 and not 9AA3 by looking at the other correlation pairs. Since all
stations were referenced to 9AA3, any noise due to the malfunction of 9AA3 would
have appeared on all correlation plots, which it did not. From mid-2008 to late-2010,
the high amplitude signal is concentrated near and symmetric around a 0 second
time lag. This behavior is consistent with both stations being well timed, since phase
arrivals were recorded by the two stations at similar times, which we expect because
the seismic network is small and surrounding the earthquake epicenters. Finally,
the data become very noisy as we move up into late-2010. The amplitudes appear
uncorrelated between the two stations, likely caused by high noise levels at one station.
Sep-23-11
Jan-1 5-11
-----
Jun-29-10
Oct-29-09
Apr-03-09
Jul-26-08
-80 -60 -40
-20 0 20 40
Time lag (seconds)
60
80
Figure 2-5: The cross correlation of the vertical component data from station 9AE9
relative to 9AA3 indicates the relative time lag between the stations and overall
station health. It is clear that at the beginning of aquisition (early 2008) there is a
-20 second lag, followed by a +60 second lag, and then finally no lag when the figure
is symmetric about zero seconds. In 2011, one of the stations suffered from noise
contamination, visible from the high amplitude noise across the entire range of time
lags. Time is on the vertical axis, with time lag in seconds on the horizontal.
29
These correlation plots were used in addition to the power spectral density plots
to select the data subsets to be analyzed. We aimed to select two subsets of the data
without time lag offsets and minimal noise across all stations. We ultimately selected
one set near the beginning of the aquisition period from September 1-21, 2008, and
one set near the end of the aquisition period from June 23 - July 24, 2011. The large
time difference between the sets was chosen specifically so we could look for spacial
changes in the distribution of earthquakes.
2.5
Microearthquake Event Detection
The 2008 and 2011 subsets of the the 100 samples/sec data were imported into Antelope 5.1 (developed by Boulder Real Time Technologies, Inc) and the arrival times of
the detected earthquakes were picked by hand on the broadband signal. In general,
P-phases were picked on the vertical component, while S-phases were picked on one
of the two horizontal components. If the signal was not clear on a station component
or there appeared to be a large time offset (greater than 5 seconds) between P-phase
arrivals for the same event on different stations, the phases were not picked. During 2008, station 98CE was almost always unusable because it suffered from continual
ringing, presumably because it has poor contact with the borehole wall. During 2011,
station 9BDA suffered from large, periodic spikes that introduced an unacceptably
high level of noise to the data. Therefore, in each of the subsets, data from only
five stations were useable. Occasionally, 12 phases could be picked over the six stations, but in general, a single event consisted of 8-10 phase arrivals picked over 4 or
5 stations. Phases were grouped into events by associating phases occurring within 5
minutes of the initial P-phase.
247 events were picked from the September 1-21, 2008 time period and 225 events
were picked from the June 23 - July 24, 2011 time period. Figure 2-6 shows the
temporal distribution of the events. In the 2008 data subset, there were an average of
12 events per day. The rate of event occurrence shows a sharp decrease in frequency
over the last four days, which was due to instruments turning off. On September 15,
30
station 98CE turned off, and on September 18, station 9AA3 turned off. With the
loss of two stations in the 6-station network, each event needed to have clear phase
arrivals on all of the remaining four stations in order to be included in the analysis.
By September 22, stations 98D4 and 9BDA had also turned off, so no further picks
were possible. In the 2011 data subset, there were an average of 7 events per day,
with no apparent change in event rate during the analysis time window. Overall, the
decrease in rate of picked events from 2008 to 2011 is likely due to an increase in the
background noise of the network, which makes detection of the first arrivals difficult.
31
201816-
S14-
4 2 SepG-1-(
Day
(a) 2008 Subset of Events
18
16
a14
c12
c
W 10
'6
.0
a) 8
zz3
6
4
2
0
June-
-11 July-14-11 July-21-11
Day
(b) 2011 Subset of Events
Figure 2-6: Temporal distribution of events in the two data subsets. The 2008 data
set is 21 days long, with an average of 12 events per day, while the 2011 data set is
32 days long and has an average of 7 events per day.
32
Several representative waveforms weill be discussed. Figure 2-7 shows six traces
from an event on September 2, 2008. Station 9871 (traces 1-3) has a good signal
to noise ratio, with the P-wave arrival clearly appearing on the vertical component
trace and the S-wave arrival appearing on both horizontal component traces. Station
98CE (traces 4-6) suffers from ringing (as shown in the power spectral density analysis,
section 2.3), the envelope of which is visible on the vertical component trace. The
two horizontal components are noisy, with no identifiable change in amplitude or
frequency at the time of the phase arrivals. No picks were made on such noisy data.
Figure 2-8 shows three traces from an event on June 23, 2011, and demonstrates clear
phase arrivals on all components because of the excellent signal to noise ratio.
3
15
Figure 2-7: Phase arrival on stations 9871 (traces 1-3) and 98CE (traces 4-6) on
September 2, 2008. The P-phase arrival is marked on the vertical component (trace
1, red flag), and the S-phase arrival is marked on one of the horizontal components
(trace 2, red flag). The final traces associated with station 98CE demonstrate the
difficulty of picking on a ringing station. Ticks indicate 0.5 second increments.
33
2
Figure 2-8: Example of an excellent signal to noise ratio and clear phase arrivals at
station 98CE on June 23, 2011. The P-phase arrival is marked on the vertical component (trace 1, red flag), and the S-phase arrival is marked on the second horizontal
component (trace 3, red flag). Ticks indicate 0.2 second increments.
34
Chapter 3
Inversion Techniques
3.1
Introduction
This chapter will begin by describing the overall theory of seismic inversion and then
specifically discuss the two tomography programs (TomoDD and PStomo-eq) used to
locate the microseismic events and develop a velocity model for the region. TomoDD
was first introduced by Zhang and Thurber (2003) as an extension of the doubledifference location scheme, HypoDD, of Waldhauser and Ellsworth(2000). PStomo-eq
was introduced by Tryggvason (1998) as a local earthquake tomography method that
incorporated a simultaneous inversion of S-wave velocity structure to the P-wave
inversion method of Benz et al (1996). Both algorithms use the conjugate gradient
method LSQR during the inversion to minimize the root mean square difference of
the model and the data, but have different constraint equations and inital parameter
settings. We close with a brief description of the methodology for the magnetotelluric
inversion and a discussion of the initial parameter settings since we will eventually
compare our velocity model to the resulting resistivity model.
3.2
Seismic Inversion: Tomography
Tomography algorithms invert seismic data (P- and S-wave arrival times) to determine
event hypocenter location and a three dimensional velocity model. Using ray theory,
35
the arrival time, T'k, of a body wave at seismic station k from earthquake i, is a
function of the origin time of the event,
TL,
T'k = T i
and a path integral of the slowness u:
+f
U ds
(3.1)
where 1 is the path between the earthquake and the station. The arrival time of the
earthquake is related to source and receiver locations and the velocity structure of the
Earth. Thus, deriving event locations and velocity structure from arrival times is a
non-linear process and is often approximated with a truncated Taylor series expansion
of equation 3.1. We then parametrize the problem by breaking up the model space
into a set of constant slowness cells and separating the perturbations of the hypocenter
locations and slownesses. The difference between the calculated and observed arrival
times is linearly related to spacial perturbations of the hypocenter (xi, X 2 , X3 ) and
perturbations to the velocity model (6u) through the following equation for the travel
time residual:
ri-
k
6
_
1=1
+
xilAXz__
1 +
u ds
j u
(3.2)
We can set up the matrices for the inversion with the basic formulation d = Gm,
with the left hand side containing the data (the travel time residuals for P- and Swaves), and the right hand side containing the Jacobian (the partial derivatives of
the travel times with respect to hypocenter location and the cell slownesses) and the
model parameters (the perturbations of the hypocenter locations and the event origin
times).
We make the following assumptions to constrain and enhance the system of matrices to one that is more stable:
1. Ray paths outside of a given cell are unaffected by changes in slowness of adjacent cells, so the forward model does not need to be recomputed at each
iteration
2. Controlled source data can be added as extra constraints and calibration values
36
3. The model slownesses (inverse velocity) is smoothed to increase stability of the
system and increase the geologic feasibility of the solution
4. A priori weights can be added to further constrain the data (such as normalization factors for data quality or Vp/Vs ratios)
We end up with a system of matrices in the following form that can be solved
iteratively with the conjugate gradient LSQR method of Paige and Saunders (1982):
W
d
=W
G
m
(3.3)
Al
0
where W contains the additional a priori weights and AI contains the damping and
smoothing factors, with d, G, and m as previously defined.
The output of the LSQR algorithm will be the perturbations to the model parameters of hypocenter location and velocity structure, along with the root mean
square difference between the model and the observed data, which reduces with each
iteration.
3.3
TomoDD, A Double Difference Tomography
Algorithm
Overview of TomoDD
Zhang and Thurber (2003) developed an efficient double-difference tomography algorithm using both relative and absolute arrival times to simultaneously invert for event
hypocenter location and a three dimensional velocity model. An absolute arrival time
of a phase is recorded according to GPS time and is independent of other events. Absolute arrival times can be used to locate the absolute position and origin time of the
event. A relative arrival time measures the time of a phase arrival relative to another
time, for example, relative to the expected origin time, a phase arrival on another
earthquake, or a fixed point in time. Often, clusters of earthquakes hypocenters are
37
located relative to each other using relative arrival times. The forward problem of determining ray paths and calculating travel times between events and stations is solved
with a pseudo-bending ray-tracing algorithm (Um & Thurber, 1987). Event location
is improved from previous algorithms with the addition of waveform cross-correlation
technique to measure more reliable relative travel times and including quality weights
with the relative arrival times.
If we write equation
3.2 for each event detected at the station k, we can pair
events (for example, events i and j) that have similar hypocenter locations. We will
assume that the path from the event to the receiver and velocity model are nearly
identical for these paired events. Therefore, any difference in travel time for these
events will be due to small differences in path length (see Figure 3-1) and/or velocity
heterogeneities near the hypocenter. The "double difference", represented as dr 2 k, is
the difference between the calculated and observed travel time residuals of the two
events:
dri3 k = rik
-
rik = (Tik - Tik ) obs
-
(Tik -
Tjk)cal
(3.4)
The observed travel time differentials can be calculated either from picked values
in the collected data (called "catalogue" values), or from cross-correlating similar
waveforms (called "WCC"). If the waveforms are sufficiently similar, which they most
likely will be for paired, adjacent events, the cross-correlation technique can greatly
enhance the accuracy of the relative arrival times, and therefore, reduce the root mean
square error of the model.
The travel time residuals provide an excellent constraint on the relative locations
of the event hypocenters and the velocity heterogeneities between them. The absolute
arrival times of the events helps constrain the absolute locations of the event clusters
and determine the velocity structure outside the event region near the receivers.
38
Receiver k
Event i
far from events
Eventj
path length difference
Figure 3-1: When two events are close to each other and far away from the seismic
receivers, the ray paths are nearly identical. Any difference in travel times is caused by
small differences in path length (shown), or velocity hereogeneities near the sources.
Figure adapted from Concha 2008.
Starting Model Parameters for TomoDD
Because our data sets from the UNC and Duke seismic networks only contained
picks and not waveforms, we were unable to use waveform cross-correlations for the
observed travel time residuals. Thus, all our observed catalogue values come from the
arrival time hand picks (for the borehole network) and the catalogued arrival times
(for the other networks). The coordinate center for the TomoDD inversion region was
set at the IDDP well and 11 iterations of the LSQR algorithm were run.
TomoDD weights different data types (catalogue travel times versus waveform
cross correlation travel times, and P-wave versus S-wave) by different amounts at
each iteration of the inversion. Since we do not have cross correlation information,
the only weighting changes are for the cataloged P- and S-wave residuals. As shown
in Figure 3-2, the weights increased as the iterations progressed, but the P-waves were
always weighted more than the S-waves. Hypocenter estimates are very sensitive to
incorrect S-wave arrival times, so it is important to make sure an incorrect identification does not further skew the inversion (Gomberg et al, 1990). However, because
the seismometers were located in boreholes, there is less noise than on the surface,
39
and the S-waves seemed as reliable as P-waves, if not more so, and were clear to pick.
*--- data weighting and re-weighting:
* NITER:
last iteration to used the following weights
* WTCCP, WTCCS:
weight cross P, S
* WTCTP, WTCTS:
weight catalog P, S
* WRCC, WRCT:
residual threshold in sec for cross, catalog data
* WDCC, WDCT:
max dit [km] between cross, catalog linked pairs
* WTCD:
relative weighting between absolute and differential data
* THRES: Scalar used to determine the DWS threshold values
* DAMP:
dampina (for L= only)
*
--- CROSS DATA ----- ---- CATALOG DATA -* NITER WTCCP WTCCS WRCC WDCC WTCTP WTCTS WRCT WDCT WTCD DAMP JOINT THRES
1
8.1 0.1
7 -9
0.1
8.88
5
9 10 50 0 0.2
1
8.1 0.1
-9
-9
.1 0.08
9
9 10 80 1 0.2
1
8.1 0.1
8 -9
8.1
8.08
8
9 10 80 1 0.2
1
0.1 0.1
7 -9
0.1
0.08
8
9 10 50 0 0.2
1
0.1
8.1
7 -9
8.1
0.08
7
9 18 80 1 0.2
1
0.1
0.1
7 -9
0.1
0.08
7
9 10 50 0 0.2
1
0.1
0.1
7 -9
1.0
0.8
7
9 .1 80 1 0.2
1
0.1 0.1
7 -9
1.0
8.8
7
9 .1 80 0 0.2
1
8.1
0.1
7 -9
1.0
0.8
6
9 .1 80 1 0.2
3
8.1
0.1
7 -9
1.0
0.8
6
9 .01 80 8 0.2
3
0.1
0.1
7 -9
1.0
0.8
6
9 .01 80 0 0.2
Figure 3-2: The initial weighting parameters for TomoDD. The catalogue weights are
boxed, since waveform cross-correlation data were not available.
The initial velocity model was the same for both TomoDD and PStomo-eq, and
was modified from Riedel et al (2005). Figure 3-3 shows the initial one-dimensional
P-wave velocity model that was used. Due to the limited aperture of our seismic
network and the shallow depth of events, our best constrained imaging region was
from sea level to about 3km depth. Therefore, we essentially used a constant starting
velocity of Vp = 3.4 for our entire model. The S-wave velocity starting model was
constructed by dividing the P-wave model by 1.78.
40
-2
-1
4
Velocity (km/sec)
Figure 3-3: The initial layered velocity model for both TomoDD and PStomo-eq. The
original model extended to 40km depth. The region that was best constrained by our
seismic data was from approximately sea level to 3km depth, which had a constant
Vp value of 3.4 km/sec.
3.4
PStomo-eq, A Finite Difference Tomography
Algorithm
Overview of PStomo-eq
This technique builds on the algorithm of Benz et al (1996) by tracing the ray paths
for P- and S-waves independently to simultaneously invert for event hypocenter location and the P- and S-wave velocity models. This method places more emphasis on
rigorous weighting and including additional constraints, such as an a priori Vp/Vs
ratio, and focuses on transforming the data to separate the hypocenter and slowness perturbations. First arrival times are solved with the finite difference solution
41
of the Eikonal equation in three dimensions, which is able to solve for transmitted
and reflected body and head waves in complex geometries of velocity heterogeneities
(Tryggvason, 1998).
PStomo-eq transforms the data d from equation 3.3 before solving with LSQR.
As explained in Tryggvason (1998), G is the Jacobian, containing the spacial derivatives corresponding to hypocenter perturbation and time derivatives corresponding
to slowness perturbation. These two parameters can be resolved into two separate
components to separate the hypocenter dependence from the slowness perturbations
by decomposing the spacial derivatives with SVD. This decomposition is performed
for each earthquake in the system, resulting in a series of matricies we will call UT,.
When this matrix is multiplied through the right hand side of equation 3.3, the terms
containing hypocenter perturbations cancel to zero because of the orthogonal properties of UT . The resulting system of matricies consists of the set of data transformed
by this parameter separation process and model parameters that only include slowness perturbations (Pavlis, 1980). The final system of equations for the inversion that
can be solved with LSQR is written as:
B'
d'
BC
dc
=
0
kL
0
iS
Au = GAu
(3.5)
where the prime indicates transformed data and travel time derivatives, c indicates
controlled source data and travel time derivatives, B are slowness perturbation derivatives, L are the smoothing equations for the slowness with weighting k, S is the Vp/Vs
ratio with weighting 1,and Au are the slowness perturbations (the model data). These
resulting slowness perturbations are used to relocate the earthquakes. To enforce the
Vp/Vs ratio, the cross gradient between the two parameters is minimized.
The model space is divided into grid cells of equal size and constant velocity.
Ray tracing and travel time calculations can be computed on a smaller grid with
interpolated velocity values (Tryggvason, 1998).
42
Starting Model Parameters for PStomo-eq
PStomo-eq used the same grid cell size of 125x125x125m for the forward problem
computations and the inversion calculations for 6 iterations. As explained previously,
the same starting model was used for both PStomo-eq and TomoDD and is shown in
Figure 3-3.
3.5
Magnetotelluric Inversion
Data Acquisition and Processing
The magnetotelluric (MT) data were obtained between 2004 and 2006 by several
research groups at 102 sounding sites with 15 frequencies recorded between 0.003 and
300Hz at each site. The network was approximately a square of 10km on a side,
roughly centered around the IDDP well. The sounding sites were closer together at
the center of the network than at the edges.
The inversion of the data was conducted at Lawrence Berkeley National Laboratory in Berkeley, California, by Erika Gasperikova and Greg Newman. For completeness, a brief description of the inversion algorithm follows, with an interpretation of
the results to be discussed later.
Theory
Magnetotellurics uses the Earth's naturally occuring broadband electromagnetic (EM)
field as a source to image underground resistivity structure. These EM fields arise
from thunderstorms around the world and from the interaction of solar winds with
the Earth's magnetosphere. Since the EM sources are in the far field, we assume the
waves are planar once they reach Earth and that they propagate vertically through
the subsurface.
The electric field (E) is written as a vector equation:
V x [t V x E + ipoo-E = 0
p
43
(3.6)
with y- as the magnetic permeability of the medium, yo as the permeability of free
space, and o as the electrical conductivity. The magnetic field (H) can be derived
from the electric field with Faraday's Law, as:
H =V x
E
-iwp
(3.7)
Under the conditions V - oE = 0 and V - E = 0, which force the electric field to be
divergence-free in the Earth and the air, the forward model of the problem consists
of solving for the electric and magnetic fields at each point on the grid.
The electric and magnetic fields are simply related by the impedance, E =
[Z]H.
To solve the inverse problem, we need to determine each component of the complex
impedance tensor (equation 3.8) at all locations in the model space.
Z=
Zxx
Zyx
ZXY
Zyy
(3.8)
An objective function is constructed with the data error (difference between observed
and calculated impedance) and smoothness constraints and is solved with a non-linear
conjugate gradient method. Preconditioning is added to improve the convergence rate
and the inversion is implemented in parallel to reduce the solution time. Apparent
resistivity and impedance phases quantities, which are more intuitive to interpret
than the full complex impedance, can be obtained by manipulating the off-diagonal
elements of the impedance tensor.
Starting Model Parameters
The initial resistivity model for the inversion consisted of five layers varying in
depth: a resistive surface layer, a shallow low-resistivity layer, and intermediate highresistivity, a deep low-resistivity, and a relatively resistant basement. A staggered
grid mesh was used to help enforce boundary conditions and take surface topography
effects into account.
44
3.6
Model Comparison
The results obtained using the two tomography algorithms will be visually compared
to each other with two perpendicular cross sections and four depth slices. We will
first compare the number of earthquakes successfully relocated and included in the
inversion with the original number of input events. In addition, we wish to examine
the clustering of the relocated events: Are there multiple clusters across the region,
or is there one main area of activity? Are the event cluster(s) aligned spacially or in
depth? Since the initial velocity model is constant through our region of greatest sensitivity (sea level to 3km depth), we expect to avoid earthquake alignment problems
associated with discontinuities in the velocity model.
We are also interested in comparing the wavelength of the velocity changes between
the two tomography inversions. Abrupt changes in velocity are unlikely to be geologic
in nature and are more likely due to outliers in the data that were insufficiently
smoothed. Because of our relatively tight grid spacing (300m in TomoDD and 125m
in PStomo-eq) compared to the scale of geophysical property changes, we are unlikely
to encounter aliasing effects. Overly broad features, on the other hand, may show an
insensitivity to anomalies or an unconstrained region of the model.
The aperture of the seismic network significantly affects the region of detectable
earthquakes. However, the distribution of earthquakes within that region will determine the ray coverage and constraints on the model. We are interested to see how
the two programs are effected by reduced ray coverage with increasing depth.
Finally, we compare the final velocity models to the resitivity model resulting from
the magnetotelluric inversion. A visual comparison of cross sections will be made for
correlated structure. We also construct a crossplot of the velocity from TomoDD and
resistivity parameters in order to provide a more quantitative comparison. Since the
velocity and resisitivity grids are different sizes (300m and 100m, respectively), the
velocity grid was interpolated onto the resistivity grid for the entire volume of the
model. The cross plot was constructed from a smaller cube (4x4x3.lkm) to reduce the
signal introduced from unchanged edge values from the initial velocity background.
45
46
Chapter 4
Results
4.1
Introduction
The results obtained using each tomography method (TomoDD and PStomo-eq) will
be presented separately. The velocity models (Vp, Vs, and th4 Vp/Vs ratio) are
compared with six cross sections (as shown in Figure 4-1). Two 6km-long vertical
cross sections through the IDDP well (South-North and West-East) were made. Four
horizontal slices (6km on a side) were made at depths of 1km ("layer 25", 0.995
- 1.120km in PStomo-eq), 1.5km ("layer 28", 1.375 - 1.5km in PStomo-eq), 2km
("layer 32", 1.875 - 2km in PStomo-eq), and 2.5km ("layer 36", 2.375 - 2.5km in
PStomo-eq). In all cross sections, the location of the IDDP well is marked with a
vertical white line. The IDDP well is located at (0,0) in all of the depth slices, but is
not marked. Finally, we will compare the velocity model obtained by TomoDD with
the resisitivity model made from the MT inversion.
47
IDDP well
EW section
Depth = 1km
Depth = 1.5km
Depth = 2km
Model
Depth = 2.5km
6km
N
E
W
s
6km
-
i
Figure 4-1: The 2 cross sections and 4 depth slices of the velocity model are centered
around the IDDP well.
4.2
Tomography Results
TomoDD Results
Of the 810 earthquakes (from the Duke network in 2004, the UNC network in 2005,
and the borehole network subsets from 2008 and 2011) that were initially located by
TomoDD, 687 events, or 84%, were successfully relocated and used in the inversion.
Most events were located in a depth range of 1-3km, shown in Figure 4-2. Overall,
the relocation clustered the events more closely to the center of the caldera and
aligned in a NW/SE direction compared to the initial locations (see Figure 4-3(a)).
The south-to-north cross section of Figure 4-3(b) more clearly shows the effect of
the relocation and the cluster's relationship to the IDDP well. The entire cluster of
events was shifted approximately 1.5km deeper and offset to the North relative to
the inital locations. The IDDP well terminates at 2km depth, directly meeting the
plane of earthquake locations. At the northern edge of the profile between latitudes
of 65.72'N and 65.73'N, several dozen events appear to be aligned nearly linearly at
2km depth. This linear feature is also visible in map view (Figure 4-3(a)), oriented
48
NW/SE and centered at (65.725'N, -16.765'E). Even though TomoDD relocated most
of the events to one main cluster, some events were relocated 1.5km above sealevel,
which indicates an error since the surface elevation is 500-600m. These air events are
also visible in the histogram in Figure 4-2.
350
m 300 c)
250 200 -
.o
0
4)
150 2
4
E lo00
z
50
4
-202
6
8
10
Depth (km)
Figure 4-2: Histogram of depths of events located by TomoDD. The vast majority of
events are located between 1 and 3km depth.
The P-wave, S-wave, and Vp/Vs ratio models along the south-to-north cross section (NS) are shown in Figure 4-4. The P-wave velocites vary from 3.1 to 5.2km/sec
and the S-wave velocities vary from 1.9 to 2.9km/sec. Local velocity lows are represented by the velocity contours dipping below the background model values, while
local highs are shown by an increase from the background model. There are local
velocity lows visible in the cross section about 1.5km north and south of the IDDP
well. There are local velocity highs north of the IDDP well in the shallow part of the
section (about 1km north) and in deeper parts (about 200m north). Unfortunately,
the velocity ratio profile is difficult to interpret. The Vp/Vs ratio is not constrained
in TomoDD, so the ratios were obtained by simply dividing Vp by Vs and are likely
unreliable. However, we still note that the main cluster of earthquakes is located in
a region of a low Vp/Vs ratio.
49
Inner Krafla Caldera
o Earthquakes
o Relocated Earthquakes
*IDDP "iI
A Stations
0
00
65.735 -
0
0
65.73 -
0
0
0%0
0
1
65.725
0
0
A
0
0
65.72I- 0
0
65,715 00
0
65.71 mi
0
00
0
o
00
0
00
65.705 -
A
0
0
65.7 1-
00
0
0
0A
65.695 -
A
0
00
A
65.69
F
At
0
A
0
0L
A
A
A
0
0
0
AL
A
65.685 --
4L
AL
0
AA
-1 6.84
-1 6.82
-1 6 8
-1 6.78 -1 6.76
Longitude
-16.74
-16.72
-16.7
-1668
(a) Map view of events located by TomoDD, with network stations in red.
0
2
0
1
3 04>
0
0
Earthquakes
Relocated Earthquakes I
00
0
o
0
3*25
o0 oo
00
0
o0
0
0
0
0
0
0
<9
'0
o
-
P
0
0 0
0
0
0
5-
0
60
65.685
-00
o
0
0
1-
S
0
&
0
o0
S
0"
-1 -
65 69
65 695
65.7
65.705
65.71
Latitude
65.715
|
|
65.72
65.725
65.73
65.735
(b) NS cross section projection of all events within the inner caldera, red line = IDDP well.
Figure 4-3: Original earthquake locations are shown in blue, with the relocated events
shown in black. The relocated earthquakes are more tightly clustered than the original
events and fall generally along a NW-SE trend at a depth of 1-3km.
50
-0.5
5.5
0
5
0.5
1
4.5
1.5
2
=3.5
2.5
3
-3
-2
-1
0
1
2
3
Y(km)
(a) Absolute Vp at NS cross section.
-0.5
3.2
3
0
0.5
2.8
E
14
2.4
1.5
2.2
2
2
2.5
3
-3
-2
-1
0
Y(km)
1
2
3
1.6
1.
(b) Absolute Vs at NS cross section.
1576
1 75
0
1 74
1 73
172
Z
71
1.51
17
ise
2.5
3
-2
-1
0
1
2
3
1 67
Y(km)
(c) Vp/Vs ratio at NS cross section.
Figure 4-4: NS cross section through the IDDP well. Located earthquakes within
300m of the section are shown as black dots, and the IDDP well as a white line. Note
that the color bars are different in the three figures.
51
-0.5
.5
0
5
0.51
E
C.5
1-
1.51
4
21
35
2.5
3
-2
-1
U
X~krn)
2
3
3
(a) Absolute Vp at EW cross section.
-0 5
32
3
0
28
05
26
1
24
d
1.5
22
2
1.8
2]5
1.6
-3
z
X(km)
(b) Absolute Vs at EW cross section.
-0.5
1 76
1.75
0
1,74
0.5
- 1.73
1.72
1.71
1.7
1.69
25
1 68
1
3
-r
-i
U
X(km)
1 67
(c) Vp/Vs ratio at EW cross section.
Figure 4-5: EW cross section through the IDDP well. Located earthquakes within
300m of the section are shown as black dots, and the IDDP well as a white line. Note
that the color bars are different in the three figures.
52
The west-to-east cross section (EW) through the IDDP well is shown in Figure 45. As expected, the range of Vp, Vs, and Vp/Vs ratio values are the same as for the
NS section. We notice a local P- and S-wave velocity highs to the west of the well
and a P-wave velocity low to the east of the well. The Vp/Vs ratio profile remains
difficult to interpret. There seems to be little correlation in local changes in velocity
to location of the earthquakes along this section.
Figure 4-6 shows Vp at constant depths with earthquakes projected from 300m
above and below the slice. A 2km-wide velocity low is evident to the south of the
IDDP well in the top slice (1km depth). As we move deeper into the model, a small
velocity high emerges in the center of the overall low. A coherent velocity low south
of the IDDP well is visible on all slices. The earthquakes are mainly located at the
boundary between the high and low velocity regions.
The upper left hand corner of Figure 4-6(a) shows an example of the smoothing
of anomalous velocity values that stick out of the background model. These outliers
are easy to see when the velocity changes are relatively coherant, however, on deeper
slices, it becomes more difficult to determine if small changes in velocity are due to a
real signal with sparse coverage or an outlier. This heavy smoothing is necessary for
the model because the grid spacing (300m) is coarse relative to the coverage of the
seismic network (4km).
Figure 4-7 shows the Vs variations at constant depths with earthquakes projected
from 300m above and below the slice. In contrast to the patterns of Vp variation, the
Vs anomalies are at a higher velocity than the background model. A local high to the
south of the IDDP well is evident on all depth slices, and a second high to the west
is visible on the shallow slices. In general, earthquakes are located in these regions of
higher S-wave velocity.
Figure 4-8 shows the Vp/Vs ratios along the depth slices. There is a low velocity
ratio to the south of the IDDP well that surrounds most of the events. By the lowest
depth slice (2.5km), the plot appears to be noise, since a coherant signal for both the
P- and S-wave models at that depth is difficult because of decreasing ray coverage.
53
3
2
I,
.6
.5
.4
.4
.3
3
-1
3.9
3.8
-2
37
-3-
-z
-1
3.6
u
X(km)
U
X(krn)
(b) Vp at 1.5km depth
(a) Vp at 1km depth
3
5
5.3
.9
2
52
.8
I
5.1
.7
9
.5
-z
-I
U
Xkm)
z
3
5
0
.6
-3
-3
-39
-1
.4
.8
.3
.7
.2
-31
-3
-Z
-1
U
.6
X(km)
(d) Vp at 2.5km depth
(c) Vp at 2km depth
Figure 4-6: Horizontal slices at (a) 1km, (b) 1.5km, (c) 2km, and (d) 2.5km depth of
absolute Vp from TomoDD. There is an area of relatively low Vp near the origin of
the map in the upper sections. Located earthquakes are shown as black dots. The
IDDP well is located at the origin of the plot. Note that the color bars are different
in the four figures.
54
2.5
2 65
245
2
26
2.4
2 55
2.35
2.3
2
2.25
2.2
-1
25
0
i
-1
24
2.15
2.1
-2
2.45
2 35
-2
2.05
-3
-3
-'
-I
4
U
x~km)
23
-31
-3
a
X(kM)
(a) Vs at 1km depth
(b) Vs at 1.5km depth
3.1
2.8
3.05
2
2.75
3
1
2.7
295
2 5
2.9
2.65
-1
.2.85
-1
2.8
2.55
-2
2.75
25
-3E
-3
-z
-I
U
X(km)
Z
-3-3
.3
-2
-1
U
Z
.5
2.7
X(km)
(c) Vs at 2km depth
(d) Vs at 2.5km depth
Figure 4-7: Horizontal slices at (a) 1km, (b) 1.5km, (c) 2km, and (d) 2.5km depth
of absolute Vs from TomoDD. There is an area of relatively high V, near the origin
of the map in the shallow slices. Located earthquakes are shown as black dots. The
IDDP well is located at the origin of the plot. Note that the color bars are different
in the four figures.
55
3
1.85
21.8
3
1.85
21.8
1
1.75
1
1.75
0
1.7
0
17
-1
1.65
-1
1.65
-2
1.6
3
-2
-1
0
1
2
3
-33
1.55
-2
-1
x(km)
0
1
2
3
1 55
X(km)
(a) Vp/Vs at 1km depth
(b) Vp/Vs at 1.5km depth
3
1.85
3
1 85
2
1.8
2
1.8
1
1.75
1
175
01.7
1.7
-1
3
-2
-1
0
x(kmn)
1
2
3
1.65
11.65
1.6
-2
1.55
-3
(c) Vp/Vs at 2km depth
16
-2
-1
0
xtemn)
1
2
3
1.55
(d) Vp/Vs at 2.5km depth
Figure 4-8: Horizontal slices at (a) 1km, (b) 1.5km, (c) 2km, and (d) 2.5km depth
of the Vp/Vs from TomoDD. As expected, there is an area of low Vp/Vs ratio south
of the IDDP well, where most of the events are clustered. Located earthquakes are
shown as black dots. The IDDP well is located at the origin of the plot. The color
bars are the same for all figures.
56
PStomo-eq Results
Almost twice as many events were included in PStomo-eq as TomoDD. All of the
additional events were from the UNC and Duke data set and were picked on fewer
stations (3 or 4) than the other events. Even though the data were less well constrained because there were so few picks, we included the extra events to determine
the robustness of the PStomo-eq algorithm. Out of the 1554 events initally located
by PStomo-eq, 1306 (84%) were relocated and used in the inversion. Figure 4-9 shows
that like TomoDD, the majority of events were located between 1 and 3km depth.
The relocated PStomo-eq events are shown in Figure 4-10 relative to those relocated
by TomoDD. In map view, we see that the events are similarly clustered at the center of the caldera, aligned to the NW. In the cross-section view, we noticed that
even though we have nearly twice as many events as from TomoDD, the clustering
by PStomo-eq is as tight and well aligned along a horizontal plane centered at 2km
depth. PStomo-eq locates only one or two events in the air, but seems to have an
alignment of events at 0.5km depth, which are likely mislocated.
500
450
to
4) 400
2150 -
%.
50
0100 1o
s -
2
0
2
4
6
8
Depth (km)
Figure 4-9: Histogram of events located by PStomo-eq. The vast majority of events
are located between 1 and 3km depth.
57
0
0
65 735
o
-
00
0
0
_0
* IDDPwell
A Stations
r,
0
65.73 -
InnerKrafla Caldera
o TomoDD locations
o PStrno locations
00
0
65.725 -
0
0
65.72
0
0
0
65.715
0
E
.W
0
0
0
~0
0
0
65.71
00
0 V
~o
65,705
o
o
0O.
0
00
0ooo
65 7-
o08
04
A
0
A0
0
S
00
0
A
A
0 0
0
Coo
0
0
65.95
65.691..
A
0
o
oA
0A
0
0
0
0
0
0
A
A
0
4k
0
65.685
a
A
-1684
-1682
-16.8
-1678
-16,76
Distance, km
-16,74
-16.72
-167
-16.68
(a) Map view of events located by PStomo-eq.
-2,
O2
Latitude
(b) NS cross section of all events within the inner caldera.
Figure 4-10: Earthquake locations from PStomo-eq are shown in blue, with a comparison to the TomoDD locations in red. Even though PStomo-eq has twice the number
of events as TomoDD, they are tightly clustered and fall generally along a NW-SE
trend. The IDDP well is shown in black. 58
A three dimensional visualization of the earthquakes better shows the alignment.
When viewed from the northwest (Figures 4-11(a) and 4-11(c)), the earthquakes seem
vertically aligned, whereas from the southwest, they appear flat on a plane (Figures 411(b) and 4-11(d)). This could represent a vertical transform fault between parallel
fissures oriented to the northeast.
2,
$.*........
0
00
0.0
00
0
C)-3 -
0
000
0-6,
-1
-4 -
0
*
0
00
0
-16 7
65 6
65 7
65.65
.
0
6575
657
65.8
-1685
Latitude
-168
-16.75
Longitude
(a) Earthquakes located by TomoDD.
'656
-167
(b) Earthquakes located by TomoDD.
2
0
0
00:
00
-1
0
0
-
-1
-c
.0-a
0.
a)
0
-
-2
-3
0
0
-c
4-a
'0
0.
a)
0~
.
~0o00
.
0
-4
0
0
-5 -
0
00
. .....
-16.8
8
(c) Earthquakes located by PStomo.
-16&85
-16.8
-16.75
P65.8
00
65.7
-16.7 16
Longitude
(d) Earthquakes located by PStomo.
Figure 4-11: Visualization of the earthquakes in three dimensions from perpendicular
points of view shows that they are located along a vertical plane aligned to the NW.
59
Figures 4-12 and 4-13 show the NS and EW cross sections generated from PStomo eq.
The individual Vp and Vs models are similar to TomoDD, showing slightly low velocities to the south and east and a higher velocity to the north. The velocity ratio
profiles, however, show a coherant image since an a priori constraint is applied to the
P- to S-wave velocity ratio. A black square represents a low saturated value, and a
white square represents a high saturated value on the colorbar scale in Figures 4-12(c)
abd 4-13(c). Along the NS profile, we see clear low ratio values between 1 and 2km
depth. Along the EW profile, we see a high Vp/Vs ratio to the west of the well and
a low ratio to the east.
The depth slices from the PStomo-eq model are shown in Figures 4-14 to 4-16.
Earthquakes are only shown on the first two depth slices because they obscured the
velocity characteristics of the lower depth slices. An area of low Vp velocity is evident
just south of the IDDP well in Figure 4-14, flanked with high velocity anomalies to
the east and southwest. The velocity changes are most clearly seen in the slice at a
depth of 1.5km, where two velocity lows and three velocity highs are clustered near
the IDDP well site and a concentration of microearthquakes. Even though we begin
to lose ray coverage by 2km depth, a local velocity low remains coherant on the depth
slice.
The Vs model, shown in Figure 4-15, closely follows the pattern of the P-wave
velocity anomalies. One notable difference between the Vp and Vs models is seen in
the third and fourth depth slices (2 and 2.5km), where the outer fringe of the model
has a lower velocity than the center - in the P-wave model, the edge values were the
same or higher than the values in the center.
The Vp/Vs ratio model shows a low to the south west of the IDDP well at the
section closest to the surface. The low region migrates toward the center of the region
(and the IDDP well) with increasing depth. Again, this image is much more coherant
than those seen in TomoDD because the velocity ratio is set as a constraint to keep
the model from diverging from a geologically reasonable solution.
60
-1
5.5
0
1
4.2
2
3
3.0
0
2
4
6
(a) Absolute Vp at the NS cross section.
-1
3.2
0
1
2.4
2
3
1.7
2
0
4
8
(b) Absolute Vs at the NS cross section.
-1
1.74
0
1
I
1.72
2
3
1.68
2
0
4
6
(c) Vp/Vs ratio at the NS cross section.
Figure 4-12: NS cross section, with the IDDP well shown in white. Color bars are
different for the three figures.
61
-1
5.5
0
1
4.2
2
3.0
30
0
2
4
6
(a) Absolute Vp at the EW cross section.
-1
3.2
0
1
2.4
2
3
1.7
0
2
4
6
(b) Absolute Vs at the EW cross section.
-1
1.74
0
1
1.72
2
1.69
30
2
4
6
(c) Vp/Vs ratio at the EW cross section.
Figure 4-13: EW cross section, with the IDDP well shown in white. Color bars are
different for the three figures.
62
2
4.6
F6
%d
ag4.4
*2
1
1
S0
-3
--
-2
-1
0
1
X (km)
2
3
4.2
04.5
4.1
-4.4
(a) Vp at layer 25 (depth 0.995 - 1.12km)
-3
-2
-1
0
1
X (km)
2
3
(b) Vp at layer 28 (depth 1.375 - 1.5km)
4.9
2
E
5.2
3
2
E
5.1
4.8
-2
-3
-3
2Wlw
-2
-1
0
1
X (km)
2
3
4.7
(c) Vp at layer 32 (depth 1.875 - 2km)
.3
-3
-2
-1
0
1
X (km)
5.0
2
3
(d) Vp at layer 36 (depth 2.375 - 2.5km)
Figure 4-14: Horizontal slices centered around (a) 1km, (b) 1.5km, (c) 2km, and (d)
2.5km depth of absolute Vp from PStomo-eq. There is an area of relatively low Vp
near the origin of the map and areas of high Vp to the south and east of the center.
Located earthquakes are shown as dots. The IDDP well is in the center of the plot.
Note that the color bars are different for the four figures.
63
2.5
2.4
2.7
.
2.4
(a) Vs at layer 25 (depth 0.995 - 1.12 km)
-2
-1
0
1
X (km)
2.5
2
(b) Vs at layer 28 (depth 1.375 - 1.5km)
i 2.88
3
3.0
2
0
.*
2.84
3.0
i
*
1-2-S'
2.80
'-3
-2
-1
0
X (kIn)
1
2
3
-3
(c) Vs at layer 32 (depth 1.875 - 2km)
-2
-1
0
X (kIn)
1
2
3
2.9
(d) Vs at layer 36 (depth 2.375 - 2.5km)
Figure 4-15: Horizontal slices centered around (a) 1km, (b) 1.5km, (c) 2km, and (d)
2.5km depth of absolute Vs from PStomo-eq. There are areas of relatively high Vs to
the southwest and east of the origin and low Vs in the southeast. Located earthquakes
are shown as dots. The IDDP well is in the center of the plot. Note that the color
bars are different for the four figures.
64
1.7
1
1.7
.X1
-2
-3
-2
-1
w"
0 1
X (km)
BIN 1.6
2
3
..
3
-2
-1
0
1
X (kn)
1.6
2
3
(a) Vp/Vs at layer 25 (depth 0.995 - 1.12km) (b) Vp/Vs at layer 28 (depth 1.375 - 1.5km)
3 .
1.7
3
1.7
2
2
x
1
1.7
t
0
1.7
-1
X
-2
-e
-3
-2
-1
0
1
X (km)
2
3
1.6
(c) Vp/Vs at layer 32 (depth 1.875 - 2km)
-3
j-
3
-2
-1
0
1
X (km)
23
1.6
(d) Vp/Vs at layer 36 (depth 2.375 - 2.5km)
Figure 4-16: Horizontal centered around (a) 1km, (b) 1.5km, (c) 2km, and (d) 2.5km
depth of the Vp/Vs from PStomo-eq. As expected, there is an area of low Vp/Vs
ratio near the center of the map to the south of the IDDP well, where more of the
events are clustered. Located earthquakes are shown as dots. The IDDP well is in
the center of the plot. The color bars are the same for all figures.
65
4.3
Comparison of Tomography with Magentotellurics
The earthquake locations and velocity model from TomoDD were compared to the
resistivity model from the magenrtotelluric inversion. Earthquakes within 500m offaxis of the NS and EW cross sections are plotted on top of resistivity cross sections
in Figure 4-17. The earthquake locations seem to be clustered around regions of low
resistivity, for example, on the south side of the IDDP well on the NS section. A visual
comparison of the PStomo-eq velocity ratio cross section seems to show a correlation
between the top of the low ratio region from the velocity plot and the underside of the
low-resistivity cap from the magnetotellurics. Along the EW velocity cross section,
the boundary between the high and low ratio zones to the west of the IDDP well may
be correlated with the vertical contour of the low resitivity region.
Figure 4-18 shows the corresponding horizontal depth slices to compare to the
velocity sections. The low resistivity zone in the northwest corner of the model is
common to all depth slices. The wavelength of the resistivity model changes is at
least 4km (looking at peak-to-peak amplitude changes). Since the wavelength of the
velocity model is approximately 1km, it is very difficult to compare the models as
most of the velocity anomalies occur entirely within one band of resistivity. However,
the main cluster of earthquake locations is at the center of the plot, which appears
to be a saddle point in the resistivity model, with relatively high resistivity values to
the NE and SW and low resistivity values to the west and east.
Cross plots were made between the log10(resistivity) and the velocities generated
by TomoDD to look for correlations between the two parameters. As described in
the methods (Section 3.6), the cube volume for the cross plot was smaller than the
original model cube to reduce edge effects of the initial model parameter settings.
Figure 4-19 shows the results of the cross plot for a 4x4x3.1km cube, centered in
the xy plane at the IDDP well. A positive correlation between velocity (Vp and Vs)
and resisitivity is evident, consistent with previous studies (Wilkens & Schultz, 1988;
Jackson et al, 1993; Werthmuller et al, 2012). The red overlay line shows the trend of
66
the highest density of the points. The vertical stems extending down from the overall
trend are an artifact of the remnant background values, even though the cube was
cut down. Since the resistivity model did not have constant background values in the
area we examined (the sides of the resistivity cubes are 10km long), the resisitivity
varies in regions where the velocity background is constant. Figure 4-19(c) shows
that the velocity ratio is generally within a range of 1.65 to 1.75 for all resistivity
values. This wide range of values is expected since TomoDD does not constrain the
velocity ratio.
Cross plots were also made along the depth slices of the model cube to determine if
the velocity-resistivity relationship changes with depth. Results at depths of 1km and
2km are shown in Figure 4-20. Again, a vertical line shows the background velocity
value of the slice. Overall, there are no siginificant changes to the cluster shape as
depth increases. These individual slices demonstrate another visualization of the final
model velocity values compared to the initial values: Vp shows most values lower than
the background, Vs shows higher values than the background, and the Vr/Vs ratio is
lower than the background.
67
3.5
0.5
3
0
-2.5
-1.5
1.5
-2
1
-2.5
3
-2
-1
0
Distance, km
1
2
0.5
(a) log1O(resitivity) along NS cross section
1
3.5
0.5
3
0
2.5
-1.5
1.5
1.5
-2.5
-33
-2
-1
0
1
2
3
0.5
Distance, km
(b) log1O(resitivity) along EW cross section
Figure 4-17: Resistivity model plotted with earthquakes within 500m of the section. The IDDP well is shown as a white line. Note that the color bars represent
log10(resistivity) and are the same for all plots.
68
6574
65 735
6573
65 725
6572
65715
65.71
65705
65.7
65 695
6569
Longitude
Longftude
(a) loglO(resitivity) at 1km depth
(b) loglO(resitivity) at 1.5km depth
65,74
65.74
65735
65.735
65.73
65.73
65 725
65 725
65 72
65.72
65715
S65 715
65 7
65.7
65.7
65.695
65 695
65.69
-1682
Longitude
-168
-16.78
-16.76
-16.74
-16.72
-16
Longitude
(c) loglO(resitivity) at 2km depth
(d) loglO(resitivity) at 2.5km depth
Figure 4-18: Resistivity model depth slices. The scale bars are labeled with latitude
and longitude, but the box is the same 6x6km square used previously. Note that the
color bars represent log10(resistivity) and are the same for all figures. The IDDP well
is located at the exact center of the figures.
69
2
19
|
18
17 -
I'
146
1.3
35
4
45
Vp
5
55
6
(a) Cross plot of resistivity versus Vp.
2,
19
1.8117
16
R71I
815
I
'1
14
13
2
1.8
22
24
2.6
2.8
3
3.2
vs
(b) Cross plot of resistivity versus Vs.
2
1,9 1.8
171.6
1.5
-I
1.4
13
15
1 55
16
1,5
17
vp/Vs
1 75
18
1 85
19
(c) Cross plot of resistivity versus Vp/Vs
Figure 4-19: Plots of velocity versus resistivity at every point in the model cube. The
red lines show overall trends.
70
1.6
17
1.55
15
-[
1.45
1 55 -
1.411.51.35
1.45 1.3
14
12
37
3.8
3.9
4
41
42
44
43
45
4.6
Vp
47
48
49
Vp
(a) Resistivity versus Vp at 1km depth.
(b) Resistivity versus Vp at 2km depth.
1.6
1.75
1.55
17
1 65-
15
~s
~*t~3:.
~
4.
1 45|8.*
4.
1.4
"6k
.' . .
1 55
.4....
4...
44)4*4*
.4......
~.
8
1.5
1 35
1.45
13
1I
1.4
2 2
225
2.3
235
24
2.45
25
255
26
2. 3
Vs
2.75
28
285
(d) Resistivity versus Vs at 2km depth.
1.6e
1 75
17
1.55
1.65
15
I
27
VS
(c) Resistivity versus Vs at 1km depth.
1 45
2.65
*
1.6
I-
1.55
1.4
1.35
I145
1.3
1.25
1. 5
. .
1.411 55
1.6
1.65
1.7
Vr/vs
1 75
18
185
1.9
1.6
1.65
1.7
1.75
1.8
185
Vp/Vs
(e) Resistivity versus Vp/Vs at 1km depth.
(f) Resistivity versus Vp/Vs at 2km depth.
Figure 4-20: Plots of velocity versus resistivity for individual depth slices in the
model.
The velocity-resistivity relationship does not change significantly with in-
creasing depth.
71
72
Chapter 5
Discussion and Conclusions
5.1
Introduction
We will begin our discussion with an analysis of the local borehole network coverage
in comparison to the overall Icelandic seismic network. Then, we will give a brief
structural interpretation of some of the anomalies seen in the horizontal sections of
the velocity models generated by PStomo-eq. Finally, we try to better understand
the velocity-resistivity cross plot with modeling of synthetic data and recognize some
of the complexities associated with combining data of different wavelengths.
5.2
Borehole Network Coverage
The Icelandic Meteorological Office (IMO) operates a digital seismic network (SIL)
of 55 stations across the country to monitor earthquakes, crustal deformation, and
volcanic activity. A map of the the twelve closest SIL stations to the Krafla volcano
are shown in Figure 5-1. Events detected on this network contained in the inner and
outer calderas from January 2010 through September 2011 are shown in Figure 5-2.
Most of the events are located within the inner caldera, consistent with results of
our earthquake locations. Events that were detected by the SIL network during July
2011 (the second time period selected from the borehole network) are shown in green
(Figure 5-2).
73
Figure 5-1: Station locations (black triangles) of the Icelandic Meteorological Office
SIL seismic stations near the Krafla volcano.
Only 18 events were detected in September by the SIL network, a significantly
smaller number than the 225 detected by the borehole network during the same
period. The borehole network is likely able to detect more earthquakes of smaller
magnitude in the Krafia caldera region because of the station positions and the location of the sensors in boreholes. The borehole network is positioned very close to the
region of highest seismicity identified by the SIL network, so the highest sensitivity
area of the network is focused on this seismically active region. Sensors located at
the bottom of boreholes generally operate with reduced noise (McNamara 2004), so
we are more likely to detect events of very small magnitude. Thus, we can be relatively confident that the spacial distribution of the events is geologic and not forced
into a specific orientation based on the geometry of the borehole network. Events
are located deeper by the SIL network compared to the borehole network, which is
not surprising since the spacial extent of the SIL network is so broad. However, the
events are still clustered with a highest density between 1 and 3km depth, which is
consistent with borehole results.
74
65.8
1
I
I
Interior Krafla caldera area
65.76 -
0
65.72
65.68 -
-17
-16.9
-16.8
-16.7
-16.6
Longitude
(a) Map view of seismicity.
1
-
:.
.. 4
2
-17
-16.9
-16.8
-16.7
-16.6
Longitude
(b) Cross section of seismicity.
Figure 5-2: Seismicity detected on the Icelandic Meteorological Office SIL network
near Krafla from January 2010-September 2011. The black inset box shows the area
of the inner caldera, and the red dots represent events detected in July 2011.
75
5.3
Structural Interpretation
Since the Vp/Vs ratios are difficult to interpret alone, we will look at the Vp, Vs, and
ratio models holistically from PStomo-eq. Two regions of relatively high Vp and Vs
are evident: one striking to the southwest from the IDDP well and the other to the
east of the well. These high velocity zones are most likely due to cold intrusions. The
southwest area of our study region is part of the Leirbotnar field (shown in Figure 53), which is believe to have large gabbro intrusions into the basalt below 1400m, so
we may be detecting these bodies (Gudmundsson et al, 2002).
In the center of our study region, we see low Vp values and low Vp/Vs ratios.
The Leirbotnar field has a liquid phase to a depth of 1000-1400m, at which point
it switches to a two-phase, or super-critical, system (Armannsson, 2010). Our area
of emerging low Vp/Vs with depth could be an indication of a fractured media with
gas in the pore spaces (Zheng and Lay, 2005).
In that case, the S-wave velocity
would be unchanged because the waves would propagate through the rock matrix.
However, the P-wave would be slowed compared to rocks with fluid-filled powers
because acoustic waves propagate through fluid at close to the rock velocity, but at
much lower velocities through gas.
Northwest of the well, a high Vp velocity anomaly is visible without a corresponding Vs anomaly. This suggests the possibility of a partial melt situation - S-waves are
slow compared to P-waves because of the more fluid nature of the intrusion. This area
corresponds to a region believed to be magma in the resistivity model. In the future,
it would be interesting to see if a joint inversion with these reinforcing constraints
could better define the area of partial melt.
76
65.74
--
65.73
IDDP well
65.72
65.71
Lr otnar
SUd
65.7
656
lficar
65.68
s9
65.67
-16.85
-16.8
-16.75
Longitude
-16.7
Figure 5-3: The four main geothermal field locations in the inner Krafia caldera are
shown. The Viti crater, fissure swarm, IDDP well, and geothermal power station are
also shown. The blue box is the 6x6km region of the model with the cross sections
through the IDDP well.
This map was compiled by combining information from
Christenson et al, 2010; Gudmundsson et al, 2002; Armannsson, 2010; and ONacha
et al, 2010.
77
5.4
Synthetic Modeling of the Velocity-Resistivity
Relationship
Since the crossplot of velocity and resistivity show a positive correlation, we tested
whether the data could be fit with the simple relations of velocity and resistivity
to porosity. From Archie's law, we know that resistivity is inversely proportional to
porosity with the equation R oc a#'. We assume the constant a is equal to 1 since it
simply represents a DC shift, and set m = 2. The Wyllie time-average equation is
10000
104
-e-elocity
-O-Resistivity
BOOO0
10
6000
10 2
4000
2000
0.2
Porosity
0.3
0.4
2500
3000
3500
4000
Velocity
4500
5000
(a) The relationship of velocity and resistiv- (b) Crossplot of the two parameters in (a).
ity to porosity.
2
/
14
13
3
35
4
45
Vp
5
55
6
(c) Overlaying the curve from (b) on real data
shows a good fit.
Figure 5-4: The overall velocity-resisitivity relationship closely follows porosity models.
78
used to relate the P-wave velocity to porosity:
1-= VW
VP
+
VR'
, where Vp is the
P-wave velocity, Vw is the velocity in the pore spaces (estimated at 1500 m/sec), and
VR
is the velocity in the surrounding rock matrix (estimated at 5000 m/sec). Over a
porosity range of 1-35%, we show the relationship of the two parameters to porosity
in Figure 5-4(a) and to each other in Figure 5-4(b).
By translating this curve up
and down, we can fit the data from the P-wave cross plot reasonably well, shown in
Figure 5-4(c). Thus, we can see that the overall model volume is consistent with rock
physics models.
However, the cross plot results of the individual depth slices are still difficult to
understand, so we created synthetic cross plots to try to better understand the cluster
shapes of those plots. We simplified the problem to a ID model of the Vp along the
NS cross section through the IDDP well at a depth of 2km. We take the velocity as
a constant value of 4km/sec, perturbed with a positive anomaly of 5km/sec in the
center flanked by two negative anomalies of 3km/sec. The resistivity was constructed
by linearly scaling the velocity according to the relationship estimated from the red
line in Figure 4-19(a), R = 12.066V + 19.95. With this simple model, we examined
the effect of smoothing, noise, and deviations from the linear relationship.
While comparing the cross section plots of the velocity and resistivity data (for example, Figures 4-6 and 4-18), it is clear that a difference exists between the smoothness
and anomaly size detected by the two models. The velocity model can be affected
by one or two grid points having an anomalously high value, while the resistivity
model shows smooth changes of greater wavelength. The distance between "peak-topeak" anomalies in the velocity model is on the order of 1km, while it is closer to
4km for the resistivity. Therefore, we can reasonably expect there to be a difference
in the apparent smoothing of the models. We can test this with our sythetic data
by smoothing the velocity and resistivity data with separate filters of different size.
Filtering was acheived by convolving a Gaussian function with the model. The size
of the filter was controlled by changing the standard deviation of the Gaussian. Since
the Gaussian function varies between 0 and 1, the overall amplitude was decreased
through filtering, though the form of the signal was conserved. One cross plot was
79
constructed between the original, unfiltered velocity and resistivity models, and another between the filtered data (excluding the convolution tails at the beginning and
end of the velocity model).
Figure 5-5 shows the results of changing the filter sizes. The left hand column
shows the unfiltered and filtered data vectors, while the right hand column shows
the resulting cross plots.
When the velocity and resistivity are filtered with the
same Gaussian filter, the cross plot follows the same linear trend as the unfiltered
data (Figure 5-5(a) and 5-5(b)).
When the resistivity is filtered with a Gaussian
having twice the width of that of the velocity, the cross plot is no longer linear
(Figure 5-5(c) and 5-5(d)). Instead, we see a pattern similar to that of the real data
for one slice of the model: a vertical stem due to a changing resitivity over constant
velocity, horizontal triplications at higher resistivities, and a "knot" at the center
of the triplication ellipse. When the velocity data is filtered with a wider Gaussian
than the resistivity, the shape of the cross plot remains the same as the previous
case, but is rotated 90' (Figure 5-5(e) and 5-5(f)).
These results suggest that the
unusual clustering of the real data is due to the velocity model's increased sensitivity
to smaller anomalies than the resistivity model.
The next three cases examine the effect of noise on the cross plots. Uniformly
distributed noise (1km/sec) was added to the velocity vector before scaling the resistivity. Figures 5-6(a)- 5-6(d) look similar to the noiseless cases, with slightly greater
scatter and "knot" size in the middle of the plot. Figure 5-6(e) and 5-6(f) show the
effect of different noise on the two signals. Additional uniform noise was added to
the resistivity model before smoothing to uncorrelate the noise while maintaining the
wavelength of the signal. The filtered crossplot does not change significantly, but the
unfiltered crossplot widens considerably. In this case, the unfiltered plot resembles
that of the entire volume of the real data, while the filtered plot resembles that of an
individual slice of the real data.
80
100
95
+Unfiltered data
-- Filtered data
90
--
90
80
70
B5
60
50
40
-Velociy
80
- - Filtered velocity
-Resistivity
- - Filtered resistivity
75
30
20
70
10
500
1000
45
1500
5
x
55
Velocity
(a) Same filter size, noiseless data
(b) Plot of velocity versus resistivity
95
100
data
90
--+Unfiltered
+
Filtered
data
85
80
75
I,
70
65
60
55
50
500
1000
x
5
1500
4
45
5
55
6
Velocity
(c) Larger R filter size, noiseless data
(d) Plot of velocity versus resistivity
100 r
+ Unfiltered data
-e- Filtered data:
90
80
70
60
80
50
VelocRy
Filtered velocity
40
Resistivity
30 - - Filtered resistivity
- -
20
70
10
500
1000
x
3
1500
3.5
4
45
5
55
6
Yelocity
(e) Larger V filter size, noiseless data
(f) Plot of velocity versus resistivity
Figure 5-5: Effect of smoothing filter size on crossplot characteristics. Different sized
smoothing filters leads to a breakdown of the linear relationship between velocity and
resistivity.
81
100
95
90
-+-Unfiltered data
Filtered data
--
80
70
60
80
50
1 75
40
70
30
651
20
10
Yelocity
(a) Same filter size, noisy data
(b) Plot of velocity versus resistivity
100
100
90
80
70
60
I
50
40
30
20
10
X
Velocity
(c) Larger R filter size, noisy data
(d) Plot of velocity versus resistivity
100
I,
45
Velocity
x
(e) Larger R filter size, separate noisy data
(f) Plot of velocity versus resistivity
Figure 5-6: Effect of noise content on crossplot characteristics.
82
Finally, we examined the possibility that certain resistivity anomalies could not
follow the same linear relationship as the overall model. We kept the same noisy
velocity vector from the previous cases and introduced independent positive anomalies
to the resistivity data. Jackson et al (1993) demonstrated that rocks with different
properties can be recognized by differences in slope of the velocity-resistivity curve.
We thus decreased the slope and increased the y-intercept of the velocity-resistivity
scaling equation to create the anomaly. In Figure 5-7(a), the resitivity anomaly has
been added to a region where the background velocity is constant. The anomaly is
clearly visible on the cross plot of the unfiltered data as a line segment of a different
slope above the rest of the points. The change in the cross plot of the filtered data
(Figure 5-7(b)) is more subtle - the vertical stem is extended above the triplication
region, showing that the resistivity changes as the velocity remains constant. The
next case shows the resistivity anomaly added in the same location as a velocity
anomaly, effectively canceling it out (Figure 5-7(c)). The anomaly is again evident
on the unfiltered crossplot as a discontinuous line segment, and seems to spread out
the triplication vertically on the filtered data (Figure 5-7(d)). This last figure closely
resembles the data seen in Figure 4-20(b), implying that the spread seen by the data
clusters could be caused differing relations between velocity and resistivity. However,
since the overall trend of the entire cube is relatively linear without obvious outliers
(Figure 4-19(a)), we can assume anomalies are of relatively small scale or due to
resolution errors of the inversion models. The EM data has more complete coverage
of the region, potentially providing additional information and constraints where there
is no seismic coverage.
83
100
X
VeloCty
(b) Plot of velocity versus resistivity
(a) Resistivity anomaly on unchanging velocity
100
90
80
MIM
M1AUI
70
50
-Yelocity
- - Filtered Yelocity
40
30 ---
Resistlyrty
Fillered resistivity
20 --10-
Velocity
X
(d) Plot of velocity versus resistivity
(c) Resistivity anomaly on changing velocity
Figure 5-7: Effect of resistivity anomalies on crossplot characteristics.
5.5
Understanding the Complexities of the VelocityResistivity Relationship
Analyzing seismic and electromagnetic data jointly is difficult because the wavelengths
of the two phenomena are different and they lack a common physical parameter
(Werthmuler et al, 2012). Both of these issues must be addressed in order to move
forward in the joint inversion process.
The size of of a detectable anomaly is generally equivalent to the wavelength of
the signal probing it. Seismic tomography uses a high frequency approximation for
84
ray tracing, giving us the potential to detect very small velocity anomalies where ever
there is ray coverage. However, due to non-optimal station geometries or non-uniform
distributions of events in the subsurface, thorough ray coverage is rarely attained. In
magnetotellurics, lower frequency signals are used. Electromagnetic signals suffer
from very high attenuation in the Earth and propagate with a meaningful amplitude
less than one skin depth into the ground. The skin depth is the distance a signal
propagates before decaying to an amplitude of 1/e, and is related to the frequency
of the signal and the resisitivity of the surrounding medium: 6 ~ 500 x
.
High
frequency signals are able to resolve small anomalies, but can only see these anomalies
at shallow depths. Low frequency signals can penetrate much deeper in the Earth,
but detect only large anomalies. The skin depth of the MT signal for the model in
this study varies from 3 meters to 3 kilometers, assuming an average resistivity of
10-100 Ohms for the half space and evaluating at the highest and lowest frequencies
(300 and 0.003Hz, respectively). Since our seismic model only extends to a depth of
3km, we fall safely within the range of high MT resolution. However, the size of the
resolvable anomlies increases with depth.
One of the goals of the joint inversion is to leverage the strengths of each method
to obtain a better combined result. The high resolution of seismic imaging can be
combined with the uniform coverage of magnetotellurics. In places were there is no
seismic coverage, the trend of the resisitivity can be used to enforce smoothness or
change in the velocity. In areas with good seismic coverage, the high resolution of the
velocity can be used to enhance the resolution of the resisitivity.
Seismic and magnetotelluric data do not have any common physical parameters.
However, both velocity and resistivity have established dependencies porosity, so it
is possible to link them (Jackson et al, 1993; Werthmuller et al, 2012). Velocities are
greatly affected by the shape and distribution of pores and flat cracks in a media,
and resistivity is greatly affected by the content and volume of the fluid or gas in the
pores (Wilkens & Schultz, 1988). Thus, obtaining a measure of the porosity from well
logs can provide crucial constraints for a joint inversion. It is possible the mineralogy
of the rocks at the surface of the geothermal field could have a greater effect on the
85
resisitivity than the porosity, so that could be another parameter that provides a
constraint on the inversion (Zhang et al, 2012). As we saw in the modeling process of
the previous section, straightforward relationships between velocity and resistivity are
difficult to identify due to the nature of the measurement methods. The difference in
the wavelength of the resolution of the velocity and resisitivity models must be taken
into account any time a quantitative comparison is made.
5.6
Future Research
Shear Wave Splitting and Anisotropy
Both Onacha et al (2010) and Brandsdottir et al (1997) found evidence of shear-wave
splitting, possibly an indication of anisotropy due to aligned, fluid-filled fractures.
During the S-wave picking for this study, there were numerous events with slightly
different S-wave arrival times on different components of the same station (two such
events are shown in Figure 5-8). It would be interesting to further investigate the
possibility of S-wave splitting in this region by finding the angle that maximizes the
amplitude ratio of the S-wave on the two horizontal components. The time delay
between the perpendicular orientations can then be found by a cross-correlation of
the amplitudes (Onacha et al, 2010).
86
Hor zontal 1
Hor zontal 2
(a) Example 1 of differing S-wave arrivals on horizontal station components
(b) Example 2 of differing S-wave arrivals on horizontal station components
Figure 5-8: Two examples of possible anisotropy in S-wave arrivals. Both examples
are from the 2008 data set, though from separate events and different stations. Notice
that the second horizontal component records an earlier S-wave arrival (red flags
indicate arrivals). Tick marks designate 0.2 second increments.
Further Joint Analysis
The ultimate goal of this project is to conduct a simultaneous joint inversion of the
velocity and resistivity model. One implementation involves imaging the seismic and
MT data separately, and enforcing structural similarity by using velocity attributes as
87
a prior constraint for the MT inversion and resistivity attributes as a prior constraint
for the seismic inversion.
Another implementation under consideration is updating the attributes for common structure using the joint inversion strategy of Gallardo (2007), shown in Figure 59. During an inner iteration cycle, the resistivity and velocity attributes are driven
to have similar structure, without regard to reducing the data errors between the
observed and calculated data. An outer iteration loop is used to enforce acceptable
fits to the respective data measurements for each inversion.
Figure 5-9: The joint inversion flow chart strategy from Gallardo (2007).
Further work will involve testing both of these methods and determining which
one has the best combination of run time, stability, and solution quality.
88
5.7
Conclusions
Overall, we determined that the earthquakes from the three seismic networks were
located in a tight cluster just south of the IDDP well, concentrated into a horizontal
layer centered at 2km depth, and oriented in a NW-SE direction by both PStomo-eq
and TomoDD. Both tomography methods yielded similar overall velocity models,
with a locally low velocity region near the surface and a locally high velocity region
moving deeper into the model. We have interpreted a high density intrusion, a gasfilled fracture zone, and a possible partial melt region based on the P-wave, S-wave,
and Vp/Vs ratio anomalies in the velocity model. A clear connection between the
earthquake locations and resisitivity values was not made, but the main cluster of
earthquake locations seems to be located at a saddle point of the resistivity model.
The cross plot of the velocity and resistivity models provided a difficult to interpret
result, but also presented an opportunity for synthetic modeling to help simplify the
situation. We show that the difference in wavelength and resolution between the
seismic and magnetotelluric data introduce complexities into joint analysis techniques.
However, awareness of this issue and the introduction of additional parameters from
rock physics can aid future joint inversion algorithms. Seismic data can introduce
higher resolution to magnetotelluric data, while the uniformity and full coverage of a
resisitivity model and constrain the smoothness of a sparse velocity model.
89
90
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