California State University, Northridge
Estimation of Permeability, Porosity, and Grain-Size Distributions Across the San
Andreas Fault Zone in Northwest Coachella Valley, CA (Riverside County)
A thesis submitted in partial fulfillment of the requirements
For the degree of Master of Science
in Geology
By
Simarjit K. Chehal
August 2014
The thesis of Simarjit K. Chehal is approved:
______________________________________
Dr. Janice M. Gillespie, Ph.D.
_______________
Date
______________________________________
Dr. M. Ali Tabidian, Ph.D.
_______________
Date
______________________________________
Dr. Richard V. Heermance, Ph.D., Chair
_______________
Date
California State University, Northridge
ii
Dedication
Dedicated to
Marilyn Hanna and My Parents
Thank you for your support
iii
Acknowledgements
Funding for this project was made possible due to the generous donations of others.
Thank you to all those who have donated to the Hanna Fellowship, Hanna Summer
Research Award, Los Angeles Geological Society Student Education Scholarship, Peter
W. Weigand Memorial Scholarship in Geochemistry, and the Graduate Thesis/Project/
Performance Support Program 2013-2014.
There are a number of people that I would like to thank, including Casey Polon and
Michael Vadman for their assistance in conducting fieldwork in the scorching desert heat.
To all the graduate students who participated in the Saturday grad-office brunches, thank
you for the company. A special thanks to Dave Liggett and Mike Tacsik for helping me
find the equipment to conduct a thesis project. Mari Flores and Terry Dunn, thank you for
all the encouragement.
Most of all, I would like to thank all those who made this thesis a reality. I express
my gratitude to all the professors who have helped me in the conception of this study,
especially Dr. M. Ali Tabidian, Dr. Kathleen Marsaglia, Dr. Vicki Pedone and Dr. Janice
M. Gillespie (CSUB). Thank you for your guidance and providing resources and answers
even when I did not know what I was asking. Last, but not least, I would like to thank my
advisor for believing in me, even when I did not believe in myself. Dr. Richard V.
Heermance, thank you for teaching me self-reliance, independence, and selfmanagement- a skill set that goes beyond a master’s degree.
iv
Table of Contents
Signature Page
Dedication
Acknowledgements
List of Figures
List of Tables
List of Equations
Abstract
ii
iii
iv
vii
x
xi
xii
Chapter 1- Introduction
1
Chapter 2- Background
2.1- Description of Area
2.2- Climate
2.3- Water Importation
2.4- Geologic Structure
2.5- Stratigraphy
2.5.1- Non-water Bearing Units
2.5.2- Semi-Water Bearing Units
2.5.3- Water-Bearing Units
2.6- Groundwater Basins
2.6.1- Desert Hot Springs Subbasin
2.6.2- Mission Creek Subbasin
2.6.3- Indio Subbasin
2.7- Fault Zone Architecture
9
9
9
12
13
23
30
39
Chapter 3- Methods
42
3.1- Field Methods
42
3.1.2- Fault Zone Architecture Characterized by Numerical Measurements
3.2- Laboratory Methods
44
3.2.1- Core Laboratories
3.2.2- Thin Section Analysis
3.2.3- Porosity- Density Analysis
3.2.4- Grain-size Analysis
3.2.5- Hydraulic Conductivity Analysis
3.2.6- Permeability Analysis
Chapter 4- Results
4.1- Field Observations
4.2- Results from Core Labs
4.3- Thin Section Analysis
4.3.1- Thin Section Analysis using Petrographic Microscope
4.3.2- JMicroVision Software
4.4- Porosity- Density Data
4.5- Grain Size Analysis
4.6 Hydraulic Conductivity
v
54
54
68
68
82
86
86
4.7 Intrinsic Permeability
91
Chapter 5- Discussion
5.1 Field Data
5.2 Core Labs Data
5.3 Thin Section Data
5.4 Porosity-Density Relationship
5.5 Grain Size Analysis
5.6 Hydraulic Conductivity and Permeability
5.7 Implications
5.8 Fractoconformity vs. Fault Zone Controlled Fluid Flow
92
92
93
94
98
99
101
109
111
Chapter 6- Conclusions
113
References
115
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Appendix F
Appendix G
Appendix H
Appendix I
119
122
133
149
151
159
163
167
202
vi
List of Figures
Figure 1-1: Schematic Fault Zone Architecture
3
Figure 1-2: Schematic of Perched Water Tables
5
Figure 1-3: Groundwater Subbasins and Subarea Boundaries
6
Figure 1-4: Schematic Comparison of Unfaulted and Faulted Water Bearing Units 7
Figure 1-5: 1936 Groundwater Surface Contour
8
Figure 1-6: 2009 Groundwater Surface Contour
8
Figure 2-1: Study Region
10
Figure 2-2: Study Region with respect to Pacific-North American Plate Boundary 11
Figure 2-3: Geomorphic Regions in California
15
Figure 2-4: Fault Strands in Study Region
17
Figure 2-5: Vegetation in Study Region
18
Figure 2-6: Geomorphic Surface Expression in Study Region
20
Figure 2-7: Alluvial Deposits in Study Region
21
Figure 2-8: Seismic Survey Interpretation of Mission Creek Strand
22
Figure 2-9: Stratigraphic Column of Northwest Coachella Valley, CA
24
Figure 2-10: Watersheds and Flow Path in Study Region
32
Figure 2-11: Gypsum Deposits along the Mission Creek Strand
34
Figure 2-12: Relationship between Faults and Fluids
41
Figure 3-1: Schematic Thin Section
46
Figure 3-2: Ernst Leitz PRADO- 500
46
Figure 3-3: Schematic Volume Water Displacement Test
47
Figure 4-1: Sampling Locations
55
vii
Figure 4-2: Location B: B4 LiDAR Analysis
56
Figure 4-3: Location RCB:B4 LiDAR Analysis
57
Figure 4-4: Location MF: B4 LiDAR Analysis
58
Figure 4-5: Location P: B4 LiDAR Analysis
59
Figure 4-6: Banning Strand at Location B
61
Figure 4-7: Location B- Fault Zone Architecture
62
Figure 4-8: Location RDB- Fault Zone Architecture
64
Figure 4-9: Location MF- Fault Zone Architecture
65
Figure 4-10: Location P- Fault Zone Architecture
67
Figure 4-11: Banning Strand Fault Zone Indices
70
Figure 4-12: Mission Creek Strand Fault Zone Indices
70
Figure 4-13: Petrographic Microscope Porosity Calculation
72
Figure 4-14: Sample B2- Thin Section
73
Figure 4-15: Sample B3- Thin Section
74
Figure 4-16: Sample MF4- Thin Section
75
Figure 4-17: Sample MF7- Thin Section
76
Figure 4-18: Sample P1- Thin Section
78
Figure 4-19: Sample P3- Thin Section
79
Figure 4-20: Sample P4- Thin Section
80
Figure 4-21: Sample P5- Thin Section
81
Figure 4-22: JMicroVision Porosity Calculation
83
Figure 4-23: Density Relationship Porosity Calculation
84
Figure 5-1: Lithofacies Identification at Location P
95
viii
Figure 5-2: Geologic Map of Study Region
100
Figure 5-3: Location B- Hydraulic Conductivity and Intrinsic Permeability
103
Figure 5-4: Location RCB- Hydraulic Conductivity and Intrinsic Permeability
102
Figure 5-5: Location MF- Hydraulic Conductivity and Intrinsic Permeability
103
Figure 5-6: Location P- Hydraulic Conductivity and Intrinsic Permeability
106
Figure 5-7: Fault Core and Damage Zone Hydraulic Conductivity VS Protolith
108
Rock Hydraulic Conductivity
Figure 5-8: Fault Core Hydraulic Conductivity VS Damage Zone Hydraulic
Conductivity
ix
110
List of Tables
Table 2-1: Lithofacies Associated with SGIM Complex
26
Table 2-2: DWR Classification Number of the Groundwater Basin
31
Table 2-3: Stratigraphy of Mission Creek Subbasin
36
Table 3-1: Sieves Used for Dry Sieving
49
Table 4-1: Fault Zone Index Calculation
69
Table 4-2: Porosity Comparison
85
Table 4-3: Effective Grain Sizes
87
Table 4-4: Uniformity Coefficients
88
Table 4-5: Breyer Hydraulic Conductivity and Intrinsic Permeability
89
Table 4-6: Slichter Hydraulic Conductivity and Intrinsic Permeability
90
Table 5-1: Uniformity Coefficients by Fault Strands
102
Table 5-2: Breyer and Slichter Hydraulic Conductivity and Intrinsic Permeability 107
by Fault Zone Architecture
x
List of Equations
Equation 3-1: Fault Zone Architectural Index
43
Equation 3-2: Average Fault Zone Architectural Index
43
Equation 3-3: Spatial Variability of Fault Zone Architectural Index
43
Equation 3-4: Water Displacement Test
47
Equation 3-5: Bulk Density
48
Equation 3-6: Total Porosity
48
Equation 3-7: Hydraulic Conductivity
51
Equation 3-8: Uniformity Coefficient
51
Equation 3-9: Breyer Equation
52
Equation 3-10: Slichter Equation
52
Equation 3-11: Intrinsic Permeability and Hydraulic Conductivity Relationship
53
xi
Abstract
Estimation of Permeability, Porosity, and Grain-size Distribution Across the San Andreas
Fault Zone in northwest Coachella Valley, California (Riverside County)
By:
Simarjit Kaur Chehal
Masters of Science in Geology
The groundwater aquifer system in the northwestern region of Coachella Valley,
California serves as a major natural resource for agricultural and municipal uses. In this
region, the aquifer system is partitioned into four groundwater sub-basins due to the
presence of the San Andreas fault zone. Previous investigation involving land surface
deformation, seismic data, and groundwater data indicate there are at least three main
strands of the San Andreas Fault- Mission Creek Strand, Banning Strand, and Garnet Hill
Strand. For years, these faults have been characterized as simple barriers to fluid flow due
to measureable offsets in the water table across the fault strands and hydrochemistry
variations. The ability for a fault to act as a barrier to flow in an aquifer system is the
result of significant development of gouge causing lateral variations in fault zone
permeability but the mechanisms for gouge development in the Coachella Valley in
unconsolidated-to-weakly consolidated sediments is unclear. Another explanation for
variations in water chemistry, temperature, and groundwater levels is due to displacement
of impermeable bedrock or relative offset of water bearing units. This study proposes to
document variations of permeability and porosity across the San Andreas Fault zone.
Field mapping, sampling and descriptions of fault zone, damage zone, and gouge width
were recorded at four fault outcrop locations in the region. Analysis of porosity,
hydraulic conductivity, intrinsic permeability, and grain size distribution across the fault
zone associated with each fault strand indicate that different regions of the fault zone can
xii
act to impede and/or enhance fluid flow across the faults. This data analysis of
permeability and porosity variations across fault zones will help develop a better
understanding of fault and aquifer interactions for future groundwater models, recharge
activities, fault displacement, and development of gouge in unconsolidated sediments.
xiii
Chapter 1- Introduction
Subsurface fluid flow systems are often modeled by geologists through a series of
differential equations and/or simplified abstractions. This approach is ubiquitous for
groundwater flow models in the southwestern United States. However, the numerical
schemes employed to create subsurface models present a sense ambiguity to the
representative model. The ambiguity associated with subsurface modeling is often a
result of sparse sampling locations and lack of data related to actual porosity and
permeability of the subsurface sediments. This presents a fundamental challenge to
modeling, where modelers must assume the physical properties associated with porous
media. Physical properties, in sedimentary rocks, such as porosity and permeability are
readily influenced by lithological variations and structural features (Freeze and Cherry,
1979, Fetter, 2001, Boggs, 2006). Petrophysical properties associated with subsurface
units can be affected by faults. Faults can disrupt the petrophysical properties of porous
media by juxtaposing strata of varying permeability, porosity, and/or lithology.
Additionally, faults are capable of producing regions of varying petrophysical properties,
near the fault plane. An increase in stress and temperature along a fault plane during a
rupture event provides the opportunity for the formation of fractures, deformation of
grains, grain realignment, and grain-size reduction (cataclasism), hence, directly
influencing fluid flow across the fault plane and causing heterogeneity in the porous
media. Fluid flow can be enhanced due to the formation of fractures and/or it can be
impeded due to the formation of fault gouge.
Faults play an important role in influencing shallow subsurface fluid migration
(i.e. Faulkner et al., 2010). Faults are composed of three distinctive zones: fault core,
1
damage zone, and protolith rock, as displayed in Figure 1-1 (Caine et al., 1996). The fault
zone can either impede or enhance fluid flow (Caine et al., 1996). Fluid flow across fault
zones is a function of porosity and permeability which in turn are dependent upon the
lithology and fault movement in a given fault zone (Chester and Logan, 1986, Caine et
al., 1996). Previous studies conducted regarding fault zones and fluid flow by Caine et
al., 1996, Goodwin et al., 1999, Faulkner et al., 2010, have indicated that a contrast exists
between the permeability field of a fault zone and the protolith rock (Caine, et al., 1996,
Goodwin et al., 1999, Faulkner et al., 2010). The contrast exists when the protolith rock
is relatively more permeable than the fault zone; thus, resulting in a barrier-forming fault
with respect to fluid flow. The contrast in the permeability field is also present when the
fault zone is relatively more permeable than the surrounding protolith rock; thus,
resulting in a conduit-forming fault with respect to fluid flow. However, no correlations
have been made indicating what causes the permeability variations that can cause some
faults to behave as barriers or conduit to fluid flow.
Purpose of Study
Groundwater resources are critical to civilization, especially in desert regions, like
the northwestern portion of Coachella Valley, California. Nearly 200,000 people, in the
cities of Desert Hot Springs, Palm Springs, Cathedral City, Rancho Mirage, Thousand
Palms, and Palm Desert, rely on groundwater resources and artificial groundwater
recharge activities implemented in the region (MWH, 2013). Therefore, proper
management of groundwater resources in this region is essential.
The purpose of this study is to estimate porosity and permeability variations
across fault zone architecture present along fault outcrops associated with the Mission
2
Creek, Banning, and Garnet Hill Strands using grain size distributions. Characterizing the
porosity and permeability changes within the different zones of the fault architecture is
important in order to evaluate a fault ability to behave as a barrier or conduit to fluid
flow.
Figure 1-1: The figure above displays a schematic diagram of the fault zone architecture.
The fault zone architecture is characterized as a centralized fault core which is
surrounded with a damage zone. The protolith zone is regarded as the region that is not
affected by faulting. (Revised figure from Caine et al., 1996)
Location of Study Area
The study region is located in the northwestern corner of Coachella Valley, California
(Riverside County). In this region, the San Andreas Fault zone splays into three primary
fault strands, (as displayed in Figure 1-2a): Mission Creek Strand, Banning Strand, and
3
Garnet Hill Strand. The fault strands partition the groundwater aquifer into three
distinctive basins: Desert Hot Springs subbasin, Mission Creek subbasin, and Indio
subbasin (which contains the Garnet Hill subarea and Whitewater subarea), as shown in
Figure 1-3.
Some regions along the fault strands display vegetation alignment which indicates
shallow groundwater resources. However, the surface feature is not traceable along the
entire length of the fault strand, thus, not providing any information regarding the fault’s
ability to act as a barrier or conduit along strike. Vegetation alignments in the region can
result from:
(1) Vertical fluid migration towards the surface due to a fault strand’s ability to create
a barrier to fluid flow, as displayed in Figure 1-4.
or
(2) The presence of a perched aquifer resulting from a fractoconformity, as displayed
in Figure 1-2b.
Variations across the water table are also noted in groundwater well measurements.
Historical records of groundwater table measurements are inputted into subsurface
modeling programs (i.e. MODFlow) and overseeing water agencies in the region
continue to record water-depth measurements from monitoring wells in the region.
Reverse modeling techniques indicate offsets in the groundwater table across the fault
strands existed in 1936 (Figure 1-5, MWH, 2013). After nearly 100 years of groundwater
extraction and implantation of artificial recharge activities, offsets in the groundwater
table were still present in 2009 (Figure 1-6, MWH, 2013).
4
Figure 1-2 (A and B):
A- Study region outlined in black with the three main strands in the San Andreas Fault
zone.
B- Interpretative schematic diagram with a series of perched aquifers which are not
hydraulically connected. The perched aquifers are a result of a fractoconformity. A
fractoconformity is defined as the relation between conformable strata where faulting
of older beds proceeds contemporaneously with deposition of newer strata, also
referred to as syn-tectonic deposition.
5
N
Figure 1-3: Groundwater subbasins and subareas within the study region. Subbain and subarea boundaries are attributed to the
presence of faults offsetting the groundwater table. Artificial recharge ponds are located in the Indio and Mission Creek subbasins.
(Modified from MWH, 2013)
6
Figure 1-4: A schematic comparison of unfaulted and faulted water bearing units.
Diagram A displaying the unfaulted units and diagram B displaying a schematic where a
barrier-forming fault is causing fluid migration towards the surface, resulting in the
formation of an oasis at the surface. (Modified figure from Freeze and Cherry, 1979).
7
Figure 1-5: Groundwater contour surface in feet from 1936. Contour modeling is
constructed from historical records of water well measurements. (From MWH, 2013)
Figure 1-6: Groundwater contour surface in feet from 2009. Contour modeling is
constructed from measurements of water depth taken at monitoring wells. (From MWH,
2013)
8
2. Coachella Valley
2.1 Description of Area
The study area is located in the northwestern corner of the Coachella Valley, CA
(Figure 2-1). The Coachella Valley is a part of Riverside County in Southern California,
extending for nearly ~70 km (~43 miles) from the San Gorgonio Pass to the northern
shore of the Salton Sea. It is centered in the northern portion of the Salton Trough
bounded by the San Jacinto and the Santa Rosa Mountains along the southwest and the
Little San Bernardino Mountains along the northeast. The Coachella Valley is situated at
the northwestern extent of the Salton Trough which is characterized as a structurally
complex transition zone between the Eastern Pacific Rise tectonic spreading center and
the right-lateral San Andreas transform fault zone, as displayed in Figure 2-2 (Powell,
1993).
2.2 Climate
The climate of the northwestern region of Coachella Valley is classified as a
desert, with mild winters and hot summers. Temperatures in the region have varied from
~62F to ~128F in the past 50 years (ACS, 2014). Precipitation on the desert floor varies
from year to year, but a weather station in Palm Springs has recorded averages of
between 0.03 to 1.14 inches of precipitation per year for the past 50 years (ACS, 2014).
Precipitation generally occurs during the December to March months; however,
occasional summer month subtropical thunderstorms occur as well. The San Jacinto and
San Gorgonio mountains border the valley along the west, creating an effective
orographic barrier against coastal storms and reducing the direct precipitation to recharge
9
Legend
Study Location
Major Freeway
Lake
N
Figure 2-1: Study region (indicated by the red star at 33.920231,-116.508449) is located
northwest of the Salton Sea and east of Los Angeles. (Base Layer Credits: ESRI,
DeLorme, GEBCO, NOAA NGDC, and other Contributors)
10
Figure 2-2: Study region (indicated by the red star) with respect to the location of the
Salton Sea and the transition between the San Andreas Fault and the East Pacific Rise. A
detailed map of the study region is shown in Figure 1-2. (Modified figure from Powell,
1993).
11
the Valley’s groundwater basins. Precipitation usually either evaporates, or is consumed
by native vegetation, or percolates into the underlying sediments and eventually adds to
the subsurface fluid flow. Vegetation in the region is sparse and only xerophytic plants
have been identified, including America’s only native palm tree- Washingtonia Filifera
(Desert Fan Palms), which are found within the numerous palm tree oases along the San
Andreas Fault (Proctor, 1968, Nye, 1994, Guzman, 2010). The Washingtonia Filifera is a
relic species of palm trees that has been dated back to the Miocene and Pliocene eras
(Nye, 1994). Favorable climatic conditions of the Sonoran Desert (also known as the
Colorado Desert) have made it possible for this species to survive in regions with
groundwater springs and seeps (referred to as oases). The Desert Fan Palms support a
giant radial-root system comprised of hundreds to thousands of tiny rootlets that penetrate
to a maximum depth of nearly ~10 feet (Nye, 1994). Thus, the oases are a result of
shallow subsurface groundwater resources.
2.3 Water Importation
Water supply and management of hydrologic resources is essential to support
civilization in desert provinces like the northwestern region of Coachella Valley.
Groundwater resources for the cities of Palm Springs, Cathedral, Palm Desert, Desert Hot
Springs, and Thousand Palms are managed by three primary agencies: The Coachella
Valley Water District (CVWD), Desert Water Agency (DWA), and Mission Springs
Water District (MSWD) (MWH, 2013). A decline in groundwater levels due to over
pumping of the aquifers promoted the Desert Water Agency and the Coachella Valley
Water District to formulate agreements with the State of California to purchase water
from the State Water Project (SWP) in 1962 and 1963 (MWH, 2013). However, due to
12
the lack of pre-existing infrastructure to import water to the desired location, the two
agencies are held responsible for the construction of a pipeline system for the
transportation of water from the State Water Project Canal to the Valley. In order to avoid
an estimated cost of $150 Million in the 1970’s (current costs projected at ~ $ 1 Billion),
the Desert Water Agency, and Coachella Valley Water District reached an agreement
with the Metropolitan Water District of Southern California (MWD) which is valid until
January 2035 (MWH, 2013). The agreement was to divert Colorado River water from the
Metropolitan’s Colorado River Aqueduct to Coachella Valley in exchange for
DWA and CVWD’s water share from the State Water Project (MWH, 2013). Diversion
of water was initiated in 1973, when DWA and CVWD started to release Colorado River
water into the Whitewater River and implemented artificial recharge within the
Whitewater subarea (ED-CVWD, 2013b). The imported water percolates into the
Whitewater subarea through nearly 900 acres of percolation ponds located at Windy
Point – Whitewater Spreading Facility. However, continuous over-drafting of the aquifer
system has required CVWD and DWA to implement artificial recharge activities in the
Mission Creek subbasin as well (ED-CVWD, 2013a). In 2002, Colorado River water was
diverted to the Mission Creek Spreading Facility to percolate through 200 acres of
percolation ponds in efforts to recharge the Mission Creek subbasin (MWH, 2013).
Percolation pond locations and groundwater subbasin boundaries are displayed in Figure
1-3.
2.4 Geologic Structure
Extending for nearly 1,200 kilometers (740 miles), the San Andreas Fault is a
N35-40W trending right-lateral fault, representing the main tectonic boundary between
13
the Pacific and North American plates (i.e. Wallace, 1990). In Southern California, the
San Andreas Fault is structurally influenced by a structural knot, often referred to as the
San Gorgonio Pass Knot. This is a result of the convergence of Transverse Ranges (San
Gabriel Mountains Basement), Peninsular Ranges (San Jacinto Mountains and Santa
Rosa Mountains) and the San Bernardino-Mojave Desert block, as displayed in Figure 23 (Langenheim et al., 2005). This convergence adds a great deal of complexity to the San
Andreas Fault zone (Langenheim et al., 2005). Because of this convergence zone, the San
Andreas Fault splays into a series of strike-slip faults with a dip-slip component creating
a non-uniform fault zone (i.e. Proctor, 1968, Wallace, 1990, Yule and Sieh, 2003).
The structural formation of the upper Coachella Valley is influenced by the inland
shift of the Pacific Plate- North American Plate boundary. This process is described as a
two-stage evolution process of the plate boundary that initiated in the late Miocene (9-7
Ma) and continues to the present (Matti and Morton, 1993, Yule, personal
communication). During this time, the Salton Trough region experienced oblique
extension in the Late Tertiary (6-2 Ma) and oblique convergence in the Quaternary (2.6
Ma-present) (Matti and Morton, 1993, Yule, personal communication). Because of the
relative uplift on the valley margins, the Salton Trough is a structurally depressed region
with the ability to accumulate sediment. Gravitational and seismic data collected in the
region reveals an asymmetrical basin with the deepest portion southwest of the Banning
Strand (CDWR, 1964, Catchings et al., 2009); therefore, creating the appearance of a
series of stair-stepping half-graben basins between the Little San Bernardino Mountains
and the San Jacinto Mountains (Figure 1-2b).
14
Figure 2-3: Various geomorphic regions located in California with the San Andreas Fault
(SAF) zone in red. The “Big-Bend” in the San Andrea Fault trace is located at the
convergence of the Transverse Ranges, Peninsular Ranges, and the Mojave
Desert/Colorado Desert block. Complexity of the SAF is shown by the numerous splays
at the convergence zone. The study region is indicated by the location of the star, which
encompasses the numerous splays of the San Andreas Fault zone. (Base Layer Credits:
ESRI, DeLorme, GEBCO, NOAA NGDC, and other Contributors)
15
The nomenclature of categorizing fault sections, strands, and splays is adopted
from previous studies conducted in the region by CSU, Northridge students (Behr et al.
2010, Guzman, 2010). The portion of the San Andreas Fault that is present in the study
region is referred to as the Coachella Valley section, which is composed of various
strands related to numerous splays. The Coachella Valley section of the San Andreas
Fault is characterized by a westerly strike ranging from ~N50-80W, with much of the
fault slip distributed along three primary fault strands: San Andreas Fault-Mission Creek
Strand (SAF-MCS), San Andreas Fault- Banning Strand (SAF-BS), and San Andreas
Fault-Garnet Hill Strand (SAF-GHS). These faults are referenced as the Mission Creek
Strand, Banning Strand, and Garnet Hill Strand in this thesis (Figure 2-4).
At the surface, the Mission Creek, Banning, and Garnet Hill Strands display
several topographic features associated with fault movement (Figure 2-5). The Mission
Creek and Banning Strands form the northern and southern boundaries of the uplifted
Indio Hills, respectively. The Indio Hills is the largest unit of material that has been
displaced due to fault movement. The Indio Hills display characteristics similar to a
positive flower structure (also referred to as Palm-Tree structure) where the Mission
Creek, Banning, and Garnet Hill Strands merge into a single Coachella Valley section at
depth (Catchings et al., 2009). The Indio Hills are cut in an N-S direction by Thousand
Palms Canyon (Figure 2-5). The northern portion reaches a maximum elevation of 420
meters (1380 feet) and 530 meters (1740 feet) in the southern portion (CDWR, 1964).
The presence of the Garnet Hill Strand near the Indio Hills is unknown due to the
lack of surface exposure in the region. However, west of the Indio Hills, evidence for the
presence of a Garnet Hill Strand at depth is expressed at the surface through a series of
16
Mission Creek
Subbasin
Indio Subbasin
Whitewater
Subarea
Garnet Hill
Subarea
Desert Hot
Springs
Subbasin
Figure 2-4: The three main strands of the San Andreas Fault zone: Mission Creek Strand
(SAF-MCS), Banning Strand (SAF-BS), and Garnet Hill Strand (SAF-GHS) are
displayed in within the study region, which is outlined in black. The respected subbasins
and subareas formed by the faults are also indicated. (Base Layer Credits: ESRI,
DeLorme, GEBCO, NOAA NGDC, USGS and other Contributors)
17
Figure 2-5: Vegetation alignments present at the surface along the Banning and Mission
Creek Strands. No vegetation alignment was found along the Garnet Hill Strand.
Vegetation patches labeled a-g corresponding with Figures A-5a-g (found in Appendix
A). (Base Layer Credits: ESRI, DeLorme, GEBCO, NOAA NGDC, USGS and other
Contributors)
18
compressive steps between the Garnet Hill and the Banning Strands, as displayed in
Figure 2-6 (Yule and Sieh, 2003, 2009). These geomorphic expressions are seen on the
surface as active folds (West Whitewater Hill, East Whitewater Hill, Hugo Hill, Devers
Hill, Garnet Hill, and Edom Hill) suggesting oblique slip (dextral-reverse slip) with a left
step-over along the Garnet Hill Strand (Yule, 2009). The Garnet Hill Strand displays an
overall right-lateral movement with a thrust component in the San Gorgonio Pass region
and possible northeast dip (~40NE) (Yule and Sieh, 2003, Catchings et al., 2009). East
of Edom Hill, the presence of the Garnet Hill Strand is difficult to detect, however, it has
been reported that a major oil company conducted a gravitation survey of the region and
detected the fault as displayed in Figure 2-6 (Tyley, 1974). Seismic surveys conducted by
various agencies have not been able to detect the fault (CDWR, 1964). However, the fault
trace was not detected in the geophysical studies conducted by Shawn Biehler in 1964
(Tyley, 1974).
The San Andreas Fault- Banning Strand is a right lateral-reverse fault which
extends in a southeast direction from the San Bernardino Mountains and crosses the study
region at a strike of N70-65W (Proctor, 1968). Seismic surveys conducted along the
Banning Strand indicate dips ranging from 50-70 to the northeast along the Banning
and Mission Creek Strands (Catchings et al., 2009). Surface traces of the fault can be
identified in aerial photography by disruption of alluvial deposits, such as the Mission
Creek Upland Wash (Figure 2-7). The presence of vegetation alignments and springs
along the north side of the fault strand indicates regions of shallow groundwater
accumulation (Figure 2-5).
19
Figure 2-6: Shaded-relief topographic map displaying geomorphic surface expressions
between the Garnet Hill Strand and the Banning Strand. Approximate location of the
Garnet Hill Strand is based upon probable location detected by gravitation studies (Tyley,
1974). Abbreviations: W-WH, West-Whitewater Hill; E-WH, East-Whitewater Hill;
SAF- San Andreas Fault; -?-, Approximate location of fault;
, Approximate
drainage divide with direction of drainage. (Modified Figure from Yule, 2009)
The Mission Creek Strand crosses through the study region at a strike of nearly
N50W and is a right lateral reverse fault. Seismic studies conducted in the region have
indicated three separate splays associated with the Mission Creek Strand, each dipping at
a slightly different angle: Mission Creek splay- A (90NE), Mission Creek splay-B
(80SW), and Miracle Hill fault splay (80NE) ( Figure 2-8) (Catchings et al., 2009).
Movement along the Mission Creek Strand indicated both lateral and vertical
displacement. Geomorphic surface expressions associated with the Mission Creek Strand
20
Figure 2-7: Regions of excessive alluvial deposits within the study region. These regions are referred to as the Dillon Road Piedmont
Slope, Mission Creek Upland, and Palm Springs Sand Ridge. The Dillon Road Piedmont Slope and Mission Creek Upland deposits
are regions formed by repeated alluvial fan deposits. The Palm Springs Sand Ridge appears to be formed by sand dune deposition.
(Modified figure from CDWR, 1964)
21
Figure 2-8: Seismic survey interpretation suggesting three fault splays associated with
the Mission Creek Strand (MC splay a, b and the Miracle Hill Fault (sometimes referred
to as splay c). Seismic profile was conducted along Long Canyon Creek, located NW of
the intersection of Dillon Road and Mountain View Road and east of Sample Location
MF. The seismic data interpretation also supports the presence of a fractoconformity.
(Modified Figure from Catchings et al., 2009)
22
includes the north face of the Indio Hills, Miracle Hill and the Dillon Road Piedmont
Slope (Figure 2-7) (Proctor, 1968). Miracle Hill is an escarpment on the north side of the
Mission Creek Strand. Miracle Hill is a continuous scarp with a north-west orientation for
nearly 3.5 kilometers (~2.2 miles) and rises nearly 33 meters (110 feet) above the valley
floor. Continuous flows from streams in the Little San Bernardino Mountains have
formed a series of coalescing alluvial fans which appear to terminate at the Mission
Creek Strand and/or the foothills of the Indio Hills. These wash deposits are referred to as
the Dillon Road Piedmont Slope (Figure 2-7) (CDWR, 1964).
2.5 Stratigraphy
Surrounding and within the study region, the mountains and basement are
comprised of highly complex crystalline Precambrian-Cretaceous igneous and
metamorphic rocks. Thick deposits of Tertiary and Quaternary sediments of continental
and marine origin overlay the basement (CDWR, 1964). Formational names and mapped
locations have been adopted from Allen (1957) and Dibblee (1954), see Figure 2-9 for
stratigraphic column. Subsurface correlations and surface mapping was conducted by the
California Department of Water Resources (CDWR, 1964) in order to determine the
potable water supply in the region. Geologic formations are discussed with regard to its
ability to store groundwater according to the California Department of Water Resources.
2.5.1 Non-water Bearing Units
Units classified under the title of non-water bearing are units that contain little or
no water and display poor storage aquifer characteristics. This group of units mainly
refers to the Precambrian-Cretaceous crystalline basement rocks, and consolidated
Tertiary sediments.
23
Figure 2-9: Stratigraphic column of formations found in the northern Coachella Valley,
CA. Formations are categorized to display age and their ability to store groundwater.
(Modified Figure from Proctor, 1968).
The basement rock and the surrounding mountains (San Bernardino and Little San
Bernardino Mountains) are a complex assemblage of gneisses and schists, commonly
referred to as San Gorgonio-Igneous-Metamorphic Complex (SGIM Complex). The San
Gorgonio-Igneous-Metamorphic Complex is characterized as heterogeneous plutonic
rocks with undifferentiable schists and gneissic rocks. The SGIM Complex has many
common features resembling the Baldwin gneiss (found on the north side of the San
24
Bernardino Mountains), Chuckwalla Complex, Orocopia Schist, and San Gabriel
Formation Igneous-Metamorphic Complex (Allen, 1957). Three distinctive gneissic
lithofacies have been distinguished in the foothills of the study region: Flaser Gneiss,
Green Schist, and Piedmontite-Bearing gneiss (See Table 2-1 for further description)
(Allen, 1957).
Overlaying the crystalline rocks is nearly 1,900 meters (6,200 feet) of
consolidated tertiary sediments (Proctor, 1968). Two main units are projected throughout
the valley floor -Coachella fanglomerate and the Imperial Formation. The Split Mountain
Formation is believed to underlie the Coachella fanglomerate, however, there are no
surface exposures of the Split Mountain Formation in the upper Coachella Valley
(Proctor, 1968). The Coachella fanglomerate is a well-indurated, massive conglomerate
creating prominent cliff forming beds exposed in the Upper Whitewater Canyon (Allen,
1957, CDWR, 1964). The exposure on the eastside wall of the Whitewater Canyon is
nearly 1,400 meters thick (4,600 feet) and is late Miocene to early Pliocene in age (Allen,
1957). Along the eastern flank of the San Bernardino Mountains, numerous outcrops
have been noticed where the formation is interlayered with basalt flows. The upper and
lower members of the Coachella fanglomerate are separated by a pale-red-purple basalt
flow, which can be found at about 560 meters (850 feet) above its base near the mouth of
Mission Creek, (Figure 2-7, Allen, 1957). The number of olivine basalt flows and
thickness increases towards the Mission Creek Strand and the basalt appears to have
extruded along the fault splays (Allen, 1957, Slade, 1981). The maximum cumulative
thickness of these flows is about ~15 meters (50 feet) but wedges out towards the
southeast and north (Allen, 1957).
25
Table 2-1: Lithofacies Associated with the San Gorgonio-Igneous-Metamorphic Complex
Lithofacies
Name
Location
G1
Flaser Gneiss
North of the
Banning Fault
-Displays distinctive structures developed by cataclastic metamorphism
-Ranges from slightly sheared augen gneisses to mylonitzed and lenticularly
layered rocks with grading and foliation generally striking east.
Green Schist
North of the
Banning Fault
-Localized body of quartz-actinolite-albite-epidote schist.
- In hand sample- greenschist is dark blue green with well-developed foliation
and contains many porphyroblasts of white albite that gives a “knotty”
appearance.
-Similar to the Pelona Schist of the San Gabriel Mountains and Orocopia
Schist. Three possible explanations for the similarities are:
(1) the greenschist represents the parent rock of the migmatitic
gneisses and have been upgraded (2) greenschist represents a locally
degraded amphilbolite. The environmental setting is of high shear
stress and hydrothermal activity.
(3) This region is a large fault sliver that was left behind/midway when
the Pelona Schist and Orocopia Schist were separated.
PiedmontiteBearing Gneiss
North of the
Banning Fault
and
West wall of
Whitewater
Canyon
-Piedmontite is a dark-reddish or reddish brown, manganese-bearing mineral
of the epidote group. This gneiss facies has been found to contribute fragments
to the sedimentary rocks south of the Banning Fault.
-The piedmontite-bearing gneiss is associated with pegmatite intrusions in the
older occurrence of this gneiss which is on the west wall of Whitewater
Canyon by Red Dome and the Trout farm. This occurrence does not display
the yellow-green epidote and light-pink mica (alurgite).
G2
G3
Description
26
The Imperial Formation outcrops throughout the valley floor and represents the
last and only known marine southeasterly incursion dating back to about 6.5-6.3 Ma
(Dorsey, 2011). The Imperial formation is characterized by various deep yellowish to
brown sandstone, siltstone, and shale beds with thicknesses ranging from 0.15 meters- 30
meters (6 in-100 ft), however, the formation has an overall combined thickness of 480
meters (1,600 feet) (Allen, 1957, Proctor, 1968). Locally abundant fossils and wide
variation in grain sizes indicate that the Imperial formation was deposited in littoral or
shallow marine environments (Proctor, 1968). Some units consist of fossiliferous
sandstone, silty sandstone, and claystones with bentonites (Nye, 1994). Variations in the
lithofacies suggest that the northwestern region of Coachella Valley was predominantly a
rocky shoreline with moderate relief and deep marine environments towards the
southeast.
2.5.2 Semi-Water Bearing Units
The category of semi-water bearing units describes units with generally low
water-yielding capability. Formations spanning from the Pliocene and early Pleistocene,
are categorized as semi-water bearing units (CDWR, 1968). In these formations, water is
present in the interstices of rocks, however; due to the highly contorted nature of these
formations, it is not readily extracted (CDWR, 1968).
Along the western rim of the valley, the Painted Hill Formation conformably
overlies the Imperial Formation at Whitewater Canyon. The Painted Hill Formation
consists of 1,036 meters (3,400 feet) of pale-brown to light grey conglomerate sandstone
with sub-rounded to well-rounded clasts <.5 meters (up to 2 feet in diameter) (Allen,
1957). The semi-consolidated and poorly sorted nature of this formation is indicative of
27
low permeability.
The uplifted Indio Hills region exposes thick, compacted beds of the Palm
Springs Formation. The Palm Springs Formation is presumed to underlie the Coachella
Valley at depth. Previous studies conducted in the region date the Palm Springs
Formation as lower Pleistocene in age (Nye, 1994). In the eastern Indio Hills, the Palm
Springs Formation lies unconformably beneath the Ocotillo Formation (CDWR, 1964). In
this portion of the Indio Hills, the Palm Springs Formation is more than 600 meters
(2,000 feet) in thickness with grey to tan arkosic sandstones interbedded with red and
green siltstones and claystones (CDWR, 1964). The Canebrake conglomerate, a member
of the Palm Springs Formation, is a compacted silty conglomerate and sandstone. The
Canebrake conglomerate is believed to be more permeable than other lithofacies
associated with the Palm Springs Formation (CDWR, 1964). There is no direct evidence
indicating that the Painted Hill Formation, found on the western rim of the valley and
identified by Allen (1957), are the same rocks as the Palm Springs Formation which
outcrops on the eastern side of the study region and was identified by Dibblee (1954).
However, a chrono-stratigraphic correlation may exist between the two formations and
deposition may have occurred simultaneously with the regression of the last marine
incursion.
2.5.3 Water-Bearing Units
The principal groundwater aquifer system lies within undisturbed and
unconsolidated recent and Pleistocene alluvial deposits. The study region receives detrital
material from the surrounding mountain ranges, which is transported via streams and
deposited as heterogeneous alluvial fans. Units classified as water-bearing units are the
28
Ocotillo conglomerate, Cabezon fanglomerate, and Quaternary alluvium and terrace
deposits.
The Ocotillo conglomerate is the primary water-bearing unit in the upper
Coachella Valley. Exposures of the Ocotillo conglomerate in the Indio Hills indicate that
the Ocotillo conglomerate unconformably overlies the Palm Springs Formation (CWDR,
1964). It consists of poorly consolidated sandstones and conglomerates interbedded with
thin grey-green and red-brown silts and clay like lenses (CWDR, 1964). This unit is at
least 730 meters (2,400 feet) thick with the top 30 meters to 60 meters (100 to 200 feet)
consisting mainly of lake-deposited sediments, as shown by resistivity breaks in electric
logs of water wells (CWDR, 1964). The formation is split into two members- upper and
lower- which are distinguished due to differences in lithology. The upper member
consists of a poorly sorted grayish conglomerate with discontinuous beds of pebbles
and/or boulders with varying thicknesses of a few inches to 1-2 feet (Nye, 1994). The
lower member consists of a series of intermixed beds of multi-colored sandstones,
conglomerates, and siltstones, as mapped by Popenoe in 1959 near Pushawalla Canyon
(Nye, 1994). The exposure is described as a sequence of yellowish buff to pale green,
massive, pebbly sandstone beds that range from 1 meter to 9 meters (3 feet to 30 feet) in
thickness and alternate with conglomerate beds of similar thickness (Nye, 1994).
Stratigraphically correlated to the Ocotillo conglomerate is the Cabezon
fanglomerate, which is only found at the base of the San Bernardino Mountains. The
Cabezon fanglomerate is a coarse, heterogeneous, and poorly consolidated fanglomerate
(CDWR, 1964). The Cabezon fanglomerate reaches a maximum thickness of 300 meters
(1,000 feet) and unconformably overlies the Painted Hill Formation (Allen, 1957). The
29
Cabezon fanglomerate is the primary water-bearing unit in the Mission Creek Upland
region (CDWR, 1964).
The shallow water-bearing zone in the region is contained Pleistocene- Holocene
alluvial fan and terrace deposits. These deposits have a maximum thickness of 120 meters
(400 feet), as exposed in the Indio Hills (CDWR, 1964). The alluvial fan and terrace
deposits found in the Indio Hills are correlated throughout the valley floor to the terrace
deposits found the Mission Creek Upland region; correlations are based on lithology
exposed at the surface and in water well logs (CDWR, 1964). These undifferentiated
terrace deposits consist of grey to tan heterogeneous gravels, sands, and silts (CDWR,
1964).
2.6 Groundwater Basins
In 1964, the California Department of Water Resources and United States
Geological Survey (USGS) recognized the water-bearing and semi-water bearing
formations in the Colorado Desert Region (CDR) as the Coachella Valley Groundwater
Basin (CVGB). In the study region, the Coachella Valley Groundwater Basin is divided
into appropriate groundwater subbasins and subareas. The boundaries between the
subbasins in this region are generally based upon faults which act as effective barriers to
lateral flow across the basin (CDWR, 1964). General location of the faults is determined
by alignment of geomorphic and phreatophytic vegetation at the surface, which is
apparent in aerial photography (Figure 2-5). This thesis examines groundwater flow in
four distinct regions, as classified by the CDWR (CDWR, 1964, DWR, 2003) (Table 22). A slight discrepancy in the groundwater subdivisions exists between the
classifications system assigned by the DWR and the USGS, where the USGS recognizes
30
Table 2-2: Groundwater basin classification name and assignment number from
Department of Water Resources (DWR, 2003).
Name
Coachella Valley Groundwater Basin
Desert Hot Springs Subbasin
Miracle Hill Subarea
Mission Creek Subbasin
Indio Subbasin
Garnet Hill Subarea
Whitewater River Subarea
DWR Basin No.
No.7-21
No.7-21.03
No.7-21.02
No.7-21.01
subareas as subbasins. The DWR recognizes 3 subbasins (Desert Hot Springs, Mission
Creek, and Indio) with the Indio subbasin subdivided into the Garnet Hill subarea and
Whitewater subarea; whereas, the USGS recognizes 4 subbasins: Desert Hot Springs,
Mission Creek, Garnet Hill, and Whitewater. However, location of the designated regions
is consistent between the two agencies.
2.6.1 Desert Hot Springs subbasin
The Desert Hot Springs subbasin covers a surface area of 101,000 acres (158
square miles) (DWR, 2003). Depth of the subbasin varies but seismic data displays at
least ~160 feet (50 meters) of saturated unconsolidated sediments in the Desert Hot
Springs subbasin region (Catchings et al. 2009). The Desert Hot Springs (DHS) subbasin
is an unconfined aquifer system bounded by the Little San Bernardino Mountains and the
Mission Creek Strand on the north and south, respectfully. The Little San Bernardino
Mountains are considered a no-flow boundary due to their crystalline composition. The
main sources of natural groundwater recharge are from the watersheds of Big Morongo
Canyon and Morongo Canyon (Figure 2-10). Minor flow paths are present at the base of
canyons where runoff from the mountains recharges the subbasin through tributary wash
deposits (CDRW, 1964). The Mission Creek Strand is also regarded as a no-flow
31
Figure 2-10: Displays the stream flow paths and directions with the numerous watersheds located in the region. The watershed
boundaries provide insight regarding the alluvial depositional patterns in the region. (Base Layer Credits: ESRI, DeLorme, GEBCO,
NOAA NGDC, and other Contributors)
32
boundary in computational groundwater models due offsets in the groundwater table
found in water wells on opposites sides of the fault strands. The general direction of
groundwater flow in the subbasin is towards the southeast (CDWR, 1964).
There are two primary water bearing geologic units in the subbasin. They are the
late Pleistocene and Holocene coarse-grained and poorly sorted alluvial fan deposits
associated with the Dillon Road Piedmont Slop and the Ocotillo Formation (DWR,
2003). The principal exposure of the Ocotillo Formation is at Miracle Hill (CDWR,
1964). Groundwater resources in the vicinity are extracted for their high thermal and
mineral properties and are used to supply local hot-spring resorts. This region is referred
to as the Miracle Hill subarea due to the unique thermal properties of the groundwater
near the Mission Creek Strand northwest of the Indio Hills. The Sky Valley subarea and
Fargo Canyon subarea are not discussed in this report because they are not affected by
the fault strands being studied.
The Desert Hot Springs subbasin is uniquely defined by its hydro-chemical
characteristics. The water quality in the subbasin is relatively poor due to high salinity
content ranging from 800 mg/L to 1000 mg/L (DWR, 2003). The groundwater contains
relatively high levels of fluoride concentration, near 10 mg/L (Slade, 1981). Groundwater
resources in the Miracle Hill subarea contain high amounts of sodium and sulfate ions
(DWR, 2003). Temperatures in the Miracle Hill subarea range from 82F to 200F
(CDWR, 1964, Proctor, 1968). The relatively high temperatures of the water and high
concentrated TDS levels is probably a result of emanating gases and hydrothermal
activity associated with the Mission Creek Strand. Chemical analysis conducted by the
USGS in 1974 concludes that the hydro-chemistry of the groundwater is dominated by
33
sodium-sulfate ions and sulfate levels are generally greater than 250 mg/L (Slade, 1981,
DWR, 2003). The high sodium-sulfate content and gypsum deposits are a likely result of
hydrothermal activity along the Mission Creek Strand. Gypsum deposits form a thin layer
where natural springs have formed; such as in the Thousand Palms region (see Figure
page 2-11).
Figure 2-11: Displays gypsum (CaSO4·2H2O) deposits resulting from flow of natural
springs enriched in sodium sulfate NaSO4. Sulfate content is likely resulting from
hydrothermal activity along the Mission Creek Strand.
2.6.2. Mission Creek Subbasin
The Mission Creek subbasin is bounded by two faults to the north and south,
Mission Creek Strand and Banning Strand, respectively. To the east and west, the
34
subbasin is bounded by the semi-impermeable sediments of the Indio Hills and the
impermeable rocks of the San Bernardino Mountains, respectively. The Mission Creek
subbasin covers a surface area of 76 square miles (49,000 acres). The depth of the
subbasin varies but sediment deposits are expected to be as deep as 2,000 meters (7,000
feet) (Slade, 1981). Geophysical studies conducted by Dr. Shawn Biehler of UC,
Riverside suggest that the Mission Creek subbasin contains five layers which can be
distinguished by seismic data, see Table 2-3 for layer distinctions (Slade, 1981).
However, only the upper 600 meters (2,000 feet) are considered potable water (DWR,
2003). Groundwater is not extracted from deeper depths due to poor water quality and
poor hydraulic communication between the unconsolidated and semi-consolidated
material.
Natural recharge of the subbasin occurs through subsurface flow in intermittent creeks
and rivers such as Mission Creek and Little and Big Morongo Washes of the San
Bernardino Mountains on the western boundary (see Figure 2-10 for watershed
boundaries). Repeated flow events from Mission Creek have deposited a series of alluvial
terrace deposits at the base of the San Bernardino Mountains, creating a shallow waterbearing zone known as the Mission Creek Upland (Figure 2-8) (DWR, 2003). The
primary water bearing units are the unconsolidated late Pleistocene/ Holocene alluvial
deposits and the Ocotillo conglomerate / Cabezon fanglomerate (CDRW, 1964). All
water-bearing units are composed of unconsolidated material creating an unconfined
aquifer system.
The hydro-geochemistry of the Mission Creek subbasin is characterized by
calcium-sodium-bicarbonate-chloride ions and groundwater temperature are ambient to
35
Table 2-3: Classification of layers present in the Mission Creek Subbasin distinguished by geophysical studies conducted by Dr.
Shawn Biehler in 1979 (Slade, 1981).
Layer
Depth
(ft)
1
-
Thickness
(ft)
(m)
155
47
Velocity
(ft/sec)
Density
(g/cc)
Stratigraphic Sequence
2480-3658
1.62
Unconsolidated, active stream
deposits and fanglomerate
2
155
564
171
5380-8434
2.16
Older alluviam, terrace deposits,
and saturated Ocotillo
conglomerate/ Cabazon
fanglomerate
3
719
1488
453
9556-10810
2.39
Ocotillo conglomerate, Cabazon
fanglomerate
4
2207
3806
1160
11075-12085
2.46
Palm Springs, Painted Hill, and
Imperial Formations, and
Coachella fanglomerate
5
6013
?
-
16000-16900
2.64
Igneous-metamorphic basement
rock
Depth to
Basement:
6013 feet (1832 meters)
36
surface temperatures, ranging from 73-83F (Slade, 1981, DWR, 2003). Concentration
levels of sulfates and TDS (<500 mg/L) are low while fluoride concentrations are
moderate (Slade, 1981). A small amount of subsurface flow on the northern boundary has
been detected along the Mission Creek Strand (Slade, 1981). Groundwater chemistry and
quality similar to Desert Hot Springs subbasin is detected in the Mission Creek subbasin
at this location. Leakage across the fault strand is noted in this region due to respectively
higher levels of fluoride levels (7-9 mg/L) (Slade, 1981).
2.6.3. Indio Subbain
The Indio subbasin is bounded on the north by the Banning Strand and the Indio
Hills while impermeable rocks of the San Jacinto and Santa Rosa mountains create the
southern boundary of the subbasin. The Indio subbasin extends from the San Gorgonio
Pass to the northern extent of the Salton Sea, however, only the region within the study
area is described here. In the study region, the Indio subbasin is divided into two
subareas: Garnet Hill subarea and Whitewater subarea. A few discrepancies exist in
previous studies conducted in the region, such as the reference to the Indio subbasin as
the Whitewater subbasin and the Garnet Hill subarea as a distinctive subbasin (CDRW,
1964, DWR, 2003, ED-CVWD, 2013a, ED-CVWD, 2013b, MWH, 2013).
In its entirety, the Indio subbasin covers a surface area of nearly 525 square miles
(DWR, 2003). The primary water bearing units in the subbasin are heterogeneous
Pleistocene and Holocene alluvial deposits, Pliocene alluvial deposits, and the Ocotillo
conglomerate. Depth of the basin is unknown in the region; however, it is assumed that
the water-bearing units are greater than ~600 meters (2,000 ft) in thickness (DWR, 2003).
This assumption is due to decreasing resistivity with increasing depth in geophysical well
37
logs and a gravitation survey revealing gravity anomalies indicating that the deepest
subbasin is located south of the Garnet Hill Strand (Proctor, 1968, Tyley, 1974, Reichard,
1992). However, only the upper ~300 meters (1,000 feet) of the aquifer are considered in
computational modeling of groundwater resources (Tyley, 1974). Natural recharge of the
subarea occurs from watersheds of the Little San Bernardino Mountains and natural
subsurface flow from the Whitewater River and runoff from the San Jacinto Mountains
(See Figure 2-10 for watershed boundaries).
The hydro-geochemistry in the subbasin has been documented prior to the
implementation of artificial recharge. The groundwater was dominated by calcium
bicarbonate ions and TDS concentrations were relatively low, ~300 mg/L (DWR, 2003).
Measurements were taken from the Palm Springs subarea because it dominates most of
the subbasin. Groundwater flow in the Palm Springs subarea is generally in a
southeasterly direction (Tyley, 1974). However, the gradient steepens at the base of
Edom Hill, suggesting a barrier to groundwater flow (Tyley, 1974).
The Garnet Hill subarea (also referred to as the Garnet Hill subbasin by CVWD
and USGS) is bounded on the northern side by the Banning Strand and the Garnet Hill
Strand on the south. The Garnet Hill subarea is regarded as an unconfined aquifer system.
The Garnet Hill subarea is hydraulically connected to the Whitewater subarea in the
upper 100 feet due to either the lack of faulting or the presence of a conduit fault zone
along the Garnet Hill Strand (DWR, 1964, MHW, 2013).
The Thermal subarea is a confined region within the Indio subbasin (ED-CVWD,
2013b). The Thermal subarea is comprised of interbedded sands, silts, and clays. This
region displays anisotropic permeability where permeability parallel to the bedding is
38
several times greater than the permeability normal to bedding (ED-CVWD, 2013b).
These anisotropic conditions suggest an aquitard or perched groundwater conditions.
Shallow fine-grained zones have created a series of perched water tables in the Thermal
subarea (ED-CVWD, 2013b).
Thousand Palms subarea is a small region that is distinguished from other
subareas due to its hydro-geochemistry. This region is characterized by sodium sulfate
(ED-CVWD, 2013b). There is a sharp hydro-geochemical boundary between the Thermal
subarea and the Thousand Palms subarea. An extension of the Garnet Hill Strand to the
east would coincide with this hydro-geochemical anomaly. However, gravity
measurements and residual gravity profiles do not suggest a subsurface fault in the
region; thus, variations in hydro-geochemistry are attributed to variations in permeability
within the strata (DWR, 1964).
2.7 Fault Zone Architecture
Fault zones are structurally and hydrogeologically complex with heterogeneous
zones that influence the geologic framework, as displayed in the San Andreas Fault zone
in the northwest Coachella Valley, CA. Such heterogeneities promote anisotropic flow
across fault zones influencing a variety of fluid-fault interactions.
A simple conceptual model of fault zone architecture has developed over the past
30 years. Architectural components of fault zones are defined as three distinctive regions:
main gouge zone (the fault core), damage host rock (damage zone), and undeformed
host-rock (protolith rock) (Chester and Logan, 1986, Caine et al., 1996, Faulkner et al.,
2010). This conceptual model involves a centralized fault core surrounded by a damage
zone, as displayed in Figure 1-1 (Chester and Logan, 1986). The fault core generally
39
consists of gouge, cataclasite, and/or ultracataclasite while the damage zone contains
subsidiary structures such as secondary faults, fractures, and folds (i.e. Faulkner et al.,
2010). The fault core is defined as the region which accommodates most of the
displacement where as the damage zone is mechanically related to the growth of the fault
zone (Caine et al., 1996).
Fault cores are not the same in every field study. Field based observations suggest
that fault cores can be comprised of unconsolidated clay-rich gouge zones, brecciated and
geochemically altered zones, or highly indurated cataclasite zones (Caine et al., 1996).
However, all fault zones play an important role in controlling fluid flow. Grain size
reduction and mineral precipitation along the fault plane generally yield lower porosity
and permeability than the primary water units. The reduction in permeability is believed
to be the main component that allows the fault core to act as a barrier to fluid flow (Caine
et al., 1996). The damage zone consists of a complex network of fractures, faults, and
folds. The subsidiary structures present in the damage zone cause anisotropic flow in the
damage zone (Caine et al., 1996). The secondary features present can create flow paths
for migration of fluids causing an increase in unidirectional permeability or increase
tortuosity in the flow path causing a decrease in permeability. The degree of complexity
associated with the development of a fault zone is indicative of the number of slip events
that have occurred along the fault plane, as an overprinting of damage would have to
occur along the fault plane and its associated damage zone.
Faults are dynamic systems in which mechanical, geochemical, and
hydrogeologic properties are dependent upon variations in lithology, temperature,
pressure, and deformation rate; thus, the effectiveness of a fault to impede or enhance
40
fluid flow will vary in respect to time and space (Goodwin et al., 1999). Such dynamic
relationships of the fault and fluid interaction system are illustrated in Figure 2-12. In
these relationships, it is considered that faulting along a fault plane results in a decrease
in fluid pressure (Goodwin et al., 1999). However, subsequent diagenesis and
mineralization in the fault zone gradually decreases permeability of the fault zone
resulting in an increase in fluid pressure. In return, the high fluid pressure decreases the
effectiveness of normal stress across the fault plane, thus, facilitating seismic events
(Goodwin et al., 1999).
Figure 2-12: Dynamic relationship between faults and fluid systems. (Goodwin et al.,
1999).
41
3. Methods
For the purpose of this thesis, methods used are divided into field methods and
laboratory methods. Laboratory analysis was conducted at CSU, Northridge and Core
Laboratories: The Reservoir Optimization Company (referred to as Core Labs) in
Bakersfield, California.
3.1 Field Methods
Field investigations were primarily carried out in the months of August and
September of 2013. Outcrop locations were selected due to the presence of offset in strata
as indicated by aerial photography on Google Earth, personal exploration by others
(Kimberly Blisniuk, Janice Gillespie, Richard Heermance, Alex Meyer), and analysis of
USGS B4 LiDAR imagery. Another factor in selecting sample sites was convenience of
access to sample sites. Samples sites had to be accessible either by vehicle or on foot. All
samples were collected from fault outcrops along public property. Whenever possible,
outcrops were cleaned to provide a relatively fresh surface in order to minimize the
weathering effect.
Samples were collected from three separate zones within the identified fault
outcrop location- fault core, damage zone, and protolith rock, where possible. The
protolith rock is regarded as a representative water-bearing unit of the aquifer system in
the region. At locations where a consolidated sample was not collectable, unconsolidated
sediment was collected for laboratory analysis. The bedding style and attributes (strike
and dip) were recorded as well. Specimen colors were identified using the Munsell-Rock
Color Chart as issued and standardized by the Geological Society of America. Thickness
of fault core and width of damage zone was measured and recorded for comparison
42
among the different sampling locations. Sampling locations were recorded using global
position system (GPS) device such as the Garmin etrex-Legend HCx.
3.1.2 Fault Zone architecture Characterized by Numerical Measurements
The fault zone architecture and a fault’s ability to act as a barrier and/or conduit to
fluid flow can be described by three numerical indices (Caine et al., 1996). The three
indices, Fa, Fm, and Fs, are derived from the conceptual model of fault zone architecture
(described in Figure 1-1) (Caine et al., 1996). Previous studies involving fault zone
architecture have observed a range of fault zone architectures and developed a correlation
between the fault zone architecture and permeability structure (Caine et al., 1996). F a is
the fault zone architectural index and values can range from 0 to 1. The Fa provides a
comparison of the damage zone width with the total fault zone width:
(EQ 3-1)
Fm= mean of Fa values for a single fault zone
(EQ 3-2)
(EQ3-3)
When Fa is equal to 0, the damage zone is ideally absent causing the theoretically
low permeability fault core to dominate the fault zone architecture and cause the fault
zone to act as a barrier to fluid flow (Caine et al., 1996). However, when Fa is equal to 1,
the fault core is ideally absent causing a theoretically high permeability damage zone to
dominate the fault zone architecture and cause the fault zone to act as a conduit to fluid
flow (Caine et al., 1996). Fm is an average of the Fa values obtained for a single fault
43
strand (Caine et al., 1996). Fm values incorporate Fa values measured at different
transects. Fs is a spatially variable index for a single fault strand where Fa values may
vary depending on the fault zone architecture (Caine et al., 1996).
3.2 Laboratory Methods
3.2.1 Core Laboratories: The Reservoir Optimization Company
Core Laboratories is a leading reservoir optimization company using technology
to conduct analysis of petrophysics and reservoir fluid behavior. Four samples (Samples
I1, MF7, P1, and P3) were analyzed at Core Labs in Bakersfield, California for porosity
and permeability. Not all samples could be analyzed at Core Labs due to size restrictions
and fragility of the samples. Hand specimen samples were cored using a standard Drilling
Press Diamond Tool with a 1-inch ID diamond bit (Core Laboratories, 2014). During the
core drilling process, samples were cooled using liquid nitrogen in order to prevent
samples from falling apart. Cored samples were placed in lead sleeves in order to keep
samples intact. Basic measurements like initial weight, texture, grain size, and
fluorescence were recorded before exerting 8000 psi of pressure to conform the lead
sleeves to the samples. Samples were placed in a humidifying oven for 68 hours and then
in a lab oven for 4 hours to dry. After the samples reached equilibrium with room
temperature, porosity and permeability was measured.
Porosity analysis was conducted using the Ultragrain Grain Volume- UGV200.
The core sample is placed in a grain volume cell and helium is isothermally transferred
from a reference cell to the grain volume cell, resulting in an equilibrium pressure
between the two containers (Core Laboratories, 2014). Through Boyles Law, the grain
volume is resolved, thus, providing a porosity measurement (Core Laboratories, 2014).
44
Volume and pressure measurements are recorded and analyzed with the UPore software.
Permeability analysis was conducted using the Ultra-Perm 500. The Ultra-Perm
500 is a steady state gas permeameter system applied to plug size core samples (Core
Laboratories, 2014). The software UPerm is able to record and plot unitized mercury
pressure drop against unitized flow rate. This plotting mechanism allows the software to
determine the airflow regime required for validation of Darcy’s Law. This methodology
allows measurements to be within 0.0001 mD (millidarcies) of error (Core Laboratories,
2014).
3.2.2 Thin Section Analysis
Optical properties of a rock are analyzed using a petrographic microscope. A thin
section analysis was conducted on eight samples (B2, B3, MF4, MF7, P1, P3, P4, and
P5). Thin sections were not constructed of all samples due to the fragility of the samples.
Epoxy injected thin sections were prepared by R.A. Petrographic located in Los Angeles,
California. Point counting analysis was conducted on each thin section utilizing the
Gazzi-Dickinson method (Folk, 1974, Ingersoll, 1984, Dickinson, 1985). Point counting
was conducted with a slide-advancing machine to advance the thin section. The point
counting analysis (500-points) was used to distinguish grains, matrix, and void spaces,
pore spaces, and epoxy veins. The region being recognized for each point count was
identified by the crossing of the ocular micrometer scale present in the ocular portion of
the petrographic microscope. Analysis of the thin section was conducted under a
magnification of 4x lens. Point counts were recorded in Microsoft-Excel, converted to
percentages, and used for grain vs. pore space comparisons. Point counting of thin
sections for porosity analysis was selected because by definition a thin section provides a
45
cross sectional view of a rock, thus, if the rock is injected with blue epoxy, the blue
epoxy should only reside within the pore space, as shown in Figure 3-1. Visual analysis
of thin sections was conducted to examine for fractures promoting fluid migration. Thin
sections were photographed using an Ernst Leitz GMBH Wetzlar Germany PRADO-500
thin section projector (CSUN ID # 19871), displayed in Figure 3-2.
Figure 3-1: Schematic diagram of a thin section where grains and pore space are
differentiated.
Figure 3-2: Photograph of the Ernst Leitz PRADO-500 thin section projector used to
photograph thin sections.
46
3.2.3 Porosity-Density Analysis
Porosity in rocks is defined as the measurement of the void space in a rock sample
(i.e. Davis and Dewiest, 1966, Fetter, 2001, Weight et al., 2001). Laboratory processes
can be employed to derive porosity measurements from samples. In order to determine
porosity of samples, the mass and volume of the sample must be calculated. Volume of
an irregular sample is calculated using Archimedes’ Principle. The volume of the original
sample solid is calculated as:
Volume of water displaced= Final Volume of water- Initial Volume of water
Volume of water displaced= Volume of solid
(EQ 3-4)
This equation assumes that no water enters the pore-space of the rock. For the volume
calculations, a known volume of de-ionized water (DI-water) was poured into a
graduated cylinder and the intact sample is placed into the graduated cylinder. The initial
volume of water can be altered to accommodate the sample sizes, but must always be
greater than the sample size. The amount of water displaced by the sample is equal to the
volume of the bulk sample, assuming water does not percolate into the pore-space of the
sample. This method is described in Figure 3-3.
Figure 3-3: Volume of an irregularly shaped object is measured by the water
displacement test where a known volume of water is poured into a graduated cylinder and
the object is placed to measure the displacement of water.
47
The mass of the sample can be derived using standard laboratory techniques (i.e.
Davis et al., 1966, Fetter, 2001, Weight et al., 2001, Kroetsch, et al., 2008). The wet
samples are oven dried in a sediment oven at 105C for 24 hours in aluminum foil
containers (Fetter, 2001). This expels any moisture in the samples that may be present on
the surface of the grains. Samples were dried using a Precision Economy Oven (CSUN
ID # 021649). The mass of the oven-dried sample is weighed using a calibrated digital
scale (i.e. electronic balance).
The density of the material is calculated by dividing the mass of the substance by
the volume:
(EQ 3-5)
Total porosity (n) of a sample can be computed from a porosity relationship (i.e.
Fetter, 2001, Weight et al., 2001, Kroetsch, et al., 2008):
(EQ 3-6)
wheren is the total porosity as a percentage
is the bulk density of the material (g/cm3)
is the particle density of the material (g/cm3).
A standard of 2.65 g/cm3 is used for the particle density. The particle density of quartz is
selected because from thin section analysis the grain composition is primarily quartz.
3.2.4 Grain Size Analysis
A standard dry grain size analysis was conducted using a mechanical sieve shaker
(Ro-Tap Testing Sieve Shaker Model B (CSUN ID #-007130/ 59936). Samples were
oven dried for 24 hours at 105C in the sediment oven in aluminum foil containers (i.e.
Fetter, 2001, Kalinski, 2011). Each sample was weighed before and after oven drying.
48
Some samples which remained consolidated were loosened with a pestle and mortar.
Standard set of sediment sieves were used ranging from 64 mm to 0.045 mm (Table 3-1)
Table 3-1: Sieves used in the grain size analysis. All sieves were provided by the
Department of Geological Sciences at CSUN and grain size analysis was conducted at
CSUN.
Sieve #
2 1/2
1 1/4
5/8
5/8
3 1/2
5
6
7
8
10
14
16
18
20
25
30
35
40
45
50
60
80
100
120
170
200
230
325
pan
Size (mm)
64.00
32.00
16.00
8.00
5.60
4.00
3.36
2.83
2.38
2.00
1.40
1.18
1.00
0.85
0.71
0.595
0.500
0.420
0.355
0.300
0.250
0.180
0.149
0.125
0.090
0.074
0.0625
0.045
0.000
Size()
-6.00
-5.00
-4.00
-3.00
-2.50
-2.00
-1.75
-1.50
-1.25
-1.00
-0.50
0.00
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.50
2.75
3.00
3.50
3.75
4.00
4.50
0.00
49
Mass of Sieve (grams)
544.09
561.25
565.9
546.37
543.37
695.11
674.99
627.48
629.29
621.09
418.52
453.96
401.86
428.56
367.65
566.09
365.93
513.57
335.72
385.07
318.99
307.04
461.77
307.22
307.48
449.32
302.07
365.62
496.03
(Kalinski, 2011). All sieves and the bottom pan were weighed beforehand and cleaned
after each sieve shake. The mechanical sieve shaker was operated in 15-minute intervals
(Kalinski, 2011). For samples that required longer sieving times, the shaker was operated
for an additional 10-minute interval. After the sample was thoroughly sieved, the sieves
and samples were weighed. Graphical relationships between the particle diameter size
and the percent finer than are used to determine the effective grain size (de) (Fetter, 2001,
Kalinski, 2011).
3.2.5 Hydraulic Conductivity Analysis
Fluid flow can be enhanced or retarded by the properties of the material in which
the fluid is traveling through. The ability of a rock to transmit fluid can be described in
terms of hydraulic conductivity and permeability. Permeability is defined as the
measurement of the ability of porous material to allow fluid flow through media,
whereas, hydraulic conductivity is defined as the measurement of the ease with which
water flows through porous material (i.e. Davis et al., 1966, Fetter, 2001, Weight et al.,
2001). One of the factors that affects hydraulic conductivity and intrinsic permeability is
sorting of grains. From grain-size distributions and previously derived relationships by
others, it is possible to relate grain size distribution, intrinsic permeability, and hydraulic
conductivity.
Empirical methods can be employed to determine the hydraulic conductivity
where field methods cannot be implemented. Calculations of hydraulic conductivity of
porous media can be derived from grain size analysis (Freeze and Cherry, 1979, Fetter,
2001, Cheng and Chen, 2007). A general relationship between grain size distributions
and empirical methods can be summarized as:
50
where:
(EQ 3-7)
K= Hydraulic conductivity (L/T)
g= Acceleration of gravity (L/T2)
v= Kinematic coefficient of viscosity (L2/T)
C= Dimensionless coefficient
(n)= Function of porosity
De = Effective grain size
There are many variations of calculations for hydraulic conductivity found in
literature. Three separate empirical equations are selected to calculate hydraulic
conductivity on basis of the parameters required for the calculation. The Beyer
relationship is independent of porosity where as the Slichter relationship incorporates the
porosity value of the media.
The uniformity coefficient (Cu) provides a correlation of measurement of the
degree of how well or poorly sorted the sedimentary sample. The uniformity coefficient
can be derived from grain size analysis and is equal to the ratio of the grain size that is
60% finer by weight (D60) to the grain size that is 10% finer by weight (D10) (i.e. Fetter,
2001):
(EQ 3-8)
The uniformity coefficient quantifies the degree of grain sorting within the sample. A
sample with a Cu less than 4 is considered to be well sorted where as a sample with a C u
of more than 6 is poorly sorted (i.e. Fetter, 2001).
The Breyer equation is considered most useful for material that can be
characterized with heterogeneous distributions and poor sorting. This equation is only
valid for samples with a uniformity coefficient between 1- 20 and an effective grain size
51
between 0.06mm and 0.6mm. The Breyer equation does not factor in porosity but instead
assumes a value of 1 for the porosity function (Cheng and Chen, 2007).
The Breyer equation was used is (Pliakas, 2011):
(EQ 3-9)
whereK=Hydraulic Conductivity (m/s)
g= Gravitational Constant (9.81 m/s2)
v= Kinematic Viscosity (m2/s)
CU= Uniformity Coefficient (dimensionless)
D10= Effective grain size corresponding to the 10 % grain size (mm) curve
The Slichter formula is considered applicable for effective grain sizes between
0.01 mm and 5 mm. The Slichter formula incorporates a porosity value of each sample
and the effective grain size of the tenth percentile.
The Slichter equation used is (Pliakas, 2011):
(EQ 3-10)
whereK=Hydraulic Conductivity (m/s)
g= Gravitational Constant (9.81 m/s2)
v= Kinematic Viscosity (m2/s)
n= Porosity value (fraction)
D10= Effective grain size corresponding to the 10 % grain size (mm) curve
3.2.6 Permeability Analysis
Permeability measurements are dependent upon properties of the porous media.
Intrinsic permeability (k) is dependent upon the characteristics of the sediment material,
such as the properties associated with clays and sandstones. The relationship between
hydraulic conductivity (K) and intrinsic permeability (k) is:
52
(EQ 3-11)
where:
K= Hydraulic Conductivity (m/s)
k= Intrinsic permeability (m2)
= Density of the fluid (water density) (1000 kg/m3)
= Dynamic viscosity of the fluid (kg m /s)
g= Acceleration due to gravity (9.81 m/s2)
53
4. Results
The results of this project are presented with respect to the method used for analysis and
by field location. Data collected is included in the appendices of this thesis and
referenced in the text.
4.1. Field Observations
Fault outcrop locations associated with the Mission Creek Strand, Banning
Strand, and Garnet Hill Strand were selected due to the recommendation of other
scientists who are currently or have in past conducted field studies in the region. Analysis
of Google Earth aerial photography, topographic maps, and B4 LiDAR imagery was used
to select sampling locations which would display fault outcrops. This analysis assisted in
selecting accessible fault outcrop locations. Surface fault expressions associated with
Garnet Hill Strand could not be identified within the study region; thus, no samples were
collected for this strand. Fault outcrop and sampling locations are displayed in Figure 41. Two fault surface expressions were selected for the Banning Strand: (1) Whitewater
Canyon and (2) a river cut near Via Las Palms road, Figures 4-2 and 4-3. Two fault
outcrop locations were selected for the Mission Creek Strand: (1) Mt. View Road Cut and
(2) Pushawalla Canyon, Figures 4-4 and 4-5. Two samples were collected from the Indio
Hills to serve as samples of water bearing units. Field notes and detailed descriptions of
field expeditions are provided in Appendix B and sample photographs are available in
Appendix C.
The Banning Strand crosses perpendicular to Whitewater Canyon and juxtaposes
the San Gorgonio-Igneous-Metamorphic Complex with the Cabezon Fanglomerate (Yule
54
Figure 4-1: Locations of collected samples in the study region. Sampling locations a are
indicated by a colored star on this map: Green Star: Location B- Whitewater Canyon
(Banning Strand), Blue Star: Location MF- Mt. View Road Cut (Mission Creek Strand),
White Star: Location I- Indio Hills, Red Star: Location RCB- River Cut near Las Palms
Road (Banning Strand), and Orange Star: Location P- Pushawalla Canyon (Mission
Creek Strand). (Base Layer Credits: ESRI, DeLorme, GEBCO, NOAA NGDC,USGS, and
other Contributors)
55
Figure 4-2: B4 LiDAR data analysis of fault outcrops along the Banning Strand at Whitewater Canyon. Fault outcrop displays fault
movement which places the San Gorgonio-Igneous-Metamorphic Complex against Cabezon Fanglomerate. Sampling location is
indicated by the yellow star. (Source: OpenTopography Facility, 2014)
56
Figure 4-3: B4 LiDAR data analysis of fault outcrop along the Banning Strand at a river
cut located near Las Palms Road. The Banning Strand is mapped as a single strand
running along the southern edge of the Indio Hills. Sampling location is indicated by the
yellow star. (Source: OpenTopography Facility, 2014)
57
Figure 4-4: B4 LiDAR data analysis of fault outcrop along the Mission Creek Strand. Fault outcrop displays recent movement of
water bearing units caused by faulting at Miracle Hill. Sampling location is indicated by the yellow star. (Source: OpenTopography
Facility, 2014)
58
Figure 4-5: B4 LiDAR data analysis of fault outcrop along the Mission Creek Strand at Pushawalla Canyon. The river carved canyon
provides a cross-sectional view of the fault splays associated with the Mission Creek Strand. Sampling location is indicated by the
yellow star. (Source: OpenTopography Facility, 2014)
59
and Sieh, 2003). At this location, the Banning Strand is characterized as a right lateral
fault with a dip of 45 NW, as displayed in Figure 4-6 (Yule and Sieh, 2003).
Four samples were collected from this location (B1, B2, B3, and B4) at the
Whitewater Canyon site (Location B). Detailed field descriptions are available in Table
B-1 (see Appendix B). A 13-meter wide fault zone is identified at this location with welldeveloped localized fault gouge, damage zone, and protolith rocks, as displayed in Figure
4-7. Sample B1 is a representative sample of the protolith rock north of the fault plane
(hanging wall) and is collected from a range of 0-3.7 meters of the identified fault zone.
Sample B1 is characterized as a weak and highly fractured metamorphic rock. Sample B2
is collected from the damage zone associated with the hanging wall at meter marking 8.28.7 meters. Sample B2 is considered a part of the deformed interior damage zone and is
banded with a calcareous residue, which range in widths of 2 mm-16 mm. Sample B3 is a
representative sample of the localized fault gouge, collected at meter marking 8.7-8.8
meters. The sample is clay-rich in appearance and texture with clay like layering. The
interior damage zone associated with the footwall of the fault (located south of the fault)
is moderately unconsolidated with cemented angular clasts within the matrix. Sample B4
was collected from the exterior damage zone associated with footwall. Sample B4 is an
unconsolidated conglomerate with cobbles ranging in size from 0.5mm to 12 inches. The
sample is classified as unconsolidated due the hydrogeologic definition which
differentiates consolidated sediments as materials that have been metamorphosed or
cemented together such as limestone or sandstone and unconsolidated sediments as
sediments ranging from clay to gravel size with pore space connectivity which allows
groundwater storage (USGS, 2013). Sample B4 is identified as the Cabezon
60
Figure 4-6: Banning Strand juxtaposing the SGIM Complex against the Cabezon Fanglomerate at Whitewater Canyon
61
Figure 4-7: Identification of localized fault gouge and damage zone associated with the Banning Strand at Whitewater Canyon
62
Fanglomerate, which is also identified as a water-bearing unit in the region
An outcrop of a splay associated with the Banning Strand is noted in the Indio
Hills near a river cut terrace east of Via Las Palms road. This location is referred to as
sampling location: River Cut- Banning Strand (RCB). One sample was collected from
this location (RCB 1). An 8.5-meter wide fault zone was identified with a localized fault
gouge developed at the 3.7-meter marker, see Figure 4-8. The damage zone is composed
of consolidated, very fine-grained sands and silts with popcorn weathering. The fault
gouge is weakly developed with a thickness of 5-10 cm. The fault plane appears to be
associated with a low angle thrust fault with a strike and dip of 250, 26NW. A
representative sample of the fault gouge was collected (sample RCB1).
A single strand outcrop of the Mission Creek Strand is not detected in the study
region. However, splays associated with the Mission Creek Strand are noted along the
northern boundary of the Indio Hills and Miracle Hill. In the city of Desert Hot Springs,
California, splays of the Mission Creek Strand can be seen along a road cut of Mt. View
Road. This location is referred to as sampling location: Mt. View Road Cut- Mission
Creek Strand. A detailed description of field data is available in Table B-2 (see Appendix
B). At this location, a 136.6-meter wide fault zone was identified on the hanging wall of
the fault, as shown in Figure 4-9. No localized fault gouge was identified at this location;
however, eight fault splays were identified. Six samples were collected, 5 samples (MF1,
MF2, MF4, MF6, and MF7 are of consolidated precipitate filled damage fault cores, and
1 sample (MF 9) is of the loose matrix sediments.
Splays of the Mission Creek Strand are noted at Pushawalla Canyon. At this
location, the river carved canyon provides a cross-sectional view of the fault splays
63
Figure 4-8: Fault zone architecture associated with the Banning Strand at river cut near Via Las Palms road. A 8.5 meter wide fault
zone was identified with localized fault gouge, ~5-10 centimeters wide.
64
Figure 4-9: Fault zone architecture associated with the Mission Creek Strand at Mt. View Rd. A 136.6-meter wide fault zone was
identified as displayed in Figure 4-9a. Localized gouge was indentified is shown in Figure 4-9b and c.
65
associated with the Mission Creek Strand. This location is referred to as Pushawalla
Canyon- Mission Creek Strand. A 43-meter wide fault zone was identified on the west
sidewall of the canyon and localized fault gouge was identified on the east sidewall, as
shown in Figure 4-10. A detailed description of field data is available in Table B-3 (see
Appendix B). Five samples (P1-5) were collected from this location. Samples P1 and P2
are collected from the damage zone in the hanging wall from blue-grey siltstones and
sandstones of terrace deposits or upper member of the Ocotillo Formation. Sample P3 is
collected from a siltstone bed that is affected by fault-drag folding. Sample P4 is
collected from the east sidewall. Sample P4 is from a 10 cm thick localized fault gouge
which displays clay like layering. The material present in the damage zone associated
with Sample P4 is of highly fractured sandstone. Sample P5 is collected from the
protolith rock of the footwall associated with the fault zone. This sample does not appear
to be effected by the faulting and is a representative sample of the water-bearing units in
the region.
The water-bearing units in the valley are composed of poorly sorted
conglomerates, sandstones, and siltstones of continental origin. Surface deposits in the
Indio Hills indicate deposits of lakebeds, fanglomerates, alluvium, and wind-blown
sands. The description of these deposits is similar to rock descriptions found in water
well and geothermal well logs located in the study region. Thus, samples collected from
the Indio Hills are considered representative samples of water bearing units found
throughout the valley. Samples I1 and I2 were collected along the north face of the Indio
Hills. Detailed description of the samples is provided in Table B-5 (see Appendix B).
A numerical evaluation of the Mission Creek and Banning Strands to determine if
66
Figure 4-10: Fault zone architecture associated with the Mission Creek Strand at Pushawalla Canyon. A 43-meter wide fault zone was
identified as displayed in Figure 4-10a (western side). Localized gouge was indentified is shown in Figure 4-1-b (eastern side).
67
the strands will behave as a barrier or conduit to fluid flow was conducted according to
the conceptual scheme for fault-related fluid flow according to numerical measurements
of fault zone architecture and permeability structures developed by Caine and others
(Caine et al., 1996). The fault zone indices derived from conceptual modeling of fault
zone architecture of the Banning Strand and Mission Creek Strand are presented in Table
4-1 and displayed in Figures 4-11 and 4-12, respectively.
4.2. Results from Core Labs
Four samples were processed at Core Labs in Bakersfield, California. Data
collected from Core Labs and photographs of cores are presented in Appendix D.
Analysis of Sample I1 provides a grain density of 2.70g/cc, porosity of 37.4%, and air
permeability of 314.964 md. Analysis of Sample MF7 provides a grain density of 2.60
g/cc, porosity of 48.9%, and air permeability of 47.52 md. Analysis of Sample P1
provides a grain density of 2.68 g/cc, porosity of 26.8%, and air permeability of 8.462
md. Analysis of Sample P3 provides a grain density of 2.68 g/cc, porosity of 46.1% and
air permeability of 23.03 md.
4.3 Thin Section Analysis
Thin section analysis was conducted on Samples B2, B3, MF4, MF7, P1, P3, P4,
and P5. Analysis of thin-sections included conducting a point count to indentify pore
space and visual examination of the thin section. Photographs of thin sections under
1.25X magnification are available in Appendix E. Data collected from point count
analysis by petrographic microscope is available in Appendix F and data collected from
JMicroVision Software is available in Appendix G.
68
Table 4-1: Calculation of fault zone indices from Equations 3-1, 3-2, and 3-3 in accordance to Caine et al (1996).
Fault Zone Styles
According to Caine et al. (1996)
Fault Zone Location
Whitewater Canyon
(B1)
Banning
Strand
Mission Creek
Strand
Fault Zone Width
Damage Zone Width
(meters)
(meters)
13
8.3
Fa
8.5
8.4
0.99
Mt. View Road Cut
(MF)
136.6
135.78
0.99
Pushawalla Canyon (P)
43
69
Fs
0.64
River Cut near Via Las
Palms Rd (RCB)
42.9
Fm
(Mean of Fa)
0.99
0.81
0.35
0.99
0.004
Figure 4-11: Fault zone architectural indices for the Banning Strand. Fault zone indices
fall between the barrier and conduit index, thus, indicating that the Banning Strand
behaves as a barrier-conduit.
Figure 4-12: Fault zone architectural indices for the Mission Creek Strand. Fault zone
indices fall between the barrier and conduit index, thus, indicating that the Mission Creek
Strand behaves as a conduit.
70
4.3.1 Thin Section Analysis Using Petrographic Microscope
Results of porosity derived from thin-sections using a petrographic microscope
listed in Figure 4-13.
Two thin sections (B2 and B3) were created from samples along the Banning
Strand at Whitewater Canyon. Sample B2 is from the damage zone associated with the
hanging wall and Sample B3 is from the fault gouge. Visual analysis of Sample B2 shows
the thin section contains many interconnected pathways which filled in with blue epoxy
(see Figure 4-14). Point count analysis of this thin section shows a porosity count of
20.49%. The remaining 79.51% of the thin section is composed of matrix that is too fine
to identify. It is presumed that the matrix is composed of silts and clay size particles.
Visual analysis of Sample B3 displays three primary epoxy filled pathways propagating
in a radial pattern from the epoxy injection point (see Figure 4-15). A number of air
bubbles are also present in nearly one quarter of the thin section area. Point Count
analysis of the thin section shows a porosity count of 39.04%. The remaining 60.96% is
composed of primarily of matrix with rounded grains.
Two thin sections (MF4 and MF7) were created from samples along the Mission
Creek Strand at Mt. View road cut. Both samples are from the calcareous- precipitate fill
found in the fault cores. Visual analysis of Sample MF4 displays angular grains of
various sizes suspended in the surrounding matrix (see Figure 4-16). The thin section
does not indicate any primary epoxy induced pathways. The point count analysis shows a
porosity count of 19.40 %. The remainder of the thin section (80.60 %) is composed of
large grains surrounded by matrix. Visual analysis of thin section MF7 shows a large
breakage in the sample and other secondary epoxy induced pathways (see Figure 4-17).
71
Figure 4-13: Porosity derived from point counting using a petrographic microscope.
72
Fracture
Matrix
Matrix
Grains
Grains
Fracture
Matrix
Fracture
Grains
Figure 4-14: Thin section of sample B2 in plain light (PPL). The thin section highlights some of the fractures developed in the thin
section and regions of matrix and grains.
73
Fracture
Grains
Grains
Fracture
Matrix
Matrix
Air Bubble
Figure 4-15: Thin section of Sample B3 in plain light (PPL). Some of the fractures developed, matrix, grains, and air bubbles are
indentified in the thin section.
74
Air Bubble
Fracture
Grains
Grains
Matrix
Grains
Grains
Matrix
Matrix
Matrix
Grains
Figure 4-16: Thin section of sample MF4 in plain light (PPL). Some of the fractures developed, matrix, grains, and air bubbles are
indentified in the thin section.
75
Matrix
Grains
Fracture
Grains
Grains
Matrix
Fracture
Matrix
Matrix
Matrix
Figure 4-17: Thin section of Sample MF7 in plain light (PPL). Some of the fractures developed, matrix, and grains are indentified in
the thin section.
76
The thin section contains numerous angular grains suspended in dense matrix. The point
count analysis shows a porosity count of 16.67 %. The remainder of the thin section
(83.33%) is composed of dense matrix with small grains.
Four thin sections (P1, P3, P4, and P5) were created from samples along the
Mission Creek Strand at Pushawalla Canyon. Visual analysis of thin section Sample P1
displays a high quantity of small angular grains with variations in the denseness of the
matrix (see Figure 4-18). Regions with high density of matrix also display low quantity
of grains and regions with low density of matrix display higher quantity of grains. Point
counting analysis shows a porosity count of 14.60%. The remaining 85.40% is composed
of angular grains surrounded by weak matrix and patches of dense matrix. Visual analysis
of thin section Sample P3 displays a high quantity of sparsely connected weak matrix
(see Figure 4-19). The thin section does not display a large quantity of whole grains.
Point counting analysis indicates a porosity count of 31.67%. The remainder of the thin
section (68.33%) is composed of sparsely interconnected-consolidated matrix. Visual
analysis of thin section Sample P4 displays numerous epoxy-induced pathways (see
Figure 4-20). The thin section displays a large quantity of angular grains suspended in
matrix. Point counting analysis of the thin section indicates porosity count of 14.80%.
The remainder of the thin section (85.20%) is composed of matrix and whole grains.
Visual analysis of thin section Sample P5 displays a high quantity of sub-rounded grains
with very little matrix development (see Figure 4-21). Point counting analysis of the thin
section indicates a porosity of 13.92%. The remainder of the thin section (86.08%) is
composed of whole grains.
77
Matrix
Grains
Grains
Grains
Matrix
Fracture
Matrix
Fracture
Figure 4-18: Thin section of Sample P1 in plain light (PPL). ). Some of the fractures developed, matrix, and grains are indentified in
the thin section.
78
Matrix
Matrix
Matrix
Grains
Fracture
Matrix
Figure 4-19: Thin section of P3 in plain light (PPL). Some of the fractures developed, matrix, and grains are indentified in the thin
section.
79
Grains
Fractures
Matrix
Grains
Matrix
Fracture
Matrix
Matrix
Grains
Figure 4-20: Thin section of Sample P4 in plain light (PPL). Some of the fractures developed, matrix, and grains are indentified in the
thin section.
80
Grains
Grains
Grains
Matrix
Grains
Grains
Figure 4-21: Thin Section of Sample P5 in plain light (PPL). Some of the fractures developed, matrix, and grains are indentified in
the thin section.
81
4.3.2. JMicroVision Software
Point counting using JMicroVision was conducted using photographs of thin
sections. JMicroVision uses a random grid system fixated upon the pixel. Three separate
categories were created to classify pixels- Pore Space, Grain, and Void. The void
category applies to the regions where the point counter landed on a portion of the
photograph which is not a part of the thin section or regions where epoxy-filled pore
space resulted from thin section construction. Void spaces within the thin section and
opaque grains could not be differentiated in the photographs. Thin section diagrams with
point counts are provided in Appendix G. Porosity calculations derived from
JMicroVision ranges from ~7% to ~46% (see Figure 4-22).
4.4 Porosity-Density Data
Porosity data for all the samples was derived mathematically using a porosity and
sample density relationship, displayed in EQ 3-6. Each sample was processed three times
in order to calculate an average. Porosity calculations for each sample and trial run are
included in Appendix H. Porosity calculations used for analysis are displayed in Figure 423. The porosity data is categorized by the sampling location within the fault zone. The
protolith samples (I1, I2, B1, and P5) have porosities ranging from ~9% to ~44%. The
damage zone samples (B2, P1, P2, P3, and MF9) have porosities ranging from ~10% to
68%. The fault core samples (B3, MF1, MF2, MF4, MF6, MF7, P4, and RCB1) have
porosities ranging from ~17% to ~52%.
A comparison of all porosity values calculated is presented in Table 4-2. A
porosity calculation from bulk density was not calculated for sample B4 because no
consolidated material was found in the sample. The material collected for sample B4 was
82
Figure 4-22: Porosity derived from point counting using a JMicroVision software.
83
Figure 4-23: Porosity derived from porosity and sample density relationship.
84
Table 4-2: Porosity comparison of the different methods employed to derive porosity from unconsolidated sediments. The porosity
values derived from the Density Porosity method is the only method with a standard deviation error (1) calculation.
85
all loose material.
4.5 Grain Size Analysis
A grain size analysis was performed on all the samples using dry sieving
mechanisms. Grain analysis and distribution curves are included in Appendix I. Grain
size distribution curves were plotted using DPlot software. Four effective grain size
diameters were recorded from the grain-size distribution curves. The effective grain sizes
are D10, D20, D50, and D60 (mm), see Table 4-3 for values. The D10 effective grain sizes
range from 0.03 mm – 0.21 mm. The D20 effective grain sizes range from 0.05 mm0.55mm. The D50 effective grain sizes range from 0.07 mm- 1.53 mm. The D60 effective
grain sizes range from 0.06 mm – 2.36 mm. The effective grain-sizes of D10 and D60 are
used to calculate the uniformity coefficient of the each sample, as provided in Table 4-4.
Sample ranges from well sorted to poorly sorted.
4.6 Hydraulic Conductivity
Hydraulic conductivity of each sample was calculated using two separate
empirical relationships. The Breyer equation (EQ 3-9) relates effective grain size of D10
from grain size distribution and uniformity coefficient to hydraulic conductivity. Derived
hydraulic conductivity values using the Breyer equation are presented in Table 4-5.
Breyer hydraulic conductivity values range from ~13 m/s to ~1875 m/s. The Slichter
equation (EQ 3-10) relates effective grain size of D10 from grain size distribution and
porosity of the sample to the hydraulic conductivity. Hydraulic conductivity values using
the Slichter equation are present in Table 4-6. Slichter hydraulic conductivity values
range from ~0.17 m/s to ~151 m/s. The difference in calculated hydraulic conductivity
values of the two equations is due to different variables used in the calculation.
86
Table 4-3: Effective grain size measurements from grain size distribution curves. D 10 correlates to the 10th %, D20 correlates to the
20th %, and D50 correlates to the 50th % and D60 correlates to the 60th % of sediments finer than.
Millimeter
P5
Fault Zone
Location
Protolith
B1
Protolith
9.34
0.06
0.11
0.44
0.64
B4
I1
I2
Protolith
Protolith
Protolith
NA
44.78
27.52
0.19
0.07
0.21
0.45
0.09
0.41
2.59
0.14
1.53
5.33
0.17
2.36
MF1
Fault Core
17.62
0.09
0.26
1.29
1.96
MF2
MF4
MF6
Fault Core
Fault Core
Fault Core
29.09
18.95
47.69
0.10
0.09
0.12
0.27
0.22
0.29
1.19
0.73
1.19
1.83
0.75
1.71
MF7
P4
Fault Core
Fault Core
30.52
52.82
0.20
0.06
0.38
0.13
1.38
0.54
2.03
0.71
B3
RCB1
Fault Core
Fault Core
23.58
29.20
0.13
0.06
0.26
0.18
0.69
1.51
0.73
2.25
MF9
P1
Damage Zone
Damage Zone
10.81
24.81
0.08
0.08
0.14
0.18
0.45
0.69
0.63
0.73
P2
P3
Damage Zone
Damage Zone
68.88
32.83
0.03
0.03
0.05
0.04
0.19
0.07
0.21
0.08
B2
Damage Zone
18.52
0.33
0.55
0.75
0.78
Sample
Average Porosity
D10
D20
D50
D60
10.73
0.07
0.15
0.63
0.92
87
Table 4-4: Uniformity coefficient (Cu) for samples organized by their location within the
fault zone architecture. Cu values less than 4 are considered to be well sorted and Cu
values greater than 6 are considered to be poorly sorted.
Sample
P5
B1
B4
I1
I2
MF1
MF2
MF4
MF6
MF7
P4
B3
RCB1
MF9
P1
P2
P3
B2
Fault Zone Location
Protolith
Protolith
Protolith
Protolith
Protolith
Fault Core
Fault Core
Fault Core
Fault Core
Fault Core
Fault Core
Fault Core
Fault Core
Damage Zone
Damage Zone
Damage Zone
Damage Zone
Damage Zone
Uniformity Coefficient
Cu= D60/D10
12.43
11.20
28.07
2.43
11.41
20.76
18.00
7.89
14.38
9.95
10.97
5.70
39.43
7.91
9.67
6.15
2.64
2.35
88
Sorting
Poorly Sorted
Poorly Sorted
Poorly Sorted
Well Sorted
Poorly Sorted
Poorly Sorted
Poorly Sorted
Poorly Sorted
Poorly Sorted
Poorly Sorted
Poorly Sorted
Poorly Sorted
Poorly Sorted
Poorly Sorted
Poorly Sorted
Poorly Sorted
Well Sorted
Well Sorted
Table 4-5: Hydraulic conductivity (K) and intrinsic permeability (ki) values calculated
using the Breyer equation.
Breyer
Hydraulic Conductivity
Permeability
Sample Fault Zone Location
KBreyer
ki-Breyer
P5
Protolith
65.04
m/s
5.29592E-06
m2
B1
Protolith
40.00
m/s
3.25688E-06
m2
B4
Protolith
331.50
m/s
2.69938E-05
m2
I1
Protolith
79.58
m/s
6.47991E-06
m2
I2
Protolith
516.04
m/s
4.20205E-05
m2
MF1
Fault Core
222.97
m/s
7.16703E-06
m2
MF2
Fault Core
270.15
m/s
8.68326E-06
m2
MF4
Fault Core
293.22
m/s
9.42495E-06
m2
MF6
Fault Core
391.91
m/s
1.25972E-05
m2
MF7
Fault Core
1279.19
m/s
4.11169E-05
m2
P4
Fault Core
51.45
m/s
4.18935E-06
m2
B3
Fault Core
236.21
m/s
1.92342E-05
m2
RCB1
Fault Core
26.49
m/s
2.15686E-06
m2
MF9
Damage Zone
207.12
m/s
6.65751E-06
m2
P1
Damage Zone
72.42
m/s
5.89696E-06
m2
P2
Damage Zone
16.70
m/s
1.35948E-06
m2
P3
Damage Zone
13.78
m/s
1.12218E-06
m2
B2
Damage Zone
1875.66
m/s
0.000152733
m2
Density of
water
Dynamic
Viscosity
Gravitational Kinematic
Constant
Viscosity
Temperature
30°C
90°C
g (m/s2)
v (m2/s)
r (kg/m3)
m (kg m/s)
9.8
8.01E-07
3.26E-07
1000
0.000798
0.000315
89
Table 4-6: Hydraulic conductivity (K) and intrinsic permeability (k i) values calculated
using the Slichter equation.
Slichter
Hydraulic Conductivity
Permeability
KSlichter
ki-Slichter
P5
Fault Zone
Location
Protolith
0.44 m/s
3.578E-08 m2
B1
Protolith
0.17 m/s
1.36E-08 m2
B4
Protolith
NA
NA
I1
Protolith
40.89 m/s
3.33E-06 m2
I2
Protolith
75.36 m/s
6.136E-06 m2
MF1
Fault Core
3.64 m/s
1.17E-07 m2
MF2
Fault Core
21.92 m/s
7.047E-07 m2
MF4
Fault Core
4.66 m/s
1.498E-07 m2
MF6
Fault Core
151.28 m/s
4.863E-06 m2
MF7
Fault Core
103.17 m/s
3.316E-06 m2
P4
Fault Core
63.44 m/s
5.166E-06 m2
B3
Fault Core
17.56 m/s
1.43E-06 m2
RCB1
Fault Core
7.00 m/s
5.7E-07 m2
MF9
Damage Zone
0.52 m/s
1.673E-08 m2
P1
Damage Zone
7.21 m/s
5.869E-07 m2
P2
Damage Zone
42.78 m/s
3.483E-06 m2
P3
Damage Zone
2.59 m/s
2.111E-07 m2
B2
Damage Zone
52.59 m/s
4.282E-06 m2
Sample
Gravitational
Constant
Kinematic
Viscosity
Density of
water
Temperature
g (m/s2)
v (m2/s)
r (kg/m3)
30°C
90°C
9.8
8.01E-07
3.26E-07
1000
90
Dynamic
Viscosity
m (kg
m/s)
0.000798
0.000315
4.7 Intrinsic Permeability
Permeability data was derived from an empirical relationship between the
hydraulic conductivity and intrinsic permeability. This relationship relates the hydraulic
conductivity to properties of the fluid. Intrinsic permeability calculations are derived
from the Breyer and Slichter formulas. Intrinsic permeability calculated by using the
hydraulic conductivity values from the Breyer equation are presented in Table 4-5.
Intrinsic permeability values range from ~9.42E-6 m2 to ~1.52E-4 m2. Calculation of
permeability values using the hydraulic conductivity values from the Slichter equation
are presented in Table 4-6. Intrinsic permeability values range from ~3.6E-8 m2 to ~6.1E6 m 2.
91
5. Discussion
The results of this study are discussed with respect to the method used for analysis.
5.1 Field Data
Numerical measurements of fault zone architecture and permeability structure
derived from conceptual modeling developed by Caine et al (1996) of fault zones
indicates that the Mission Creek Strand behaves as a conduit to fluid flow and the
Banning Strand behaves as conduit-barrier fluid flow systems (Figures 4-10 and 4-11).
The Mission Creek Strand is characterized as a conduit because the theoretically
calculated values associated with the fault zone measurements fall on the conduit extent
of the indices created by Caine et al. (1996). The Banning Strand is categorized as
conduit-barriers because the theoretical values associated with the fault zone
measurements fall in between the barrier and conduit extents set by Caine et. al (1996). In
Caine et al. (1996)’s classification the Fa index numbers of 0 indicating a barrier forming
fault and 1 indicating a conduit forming fault.
Classification and measurement of the fault zone architecture was conducted in
the field, thus some discrepancies may exist in the assignment of a damage zone and fault
gouge, especially along the Mission Creek Strand at the Mt. View Road Cut location.
However, these discrepancies should not alter the identification of zones present in the
fault zone architecture. The major discrepancy is expected to lie in the identification of
the extent of the damage zone because it is noticed that the width of the damage zone
varies along strike. For instance, at Location B1-Whitewater Canyon the damage zone
associated with the footwall maybe greater than measured; however, the sampling
location is disrupted due to erosion. Another discrepancy may lie in the number of fault
92
cores indentified because some fault planes may not be visible due to deposition of
material post-fault movement causing some splays to not be exposed at the surface. Even
if some portions of a damage zone or fault cores were not measured in this study, I
believe that it will not have drastic impacts on the conceptual classification of these
faults. However, additional fault outcrop measurements will aide to provide a more
robust data set to calculate the fault zone architectural indices.
Sample collection methods can be improved in future fault zone studies by
collecting additional samples using a coring device. However, the applicability of a
coring device to collect samples of a known size is unknown due to the frailness and
unconsolidated stages of some sampling locations.
5.2 Core Labs Data
The data analysis conducted by Core Labs provides a standardized data set for
comparison. Analysis of the four samples indicates that the protolith rock (Sample I1) has
the highest air permeability (314.96 millidarcies (md)), as expected. However, the
discrepancy in the data exists between the fault core sample (MF7) and samples from the
damage zone (P1 and P3). Sample MF7 indicates an air permeability of 47.52
millidarcies (md) where as Samples P1 and P3 indicate air permeabilities of 8.462 md
and 23.03 md, respectively (results are provided in Appendix D- Figure D-1). The four
samples processed were associated with the Mission Creek Strand; however, they are
associated with different regions of the fault strand. Thus, a correlation of permeability
changes across the Mission Creek fault zone cannot be concluded from the samples
processed at Core Labs. If the samples are to be applied to the Mission Creek Strand, the
results would indicate a permeable fault gouge but an impermeable damage zone. This
93
data is contrary to the conceptual scheme for fault-related fluid flow developed by Caine
et al. (1996) where the damage zone is believed to be more permeable than the fault
gouge.
Samples P1 and P3 are from the same locality, Pushawalla Canyon, however, they
indicate varied permeabilities (Figure D-1 located in Appendix D). This difference in
calculation can be a result of variation in lithology of the siltstones and sandstones
indicating that facies variations during deposition maybe evident, as identified in Figure
5-1.
5.3 Thin Section Data
Two different methods were employed to conduct a point count analysis on thin
sections in order to calculate pore space relative to grain/matrix space in a thin section.
One method involved using a petrographic microscope with a 4X lens and slideadvancing machine to maneuver the thin section, while the second method involved
computer software which randomly picked a point based on the pixel location. A
discrepancy exists between the two methods used for point counting. The difference in
porosity calculation is attributed to the fact that thin-section maneuvering mechanisms
employed differ. The slide-advancing machine maneuvered in a lateral format, right to
left. Thus, point counts were conducted in a linear format across the thin section. In some
cases, only half of the thin section may have been examined for pore space because only
a 500-point count was conducted. Point counting on the JMicroVision software was
conducted randomly with the point counter moving to various locations of the thin
section. Thus, with this method, the entire thin section had the same probability of being
examined for pore space under a 500-point count.
94
Figure 5-1: Lithofacies identification at Pushawalla Canyon (west-side wall). Yellow outlines indicate cobble/coarse-gravel deposits.
Red outlines indicate fine silt to clay deposits. The remaining material is identified as sand ranging from coarse sand to fine sand.
Person provided for scale and photographs correlated with regions A, B, and C are provided below.
95
96
One of the caveats associated with conducting a point-count using JMicroVision,
is that it is not possible to distinguish between void spaces and opaque grain orientations.
On the other hand, the petrographic microscope provides a higher resolution for accurate
determination of pore space vs. grain/matrix space. Due to the randomization of the point
counting associated with using JMicroVision, these porosity values are believed to be
more accurate than the porosity values calculated using the petrographic microscope.
Randomization of point counting across the entire thin section is important for these
samples do to the deformation and /or widening of permeable pathways which may have
formed due to injection of epoxy.
Porosity calculations derived from thin sections are not common due to the
deformation that can occur in the thin section making process. From a visual analysis of
the thin sections, it is noted that most of the deformation may have occurred due to epoxy
injection. Thus, I suggest conducting thin section analysis without epoxy injection.
Another method that can be used for pore space analysis using thin section is injecting
the thin section with a fluorescing element and using a fluorescence microscope to
determine pore space. The fluorescence microscope would also be able to indicate micro
pore spaces and pore spaces less than 30 microns (thickness of a thin section). Thin
sections should also be examined for degree of cementation that exists in the sample.
Thus, a mechanism to measure and indicate cementation in a sample must be devised.
The porosity data derived from the thin sections is not accounted for in the hydraulic
conductivity and intrinsic permeability calculations because values could not be derived
for all samples. The porosity calculations derived from thin-sections indicate high
porosity values ranging from 13.91%- 39.04% for thin sections examined via microscope
97
and 7.89%- 46.64% for thin sections examined via JMicroVision. These high porosity
values are indicative of high permeability values. However, the expected permeability
values and recorded porosity values are not reproducible by standardized testing at Core
Labs. Thus, the data derived from thin sections is disregarded and the thin sections are
used for visual analysis. Visual analysis of the thin sections provides a cross sectional
view of the samples. From the visual analysis, it can be noted that many of the samples
developed fracture networks when injected with epoxy.
5.4 Porosity-Density Relationship
Porosity for all the samples was derived from an empirical relationship between
bulk density of the sample and the grain density. Three separate sample runs were
performed on each sample to calculate porosity and average of the values is considered
the true porosity of the sample. Data from each sample run is included in Appendix H. In
some of the porosity runs, a negative porosity value was calculated. The negative
porosity calculations are not considered representative of the sample because a negative
porosity value is not realistic, thus, these values were not included in the average
calculation. The negative porosity values could have been due to heterogeneity in the
sample used in the sample run for volume calculation because negative values were not
calculated for every trial run for a sample. Errors associated with the porosity calculation
may occur when determining the volume of the sample. The porosity calculations may be
higher than the actual porosity values because the bulk volume of the sample was not
properly calculated due to infiltration of water into the air-filled pore spaces during the
water displacement test. Infiltration of water into the pore spaces was noted due to the
presence of air bubbles in the beaker and the disintegration of the sample in the beaker.
98
Thus, it may be possible that for some samples only the volume of the grains was
recorded and not the volume of the sample. However, a true sample volume was
calculated for samples where the cementation blocked the infiltration of the water. The
infiltration of water into the sample would result in a lower volume calculations and
resulting in higher bulk density calculations. The miscalculation of the bulk density of the
material had direct impact on the porosity calculations because of the selected porosity
calculation equation (EQ 3-6).
From the various methods employed to calculate porosity, it is noted that the
calculations show high variations in porosity for samples from the protolith rock, damage
zones, and fault cores. The porosity values did not display a significant trend across the
fault zones identified, implying that the faults do not form barriers or conduits in this
region. The high variation in the porosity values across the fault zones can be a result of
facies variations during deposition. It is noted that not all samples were collected in a
lateral form across the fault zone. Thus, not all samples included in the damage zone,
fault core, and protolith rock can be stratigraphically correlated. Deposition of various
facies is evident in the region due to the convergence of various stream systems, eolian
deposits, possible debris flows, and/or variations in alluvial fill material. The various
lithologies present in the region are displayed in the geologic map of the study area
(Figure 5-2).
5.5 Grain Size analysis
A dry sieving grain size analysis was conducted on all the samples. The grain size
analysis displays a high degree of heterogeneity within the samples (see Table 4-4 for
Uniformity Coefficient values). The sieving process provided an effective grain size (D 10)
99
Figure 5-2: Geologic map of quaternary surficial deposits in the study region. (modified map of Palm Springs 30’X60’ Quadrangle by
California Geological Survey, 2012)
100
and analysis of grain size distributions (provided in Appendix I). From the grain-size
distribution curves, it is noted that the samples should be processed with a hydrometer or
laser particle analyzer to allow the measurement of particle sizes smaller than 0.045mm.
Processing samples with clay size particles using a hydrometer or laser particle analyzer
will provide a more thorough analysis of the grain size distribution. Comparison of the
uniformity coefficients associated with the fault strands does not indicate any correlation
between grain-size distributions and fault strand, as indicated in Table 5-1. A comparison
of the uniformity coefficients at each sampling location does not indicate that samples
associated with a single fault strand are more uniform than the other strand.
Some samples (P2, P5 MF1, MF2, MF4, MF7, and B1) needed to be disaggregated using
a pestle and mortar. Thus, these samples may contain grains that were crushed during the
process due to the pestle hitting a weak plane in the grain. This process should not add a
high degree of error as the samples are highly heterogeneous in the first place.
The effective grain size calculated from the grain-size distribution charts is D10.
However, for some samples, the distribution did not extend to the grain diameter that is
correlated to 10% finer by weight (90% coarser by weight). For these samples (MF2, P2,
and P3), the grain size curve was extended and the D10 value is approximated. This
approximation process can be corrected by conducting hydrometer test or laser particle
analyzer.
5.6 Hydraulic Conductivity and Permeability
Hydraulic conductivity and intrinsic permeability of the samples was derived
from empirical relationships relating grain size and porosity. It is noted that hydraulic
conductivity values can vary depending on the equation used, thus, two different
101
Table 5-1: Comparison of Uniformity Coefficients (CU) categorized by fault strand. An
average of CU is provided for each sampling location.
Protolith
Banning Strand
Mission Creek Strand
Sample Uniformity Coefficient Average
P1
P2
P3
P4
P5
MF1
MF2
MF4
MF6
MF7
MF9
9.67
6.15
2.64
10.97
12.43
20.76
18
7.89
14.38
9.95
7.91
B1
11.2
B2
2.35
B3
5.7
B4
28.07
RCB1
39.43
I1
2.43
I2
11.41
8.372
13.1483
11.83
39.43
6.92
equations are used in this analysis (EQ 3-9 and EQ 3-10).
An examination of the hydraulic conductivity values across the Banning Strand at
Whitewater Canyon (Location- B) indicates lower hydraulic conductivity in the protolith
rock and the fault core than in the damage zone (see Figure 5-3). Analysis of the
hydraulic conductivity rates and permeability values indicate that the damage zone is
more permeable than the fault core and protolith rock. Thus, the fault core impedes fluid
flow while the damage zone enhances flow.
Analysis of values calculated for the Banning Strand at river cut near Via Las
Palms Road (Location RCB) indicates lower hydraulic conductivity rates for the fault
102
Figure 5-3: Schematic diagram of the fault zone architecture at sampling location B
(Whitewater Canyon – Banning Strand). Calculated hydraulic conductivities are
displayed in their respective location within the architecture. Fluid flow at this location is
in the north to south direction. Sample B1 is not considered in the analysis because it is
from the SGIM Complex, which is not a water-bearing unit.
core than the protolith rock. The intrinsic permeability values indicate that the fault core
is less permeable than the protolith rock. See Figure 5-4 for distribution of hydraulic
conductivity and intrinsic permeability values across the fault zone architecture. An
average of hydraulic conductivity and intrinsic permeability values was used for the
protolith rock region of the architectural fault zone because more than one sample was
collected. Analysis of hydraulic conductivity rates and intrinsic permeability values
indicate that the fault core is less permeable than the surrounding protolith rock. Thus, the
fault core impedes fluid flow at this sampling location.
103
Figure 5-4: Schematic diagram of the fault zone architecture identified at a river cut near
Via Las Palms Rd (location RCB-Banning Strand). Calculated hydraulic conductivities
and permeabilities are displayed in their respective location within the architecture. Fluid
flow at this location is believed to be north/northwest to south/southeast direction.
Analysis of values calculated for the Mission Creek Strand at Mt. View Road Cut
(Location- MF) indicates higher hydraulic conductivity rates and intrinsic permeability
values for the fault cores than the damage zone and protolith rocks (See Figure 5-5). An
average of hydraulic conductivity and an average of intrinsic permeability values were
used for the fault core samples and the protolith rock samples because more than one
sample was collected related to the respected region of the architectural fault zone. At
this location, the damage zone behaves as a barrier rather than the fault core. The damage
zone associated with the fault zone is less permeable than the fault core. Analysis of
values calculated for the Mission Creek Strand at Pushawalla Canyon (location P)
indicates a higher hydraulic conductivity and intrinsic permeability for the fault core than
104
Figure 5-5: Schematic diagram of fault zone architecture identified at the Mt. View Road
Cut (Location MF-Mission Creek Strand). Calculated hydraulic conductivities and
permeabilities are displayed in their respective location within the architecture. An
average was calculated where more than one sample was collected for the defined zone.
Fluid flow at this location is believed to be north/northwest to south/southeast direction.
the damage zone and protolith rocks. Figure 5-6 displays a schematic diagram of the fault
zone with the hydraulic conductivity and intrinsic permeability values with respect to
their locations within the fault zone architecture. An average of hydraulic conductivity
and intrinsic permeability values were calculated for the damage zone because multiple
samples were collected for this region of the fault zone architecture. The hydraulic
conductivity and intrinsic permeability values calculated for this location indicate that the
fault core is more permeable than the damage zone.
Each sample location is characterized to contain the three fault zone architectural
elements discussed in Figure 1-1. Hydraulic conductivity and intrinsic permeability
105
Figure 5-6: Schematic diagram of the fault zone architecture identified at Pushawalla
Canyon (Location P-Mission Creek Strand). Calculated hydraulic conductivities and
permeabilities are displayed in their respective location within the architecture. An
average was calculated where more than one sample was collected for the defined zone.
Fluid flow at this location is believed to be a north to south direction.
values for the distinguished fault zone architectural zones are provided in Table 5-2.
Graphical representation of the hydraulic conductivity values for the damage zone and
fault core with respect to the protolith rock provides a visualization of the dominating
hydraulic conductivity associated with the architectural zone. Figure 5-7 indicates that
samples associated with the Mission Creek Strand (Locations MF and P) are more
influenced by the hydraulic conductivity within the fault core than in the damage zone,
whereas samples associated with the Banning Strand (Location B) are more dominated by
the hydraulic conductivity within the damage zone. Samples associated with the fault
zone identified at RCB are not accounted for because samples associated with the
damage zone were not collected and processed. Thus, these hydraulic conductivity values
associated with the damage zone at RCB cannot be calculated or assumed. A comparison
106
Table 5-2: Hydraulic conductivity and intrinsic permeability values for distinguished zones within the fault zone architecture.
Sample Location Fault Zone Location
B
RCB
MF
KBreyer
Slichter
KSlichter
ki-Slichter
m/s 3.25688E-06 m2 0.17 m/s 1.4E-08 m2
1875.66 m/s 0.000152733 m2 52.59 m/s 4.3E-06 m2
236.21 m/s 1.92342E-05 m2 17.56 m/s 1.4E-06 m2
Protolith
B1
Damage Zone
B2
Fault Core
B3
Protolith
B4
Protolith
I1 and I2
Fault Core
RCB1
Protolith
I1 and I2
297.81 m/s
Damage Zone
207.12 m/s
491.49 m/s
1.57979E-05 m2 56.94 m/s 1.8E-06 m2
Protolith
MF9
MF1, MF2, MF4,
MF6, MF7
P5
2.42502E-05 m2 58.13 m/s 4.7E-06 m2
2
2
6.66E-06 m 0.52 m/s 1.7E-08 m
65.04
m/s
Damage Zone
P1, P2, P3
34.30
m/s
Fault Core
P4
51.45
m/s
5.29592E-06 m2 0.44 m/s 3.6E-08 m2
2.79287E-06 m2 17.53 m/s 1.4E-06 m2
4.18935E-06 m2 63.44 m/s 5.2E-06 m2
Fault Core
P
Sample(s)
Breyer
ki-Breyer
40.00
NA
NA
331.50 m/s 2.69938E-05 m2
297.81 m/s 2.42502E-05 m2 58.13 m/s 4.7E-06 m2
26.49 m/s 2.15686E-06 m2 7.00 m/s 5.7E-07 m2
107
Figure 5-7: Graphical representation of fault core and damage zone hydraulic conductivity values with respect to the hydraulic
conductivity values of the protolith rock. The graphical representation indicates that the samples associated with the Mission Creek
Strand (locations MF and P) fall in the conduit forming fault core region while samples associated with the Banning Strand (locations
B and RCB) fall in the barrier forming fault core region.
108
of hydraulic conductivity values for the fault core and the damage zone indicates
distinctions for which zone is more hydraulically conductive than the other regions of the
fault zone. Figure 5-8 indicates that samples associated with the Mission Creek Strand
(Locations MF and P) have higher hydraulic conductivities for the fault core. Whereas,
samples associated with the Banning Strand (Location B) have higher hydraulic
conductivities for the damage zone than the fault core. Samples from location RCB are
not displayed because samples associated with the damage zone were not collected.
5.7 Implications
My analysis of intrinsic permeability and hydraulic conductivity values contradict
the analysis done in accordance to Caine et al. (1996), where the Mission Creek Strand is
characterized as a conduit-forming fault and the Banning Strand is characterized as
barrier-conduit fault. Caine et al.’s research stated that the fault core is the portion of the
fault zone which has the ability to act as a barrier and the damage zone has the ability to
act as a conduit. The analysis of the intrinsic permeability and hydraulic conductivity
values across the fault zone architecture indicates that different portions of the fault zone
can impede or enhance fluid flow. Along the Mission Creek Strand, it is noted that the
fault core is more permeable relative to the damage zone; whereas, along the Banning
Strand the damage zone is more permeable relative to the fault core. Analysis of the data
at all four sampling locations indicates that the fault zones are barriers to fluid flow
relative to the protolith rock; however, the barrier is not a result of just fault gouge
development. The barriers formed by the Mission Creek Strand and the Banning Strand
are a result of the damage zone and the fault gouge, respectively. The data implies that
Caine et al.’s (1996) theoretical classification scheme of indentifying a barrier, barrier-
109
Figure 5-8: Graphical representation of fault core and damage zone hydraulic conductivity values with respect to each other. The
graphical representation indicates that the samples associated with the Mission Creek Strand (locations MF and P) fall in the conduit
forming fault core region while samples associated with the Banning Strand (locations B and RCB) fall in the conduit forming damage
zone region.
110
conduit, and conduit forming faults does not apply to all faults, such as the active San
Andreas Fault zone where unconsolidated sediments are faulted.
5.8 Fractoconformity vs. Fault Zone Controlled Fluid Flow
Differentiating whether the groundwater table, in northwestern Coachella Valley,
is affected by a fractoconformity or by the fault zone architecture is important in order to
develop a better understanding of the dynamic relationship between faults and fluid
interaction system along the San Andreas Fault zone. If the groundwater table were offset
due to a fractoconformity then permeability variations within the fault zone would not
have drastic effects upon the pore pressure and effective normal stress across the fault
zone. The fractoconformity would not affect the dynamic relationship between fault and
fluid systems because offset in the water table would be an offset resulting from vertical
displacement of water-bearing units. However, if the groundwater table were offset due
to gouge development in the fault zone then extraction and injection of water would
impact the dynamic relationship between the faults and fluid systems by altering the pore
pressure and effective stresses across the fault zone. The groundwater table would impact
the permeability field of a fault zone, fluid pressure, and the effective normal stress
across the fault zone; thus, potentially facilitating seismic events along a fault plane
(Figure 2-12). My analysis of intrinsic permeability and hydraulic conductivity indicate
that the Mission Creek and Banning Strands behave as barriers to groundwater flow due
to development of the fault zone architecture, which contradicts the theoretical
measurements of Caine et al., (1996) and Catchings et al., (2009) seismic survey.
Distinguishing which portion of a fault zone acts as a barrier to groundwater
flow is important in order to measure the effectiveness of a groundwater flow barrier. The
111
fault core is characterized as a region where sediments have undergone diagenesis and
cataclasis during the fault movement process, whereas, the damage zone is characterized
as a zone of deformation and fracture development that has occurred during the fault
movement process. Thus, along faults where the fault core acts as a barrier, like the
Banning Strand, groundwater flow is retarded due to gouge development and
precipitation of carbonate within fractures. Thus, future seismic events along this strand
will further encourage the diagenesis of grains along the fault plane and enhance the fault
core’s ability to behave as barrier. Along faults where the damage zone acts as a barrier,
like the Mission Creek Strand, groundwater flow is retarded due to deformation occurring
in the damage zone. Both fault strands, Mission Creek and Banning Strands, behave as
barriers to groundwater flow today as shown by a decrease in intrinsic permeability and
hydraulic conductivity in the fault zone, however, the barrier is influenced by not only the
fault core but by the damage zone as well.
112
6. Conclusion
The investigations carried out and presented in this thesis represent a preliminary
effort to characterize fluid flow across the San Andreas Fault zone in Northwest
Coachella Valley, California. Outcrop investigations allowed detailed mapping of the
fault zone architecture associated with the Mission Creek and Banning Strands (no
outcrops of the Garnet Hill Strand were indentified in the study region) revealing
measurements of fault zone architecture, hydraulic conductivity, and permeability across
the fault zones.
Porosity variations across the fault zone were determined using three different
methods: thin sections, Core Labs Inc., and density-porosity relationship from a water
displacement test. Porosity ranged from 9.34% +/- 7.77% to 68.88% +/- 26.11% in the
region according to the water displacement test. Intrinsic permeability was determined
empirically from hydraulic conductivity values and grain size distributions. Two different
equations were used to calculate hydraulic conductivity in order to display possibility of
error from porosity measurements. The Breyer equation was selected because it did not
require a porosity measurement and hydraulic conductivity measurements ranged from
13.79 m/s to 1875.66 m/s. The Slichter equation was selected because it did require a
porosity measurement and hydraulic conductivity measurements ranged from 0.17 m/s to
151.28 m/s. The variations in the calculated values for porosity, hydraulic conductivity,
and intrinsic permeability indicate that values can vary depending on the methodology
and empirical formula used to derive values.
My hypothesis of a fractoconformity being the main cause of variations in the
groundwater table rather than a fault behaving as a barrier to fluid flow was not supported
113
by the measured intrinsic permeability and hydraulic conductivity values. However, my
data provides insight to what causes a barrier along Mission Creek Strand and the
Banning Strand.
My analysis of the intrinsic permeability and hydraulic conductivity values across
the fault zone architecture associated with the Mission Creek and Banning Strands
provide insight as to why these fault cause the water table to be offset. My data indicates
that the Mission Creek Strand behaves as a barrier to fluid flow due the presence of an
impermeable damage zone; whereas, the Banning Strand behaves as a barrier to fluid
flow due to the presence of an impermeable fault core (see Figure 5-7). This analysis
indicates that different components within a fault zone can impede or enhance fluid flow
with respect to the other components, thus, a general classification system of classifying a
fault’s ability to behave as a barrier or conduit in unconsolidated sediments does not
exist.
114
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118
Appendix A-Aerial Photography
Figure A-5a: Vegetation alignment along the Banning Strand at Whitewater Canyon.
Figure A-5b: Vegetation alignment along the Mission Creek Strand near the city of
Desert Hot Springs, CA.
119
Figure A-5c/d: Vegetation alignment along Banning Strand, west of the Indio Hills. This
location is often referred to as Seven Palms.
Figure A-5e: Vegetation along the Mission Creek Strand, on the north side of the Indio
Hills. Location e is referred to as 1000 Palms.
120
Figure A-5g: Vegetation along the Mission Creek Strand, in the Indio Hills. Location g
is referred to as Pushawalla Canyon.
Figure A-5g: Vegetation along the Banning Strand, along the south side of the Indio
Hills. Location f is referred to as Willis Palms Oasis
121
Appendix B-Field Notes
Table B-1
8/18/2013
Location: Whitewater Canyon
Date:
33.947025, 116.651536
GPS Location:
Fault: Banning Strand
Weather- Sunny with a high of 104F with light desert rain flashes.
Fault outcrop of the Banning Strand where the Pre-Cambrian San GorgonioField DescriptionIgneous-Metamorphic Complex (SGIM Complex) basement rock is faulted against
the late-Pleistocene and/or early-Holocene Cabezon Fanglomerate rocks.
A 13 meter-wide fault zone (measured North to South) is identified at this location
Fault Zone Identificationwith a well developed localized fault gouge, a clay-rich brecciated damage zone,
and surrouding protolith rock.
Description
Width (m)
SGIM Complex with gneissic banding. Banding is
alternating in color between very pale orange (10YR 8/2)
and dusky yellow green (5GY 5/2). Banding thickness
varies from slivers of ~10cm to zones of ~1m-1.5m. This
0-3.7m
region is highly fractured causing the rock to be weakly
consolidated. Sample B1 collected. Two measurements of
attitude were recorded (strike,dip): 250°,66NW and
270°,41NW.
Majority of the material is from the SGIM Complex,
however, it is more deformed and less consolidated.
3.7-4.7m
Vertical fracturing which is perpendicular to the bedding
plane is present. Fractures are spaced out by ~5cm-10cm.
Contact between SGIM and dark grey material. The dark
grey material is weakly consolidated and fine grained. This
material is considered to be apart of the damage zone.
4.7m
Strike and dip were measured from a consolidated plane:
270,50NW.
122
4.7-5.5m
5.5-8.2m
8.2-8.7m
8.7-8.8m
8.8-8.9m
8.9-13m
Continued Table B-1
Exterior damage zone with angular to sub-angular clasts (~0.5mm-2.0mm). The
clasts are classified as fault zone breccia. Contains very fine grains and is weakly
consolidated.
Interior damage zone. Dark yellowish brown in color (10Y 4/2). Clay-rich in
appearance and in texture. Carbonate rich veins present. The entire region is HCL
reactant.
Deformed interior damage zone. Moderate yellowish brown in color (10YR 5/4).
Material is banded by a calcareous residue with is 2mm-16mm in width.
Representative sample of deformation zone is collected at this location (Sample
B2).
Localized fault gouge. Moderate brown in color (5YR 3/4). Material is clay-rich
is apperance, texture and contains clay-like layering. Root traces and burrowing is
present in the region. Representative sample of localized fault core-gouge is
collected (Sample B3)
Moderately consolidated and cemented breccia clasts within in the matrix of the
fault gouge. This region is identified as the interior damage zone.
Very unconsolidated (weakly) conglomerate of various size cobbles (~0.5mm12inches). Grains are sub-angular to sub-rounded. A representative sample was
collected from the surface of the unconsolidated material (Sample B4). Clast
count and identification was conducted in the field as well.
123
Continued Table B-1
Width (meters)
9m
Description
Size (cm)
1
50
8
6
15
Type
Granite
Granite
Granite
Granite
Matrix
Granite
Schist -> Gneiss
Granite
Matrix-silty to coarse sand
Matrix- Coarsed sand with silty matrix
Diorite
Granite
Hornblend rich dike or hornblend diorite
Granite
Granite
Matrix
Granite
Gneiss
Matrix
Matrix
Granite
Quartizite (from a pigmatite)
Granite
Granite
Matrix
Matrix
Matrix
Granite
Pigmatite
Granite
Granite
Quartizite
Matrix
Matrix
Matrix
Granite
15
12
Hornblend diarite
granite
2
5
1
4
5
13
15
9
5
50
Clast
Count
20
10
1
15
20
6
2
30
1
13m
124
Continued Table B-1
Width (meters)
13m
Description
Size (cm)
1
3
3
3
1
2
4
1
8
1
10
1
2
5
2
12
1
Clast
Count
3
15
2
10
9m
Type
Matrix
Matrix
Granite
Matrix
Basalt
Matrix
Schist
Diorite
Granite
Granite
Metamorpic Diorite-grey meta-sed rock
Granite
Granite
Shale
Granite
Granite
Granite
Granite
Garnet Schist
Granite
Granite
Matrix
Matrix
Matrix
Granite
Matrix
Matrix
Granite
Quartzite
Granite
Matrix
125
Table B-2
Location: Mt. View Road Cut
33.941113, -116.475172
GPS Location:
Weather- Sunny with a high of 104°F with light desert rain flashes.
Field
Description-
Fault Zone
Identification-
8/18/2013
Date:
Fault: Mission Creek Strand
Fault outcrop of splays associated with the Mission Creek Strand. Outcrop is located on the upthrown block of
the Mission Creek Strand and bounded on the northern end by the Miracle Hill Fault. The outcrop being
examined is known as Miracle Hill.
A 136.6 meter-wide fault zone is identified at this location. No localized fault gouge was found at this
location, however, numerous minor fault splays and/or fractures were identified. Fault zone was measured
across in a South to North direction where fracture and/or splays were identified by meter markings. The
minor faults are identified as Splays 1-8.
Meter
marking
Description
0m
Start of uplifted block. The 0-meter marking is considered to be the base location of the Mission
Creek Strand. Representative sample collected of the loose matrix sediments (MF9).
22.10m
37.40 m
41m
Splay 1: a calcareous cemented pebbly consolidated sand-precipitate filled core. South of the
splay is a sand lens that is truncated by Splay 1, showing a slip of >3 meters. No clay like fault
gouge or damage zone present. However, a calcareous plane (~7cm in width) is present with a
strike,dip of 140°, 64°S. Representative sample collected (MF1).
Splay 2: calcareous pebbly sand-precipitate filled core. Truncates beds of sand lens against a bed
of conglomerate. Unknown displacement. Only calcareous in the splay zone. Calcareous plane
varies in width from ~3 cm to ~7 cm and has a strike,dip of 146°,69°S. Representative sample
collected (MF2).
Splay 3: calcareous unconsolidated precipitate filled core with a thickness of ~3-4 cm. Splay
displays either down dropped block to the south or up thrown block to the north, however, no
sense of displacement is identified. Fault plane has a strike,dip of 135°,40°S. No sample
collected.
126
Meter marking
Continued Table B-2
Description
52.2m
Splay 4a: Calcareous pebble sandy- precipitate filled core with a thickness of ~4-5 cm. The fault plane has a
strike,dip of 095°,72°S. Splay 4a displays >3 meters of apparent dispalcement.
53.7m
Splay 4b: Calcareous pebble sandy- precipitate filled core with a thickness of ~4-5 cm. The fault plane has a
strike/dip of 101°,61°S. Representative sample collected (MF4).
74.9m
Splay 5a: Very calcareous pebbly sandy-precipitate core with a thickness of ~10-15 cm. Precipitate core
contains pebbles upto 3 cm in diameter. Fault splay truncates a channel fill which has pebbly conglomerate at
the base. The pebbly conglomerate has calcareous cementing. The fault plane has a strike, dip of 135°,69°S.
Fault core is not very consolidated. No sample collected due to the sparcy nature of the fault plane.
77.4m
Splay 5b: Moderately consolidated pebbly sandy-precipitate core with a thickness of ~ 5-10 cm. Pebble in
fault core are not deformed. Fault core is not planar but is instead wavy and contains pebble to cobble size
loose material. Fault plane has a strike,dip of 240°,60°N, dip is in opposite direction than splay 5a. No sample
collected.
93.4m
Splay 6: Very calcareous pebbly coarse sandy-moderatly consolidated core with a width of ~6-8 cm. Fault
truncates pebbly sand lens on the north and conglomerate beds with large boulders on the south. Fault plane
has a strike,dip of 094°,69°S and extended up to 1/2 meter from the surface. Representative sample collected
from base of the plane because there is a bee-hive at the top (MF6).
107m
Splay 7: Very calcareous pebbly coarse sand/precipitate filled core with a thickness of ~15 cm. The fault plane
has a strike,dip of 135°,70°S and displays slicken lines with a rake of 15°SW of the strike. Fault plane is
consolidated and and a representative sample was collected (MF7).
112m
Splay 8a: calcareous pebbly coarse sandy core with a thickness of ~2-3 cm. Fault plane does not have the
same white precipitate filled texture and appearance in the same manner as splays 1-7. Fault plane has a strike
and dip of 135°,60°S. Sediments surrounding the fault plane are calcareous. No sample was collected.
127
Continued Table B-2
Description
Meter marking
118.5m
Splay 8b: pebbly coarse sandy core with a thickness of ~2-3 cm. Fault plane does not have the same white
precipitate filled core but sediments are calcareous surrounding the fault plane. The fault displays >1 meter of
slip and has a strike,dip of 110°,60°S. No sample was collected.
128
W
es
t
Si
de
W
all
Table B-3
Location: Pushawalla Canyon
Date:
9/8/2013
GPS Location: 33.821139, -116.286875
Fault: Mission Creek Strand
Weather- Sunny with a high of 98°F with light cloud coverage.
Fault outcrop of splays associated with the Mission Creek Strand. Outcrop location is suggested by Kimberly
Field
Blisniuk. The river carved canyon provides a cross-sectional view of the fault splays associated with the Mission
DescriptionCreek strand.
A 43 meter wide fault zone, measured from the South to North was identified on the western side of the canyon. No
Fault Zone
localized fault gouge was found on the western wall of the canyon, however, a well developed localized fault gouge
Identificationwas located on the eastern wall of the canyon.
Meter
GPS Location
Description
marking
0 meter marking is marked by a gullie with undeformed beds on the southern side
33.821139,
and beds with minor deformation and offset on the northern side. The beds on the
-116.286875
southern side of the gullie have an apparent dip to the NE which could be the result
of fault dragging.
The material north of the 0 meter marking is composed of various beds of sandstone
0m
and shale. Colors vary from browns to greens. These deposits maybe associated
with the upper member of the Ocotillo Formation or terrace deposits.
The material on the south side of the 0 meter marking is composed of conglomerate
channels and fine grained sandstone beds. In comparison to the the northern
portion, the southern portion has more variations in composition and colors.
In the river bed, vegatation is only evident on the north side of the 0 meter marking.
33.821164,
29.4 meter marking is noted because beds start to slope in a nearly verticle
29.4m
-116.286583
direction. Sample P3 was collected.
40 meter marking is noted because the beds identified at the 29.4m appear to be
33.821189,
40m
horiztonal to deposition. Sample (P1 and P2) were collected slightly north of the 40
-116.286397
meter marking.
129
Continued Table B-3
Meter
marking
GPS Location
Ea
st
Si
d
eW
al
l
33.820767,
-116.286456
33.819711
-116.286741
Description
The eastern side of the canyon wall was not measured because
deformation of rocks was not noticable.
Localized fault gouge with clay like layering and a thickness of ~10cm. A
representative sample of gouge was collected (P4). The localized gouge is
Dark Yellowish Brown (10YR 4/2) in color. The fault plane has a strike,
dip of 170The riverbed surrounding the outcrop is densly
vegatative.
On the northside of the gouge, the material is Yellowish Gray (5Y 7/2) in
color. The material is highly fractured with oxidation present in the
fractures. Grain size ranges from 0.5mm to clay size.
On the southside of the gouge, the material ranges in color from
Moderate Yellow (5Y 7/6) to Greyish Orange (10YR 7/4). The material
is also fractured. Grain size ranges from 9in-7in to clay size grain grains.
The southside is more conglomerate based.
Sample P5 collected is from the footwall of the fault zone. The sample is
collected to serve as representative sample of water-bearing units away
from the fault zone (protolith rock sample).
\
130
Table B-4
Location: River Cut Banning
Date: 9/8/2013
GPS Location: 33.842786, -116.370136
Fault: Banning Strand
Sunny
with
a
high
of
98°F
with
light
cloud
coverage.
Desert
thunder
storm
clouds
are starting to develop.
WeatherFault outcrop associated with the Banning Strand. Outcrop location is suggested by Kimberly Blisniuk.
Field
The river carved canyon provides a cross-sectional view of a fault outcrop associated with the Banning
Descriptionstrand.
Fault Zone A 8.5 meter wide fault zone was identified (measured North to South) with identifiable fault gouge at 3.7
Identification- meters.
Meter
marking
0m
3.7m
GPS Location
Description
33.842786,
-116.370136
Start of what appears to be a damage zone associated with faulting.
Material is highly affected by popcorn weathering but is consolidated and
not brittle. The fault zone is defined by the popcorn weathing.
North of the 0 meter marker, the beds are dipping near verticle. This may
be associated with down drop/slumping of a block due to faulting or
erosion.
Localized fault gouge. Fault plane appears to be associated with a low
angle thrust fault with a strike,dip of 250, 26NW. Gouge is weakly
developed and appears to be 5cm-10cm in thickness. Representative
sample collected of gouge (RCB-1).
131
Table B-5
Location: Indio Hills
Date:
3/14/2014
GPS Location: 33.833296, -116.3060639
Weather- Sunny with a high of 87°F with light breeze.
Field
Description-
David Nye's thesis from CSU, Fullerton states that water travels through permeable units of the Indio
Hills in a NW to S and/or SW direction. Thus, samples are collected from the Indio Hills as
representative samples of water bearing units.
Sample
Description
Sample I1 is a very fine grained siltstone. It is weakly
consolidated. Homogenous in composition.
Sample I2 is a fine grained sandstone with pebbles.
Sample contains oxidation in fractures. It is weakly
consolidated and hetrogenous in composition.
I1
I2
132
Appendix C-Samples
Figure C-1: Sample B1 in outcrop (A) and collected sample (B and C). Scale is provided
in cm.
133
Figure C-2: Sample B2 in outcrop (A) and collected sample (B). Scale is provided in cm.
134
Figure C-3: Sample B3 in outcrop (A) and collected sample (B and C). Scale is provided
in cm.
135
Figure C-4: Sample MF1 collected (A and B). Scale is provided in cm.
136
Figure C-5: Sample MF2 in outcrop (A) and collected sample (B, C, and D). Scale is
provided in cm.
137
Figure C-6: Sample MF4 collected. Scale is provided in cm.
138
Figure C-7: Sample MF6 in outcrop (A) and collected sample (B and C). Scale is
provided in cm.
139
Figure C-8: Sample MF7 in outcrop (A) and collected sample (B and C). Scale is
provided in cm.
140
Figure C-9: Sample MF9 in outcrop. Rock hammer for scale.
141
Figure C-10: Sample P1 collected sample. Scale is provided in cm.
142
Figure C-11: Sample P2 collected sample. Scale is provided in cm.
143
Figure C-12: Sample P3 collected sample. Scale is provided in cm.
144
Figure C-13: Sample P4 in outcrop (A) and collected sample (B and C). Scale is provided in cm.
145
Figure C-14: Sample P5 collected sample. Scale is provided in cm.
146
Figure C-15: Sample RCB1 in outcrop (A) and collected sample (B, C, and D). Scale is
provided in cm.
147
Figure C-16: Sample I1 collected sample. Scale is provided in cm.
Figure C-17: Sample I2 collected sample. Scale is provided in cm.
148
Appendix D-Core Labs Data
Figure D-1: Modified table of data collected at Core Labs showing air permeability and porosity measurements.
149
Figure D-2: Photographs of samples cored at Core Labs in lead sleeves (Samples I1, MF7, P1,
and P3).
150
Appendix E- Thin-sections under 1.25X
Figure E-1: Sample B2 thin-section under PPL and XP with 1.25x magnification
151
Figure E-2: Sample B3 thin-section under PPL and XP with 1.25x magnification
152
Figure E-3: Sample MF4 thin-section under PPL and XP with 1.25x magnification
153
Figure E-4: Sample MF7 thin-section under PPL and XP with 1.25x magnification
154
Figure E-5: Sample P1 thin-section under PPL and XP with 1.25x magnification
155
Figure E-6: Sample P3 thin-section under PPL and XP with 1.25x magnification
156
Figure E-7: Sample P4 thin-section under PPL and XP with 1.25x magnification
157
Figure E-8: Sample P5 thin-section under PPL and XP with 1.25x magnification
158
Appendix F- Point-Counting under the Petrographic Microscope
159
160
161
162
Appendix G- JMicroVision Data
Figure G-1: Point counting (500 valid counts). Blue dots represent epoxy, yellow dots
represent grain/matrix, and grey dots represent invalid or undetermined region.
Figure G-2: Point counting (500 valid counts). Blue dots represent epoxy, yellow dots
represent grain/matrix, and grey dots represent invalid or undetermined region.
163
Figure G-3: Point counting (500 valid counts). Blue dots represent epoxy, yellow dots
represent grain/matrix, and grey dots represent invalid or undetermined region.
Figure G-4: Point counting (500 valid counts). Blue dots represent epoxy, yellow dots
represent grain/matrix, and grey dots represent invalid or undetermined region.
164
Figure G-5: Point counting (500 valid counts). Blue dots represent epoxy, yellow dots
represent grain/matrix, and grey dots represent invalid or undetermined region.
Figure G-6: Point counting (500 valid counts). Blue dots represent epoxy, yellow dots
represent grain/matrix, and grey dots represent invalid or undetermined region.
165
Figure G-7: Point counting (500 valid counts). Blue dots represent epoxy, yellow dots
represent grain/matrix, and grey dots represent invalid or undetermined region.
Figure G-8: Point counting (500 valid counts). Blue dots represent epoxy, yellow dots
represent grain/matrix, and grey dots represent invalid or undetermined region.
166
Appendix H- Porosity- Density Data
Table H-1: Porosity data derived from bulk density and volume of samples. Porosity values are organized with respect to their
location within the fault zone architecture.
Porosity
Sample
B3
MF1
MF2
MF4
MF6
MF7
P4
RCB1
B2
P1
P2
P3
MF9
I1
I2
B1
P5
Sample
Location
Fault Core
Fault Core
Fault Core
Fault Core
Fault Core
Fault Core
Fault Core
Fault Core
Damage Zone
Damage Zone
Damage Zone
Damage Zone
Damage Zone
Protolith
Protolith
Protolith
Protolith
Trail-1
Trail-2
Trail-3
Average
10.12578616 29.71698113 30.90754717 23.58343816
0.824528302 21.49622642 30.54339623 17.62138365
41.21509434 31.10377358 14.95283019 29.09056604
13.88679245 19.50188679 23.46540881 18.95136268
60.49528302 -9.401257862 34.88867925 47.69198113
50.96037736 6.883018868 33.72528302 30.52289308
89.18238994 66.27358491 3.018867925 52.82494759
20.75471698 37.98742138 28.86792453 29.2033543
-8.296855346 16.73962264 20.30691824 18.52327044
17.84528302 -3.091320755 31.76754717 24.80641509
81.41509434 86.36477987 38.86792453 68.88259958
38.11320755 -14.71698113 27.54716981 32.83018868
11.29716981 8.490566038 12.64150943 10.80974843
27.16981132 32.83018868 74.33962264 44.77987421
8.113207547 46.91823899 -29.05660377 27.51572327
0.724528302
15.8327044
11.4745283 9.343920335
5.828092243 1.635220126 24.71698113 10.7267645
167
Standard
Deviation
11.66986117
15.23363327
13.24637245
4.812980407
18.10660317
22.21249331
44.62832638
8.621247596
2.522458907
9.844527391
26.11102039
7.471316933
2.117963048
25.7554657
27.43930088
7.776172838
12.29592067
Sample: RCB1
A
B
C
Average RCB1-3 RCB1-2 RCB1-1
Mass of
Mass of Sample* Sample
Container
and
Mass*
(g)
container
(g)
(g)
D
E
F
G
H
I
J
Mass of
Water
Volume of
Amount
Coffee
Sample
Level
Mass of
Original
of DI
Filter** Mass** and
with
Container
Sample
Water
and
container
Sample*
(g)
3
(mL)
Container
and
filter (g)
(mL=cm )
(mL)
(g)
Oven
Dried
Bulk Density
Sample
(g/cm3 )
Mass
(g)
3.45
10.9
7.45
21
24.5
3.5
3.74
5.03
12.38
7.35
3.45
8.47
5.02
10
13
3
3.74
5.03
9.96
4.93
3.45
7.31
3.86
18
20
2
3.74
5.03
8.8
3.77
3.74
5.03
10.38
5.35
3.45
8.89333 5.44333 16.3333 19.1667 2.833333
K
2.1
Porosity:
n (%)
20.755
1.64333333 37.987
1.885
28.868
1.87611111 29.203
Standard Deviation:
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
L
8.6212
n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
168
Sample: RCB-1
100
90
80
Porosity (%)
70
60
50
40
37.99
30
28.87
20
29.20
20.75
10
0
RCB1-1
RCB1-2
169
RCB1-3
Average
Sample: I1
E
F
G
H
I
J
K
Mass of
Sample
Mass of
Amount
Water Volume of
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Mass of
of DI Level with Original
Filter**
and
Dried Density
Container
and
Mass*
Container
Water
Sample* Sample
and
container Sample
(g)
container
(g)
(g)
(g/cm3 )
3
(mL)
(mL)
Container
and
filter
Mass
(g)
(mL=cm )
(g)
(g)
(g)
L
n (%)
I1-1
D
3.45
5.41
1.96
11
12
1
3.74
5.03
6.96
1.93
1.93
27.1698
I1-2
C
3.45
7.04
3.59
10
12
2
3.74
5.03
8.59
3.56
1.78
32.8302
I1-3
B
3.45
8.22
4.77
12
19
7
3.74
5.03
9.79
4.76
0.68
74.3396
Average
A
3.45
6.89
3.44
11
3.74
5.03
14.33333 3.333333
8.44667 3.41667 1.4633 44.7799
Standard Deviation:
3
Particle density =
2.65 g/cm
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
Porosity:
170
25.7555
n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
Sample: I1
100
90
80
Porosity (%)
70
74.34
60
50
44.78
40
30
20
32.83
27.17
10
0
I1-1
I1-2
I1-3
171
Average
Sample: I2
H
I
J
K
Mass of
Sample
Mass of
Volume
of
Amount
Water
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Mass of
of DI Level with Original
Filter**
and
Dried
Density
Container
and
Mass*
Container
Sample
Water
Sample*
and
container Sample
(g)
container
(g)
(g)
(g/cm3 )
3
(mL)
(mL)
Container and filter Mass (g)
(mL=cm
)
(g)
(g)
(g)
Average I2-3 a I2-2 I2-1
A
B
C
D
E
F
G
n (%)
3.45
8.36
4.91
16
18
2
3.74
5.03
9.9
4.87
2.435
3.45
7.65
4.2
12
15
3
3.74
5.03
9.25
4.22
1.4067 46.918
3.45
10.24
6.79
20
22
2
3.74
5.03
11.87
6.84
3.45
8.005
4.555
14
16.5
2.5
3.74
5.03
9.575
4.545
3.42
3
Particle density =
2.65 g/cm
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
Porosity:
sample run was not included in average calculation
172
8.1132
-29.06
1.9208 27.516
Standard Deviation:
a
L
27.439
n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
Sample: I2
100
90
80
Porosity (%)
70
60
50
46.92
40
30
27.52
20
10
8.11
0
I2-1
I2-2
173
I2-3a
Average
Sample: P5
H
Mass of
Mass of
Amount
Water Volume of
Coffee
Mass of Sample* Sample
Mass of
Original
of DI Level with
Filter**
Container
and
Mass*
Container
Sample
Water Sample*
and
(g)
container
(g)
(g)
3
(mL)
(mL)
Container
(mL=cm )
(g)
(g)
Average P5-3 P5-2 P5-1
A
B
C
D
E
F
G
I
J
K
Sample
Mass**
Oven
Bulk
and
Dried
Density
container Sample
3
(g/cm )
and filter Mass (g)
(g)
n (%)
3.45
14.75
11.3
22
26.5
4.5
3.74
5.03
16.26
11.23
2.49556 5.8281
3.45
11.28
7.83
27
30
3
3.74
5.03
12.85
7.82
2.60667 1.6352
3.45
7.37
3.92
16
18
2
3.74
5.03
9.02
3.99
3.74
5.03
12.71
7.68
3.45
11.1333 7.6833 21.6667 24.83333 3.166667
1.995
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
Porosity:
sample run was not included in average calculation
174
24.717
2.36574 10.727
Standard Deviation:
a
L
12.296
n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
Sample: P5
100
90
80
Porosity (%)
70
60
50
40
30
20
24.72
10
0
10.73
1.64
5.83
P5-1
P5-2
P5-3
175
Average
Sample: P4
E
F
G
H
I
J
Mass of
Sample
Mass of
Volume
of
Amount Water
Coffee
Mass**
Oven
Mass of Sample* Sample
Mass of
of DI Level with Original
Filter**
and
Dried
Container
and
Mass*
Container
Water Sample* Sample
and
container Sample
(g)
container
(g)
(g)
3
(mL)
(mL)
Container and filter Mass (g)
(mL=cm )
(g)
(g)
(g)
K
L
Bulk
Density
n (%)
3
(g/cm )
P4-1
D
3.45
5.27
1.82
10
16
6
3.74
5.03
6.75
1.72
0.28667 89.182
P4-2
C
3.45
10.89
7.44
12
20
8
3.74
5.03
12.18
7.15
0.89375 66.274
P4-3
B
3.45
14.08
10.63
34
38
4
3.74
5.03
15.31
10.28
Average
A
3.45
10.08
6.63
6
3.74
5.03
18.667 24.66667
3
Particle density =
2.65 g/cm
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
2.57
3.0189
11.41333 6.38333 1.25014
52.825
Standard Deviation:
44.628
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
176
Sample:P4
100
90
89.18
80
Porosity (%)
70
66.27
60
50
52.82
40
30
20
10
3.02
0
P4-1
P4-2
P4-3
177
Average
Sample: P3
A
B
C
H
I
J
K
Mass of
Sample
Amount
Water Volume of
Coffee
Mass**
Oven
Bulk
Mass of
of DI Level with Original
Filter**
and
Dried
Density
Container
Sample
Water Sample*
and
container Sample
3
(g)
(mL)
(mL)
Container and filter Mass (g) (g/cm )
(mL=cm3 )
(g)
(g)
L
n (%)
P3-1
G
3.45
6.33
2.88
10
11.75
1.75
3.74
5.03
7.9
2.87
1.64
38.11
P3-2 a
F
3.45
6.49
3.04
24
25
1
3.74
5.03
8.07
3.04
3.04
-14.72
P3-3
E
3.45
6.31
2.86
21
22.5
1.5
3.74
5.03
7.91
2.88
1.92
27.55
Average
Mass of
Mass of Sample* Sample
Container
and
Mass*
(g)
container
(g)
(g)
D
3.45
6.32
2.87
15.5
17.125
1.625
3.74
5.03
7.905
2.875
1.78
32.83
Standard Deviation:
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
7.471
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
178
Sample:P3
100
90
80
Porosity (%)
70
60
50
40
38.11
30
32.83
27.55
20
10
0
P3-1
P3-2a
179
P3-3
Average
Sample: P2
H
Mass of
Mass of
Amount
Water Volume of
Coffee
Mass of Sample* Sample
Mass of
of DI Level with Original
Filter**
Container
and
Mass*
Container
Sample
Water
Sample*
and
(g)
container
(g)
(g)
3
(mL)
(mL)
Container
(mL=cm )
(g)
(g)
Average P2-3 P2-2 P2-1
A
B
C
D
E
F
G
I
Sample
Mass**
and
container
and filter
(g)
J
K
Oven
Bulk
Dried
Density n (%)
Sample
(g/cm3 )
Mass (g)
3.45
7.53
4.08
21
29
8
3.74
5.03
8.97
3.94
0.4925 81.415
3.45
6.23
2.78
12
19.5
7.5
3.74
5.03
7.74
2.71
0.3613 86.365
3.45
8.41
4.96
30
33
3
3.74
5.03
9.89
4.86
3.45
7.39
3.94
21
3.74
5.03
8.866667
27.16667 6.166667
1.62
3
Particle density =
2.65 g/cm
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
38.868
3.83667 0.8246 68.883
Standard Deviation:
a
L
26.111
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
180
Sample:P2
100
90
86.36
80
81.42
70
Porosity (%)
68.88
60
50
40
30
27.55
20
10
0
P2-1
P2-2
181
P2-3
Average
Sample: P1
H
I
J
K
Mass of
Sample
Mass of
Amount Water Volume of
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Mass of
Original
of DI Level with
Filter**
and
Dried
Density
Container
and
Mass*
Container
Water Sample* Sample
and
container Sample
(g)
container
(g)
(g)
(g/cm3 )
3
(mL)
(mL)
Container and filter Mass (g)
(mL=cm
)
(g)
(g)
(g)
Average P1-3 P1-2 a P1-1
A
B
C
D
E
F
G
n (%)
3.45
7.76
4.31
18
20
2
3.74
5.03
9.3842
4.3542
2.1771 17.845
3.45
10.25
6.8
27
29.5
2.5
3.74
5.03
11.8598
6.8298
2.73192 -3.091
3.45
7.95
4.5
20
22.5
2.5
3.74
5.03
9.5504
4.5204
1.80816 31.768
3.45
7.855
4.405
19
21.25
2.25
3.74
5.03
9.4673
4.4373
1.99263 24.806
Standard Deviation:
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
L
9.8445
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
182
Sample:P1
100
90
80
Porosity (%)
70
60
50
40
30
31.77
24.81
20
10
17.85
0
P1-1
P1-2a
183
P1-3
Average
Sample: MF9
A
B
C
Average MF9-3 MF9-2 MF9-1
Mass of
Mass of Sample* Sample
Container
and
Mass*
(g)
container
(g)
(g)
D
E
G
H
Mass of
Water
Volume
of
Amount
Coffee
Level
Mass of
Original
of DI
Filter**
with
Container
Sample
Water
and
Sample*
(g)
3
(mL)
Container
(mL) (mL=cm )
(g)
I
J
Sample
Mass**
Oven
and
Dried
container Sample
and filter Mass (g)
(g)
K
L
Bulk
Density
n (%)
3
(g/cm )
3.73
41.63
37.9
39
55
16
3.59
5.03
42.64
37.61
3.73
28.22
24.49
36
46
10
3.59
5.03
29.28
24.25
2.425
8.49057
3.73
22.57
18.84
34
42
8
3.59
5.03
23.55
18.52
2.315
12.6415
3.59
5.03
3.73
30.8067 27.0767 36.3333 47.6667 11.3333
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
F
2.35063 11.2972
31.8233 26.7933 2.36354
10.8097
Standard Deviation:
2.11796
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
184
Sample:MF9
100
90
80
Porosity (%)
70
60
50
40
30
20
10
11.30
12.64
8.49
10.81
0
MF9-1
MF9-2
185
MF9-3
Average
Sample: MF7
H
I
J
K
Mass of
Sample
Mass of
Water
Volume of
Amount
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Level
Mass of
Original
of DI
Filter**
and
Dried Density
Container
and
Mass*
with
Container
Sample
Water
and
container Sample
(g)
container
(g)
Sample*
(g)
(g/cm3 )
3
(mL)
Container
and
filter
Mass
(g)
(g)
(mL) (mL=cm )
(g)
(g)
Average MF7-3 MF7-2 MF7-1
A
B
C
D
E
F
G
n (%)
3.45
8.72
5.27
19
23
4
3.74
5.03
10.2282
5.1982 1.29955 50.96
3.45
5.9
2.45
14
15
1
3.74
5.03
7.4976
2.4676
3.45
12.5
9.05
20
25
5
3.74
5.03
13.8114
8.7814 1.75628 33.725
3.45
9.04
5.59
17.667
21
3.333333
3.74
5.03
10.5124
5.4824 1.84114 30.523
2.4676
Standard Deviation:
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
L
6.883
22.212
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
186
Sample:MF7
100
90
80
Porosity (%)
70
60
50
50.96
40
33.73
30
30.52
20
10
6.88
0
MF7-1
MF7-2
187
MF7-3
Average
Sample: MF6
A
B
C
D
E
F
G
H
I
J
K
Average MF6-3 MF6-2 a MF6-1
Mass of
Sample
Mass of
Amount Water Volume of
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Mass of
of DI Level with Original
Filter**
and
Dried
Density
Container
and
Mass*
Container
Water Sample* Sample
and
container Sample
(g)
container
(g)
(g)
(g/cm3 )
(mL)
(mL) (mL=cm3 )
Container and filter Mass (g)
(g)
(g)
(g)
n (%)
3.45
11.84
8.39
18
26
8
3.74
5.03
13.405
8.375
1.0469 60.495
3.45
7.81
4.36
19.5
21
1.5
3.74
5.03
9.3787
4.3487
2.8991 -9.401
3.45
6.95
3.5
15
17
2
3.74
5.03
8.4809
3.4509
1.7255 34.889
3.45
9.395
5.945
16.5
21.5
5
3.74
5.03
10.94295 5.91295 1.3862 47.692
Standard Deviation:
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
L
18.107
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
188
Sample:MF6
100
90
80
Porosity (%)
70
60
60.50
50
47.69
40
34.89
30
20
10
0
MF6-1
MF6-2a
189
MF6-3
Average
Sample: MF4
H
I
J
K
Mass of
Sample
Mass of
Amount Water Volume of
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Mass of
Original
of DI Level with
Filter**
and
Dried
Density
Container
and
Mass*
Container
Water Sample* Sample
and
container Sample
(g)
container
(g)
(g)
(g/cm3 )
3
(mL)
(mL)
Container
and
filter
Mass
(g)
(mL=cm )
(g)
(g)
(g)
Average MF4-3 MF4-2 MF4-1
A
B
C
D
E
F
G
n (%)
3.45
5.8
2.35
10
11
1
3.74
5.03
7.312
2.282
2.282
3.45
7.79
4.34
13
15
2
3.74
5.03
9.2964
4.2664
2.1332 19.502
3.45
9.57
6.12
24
27
3
3.74
5.03
11.1145
6.0845
2.0282 23.465
3.45
7.72
4.27
15.667
17.6667
2
3.74
5.03
9.240967 4.21097 2.1478 18.951
Standard Deviation:
3
Particle density =
2.65 g/cm
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
L
13.887
4.813
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
190
Sample:MF4
100
90
80
Porosity (%)
70
60
50
40
30
20
23.47
19.50
10
18.95
13.89
0
MF4-1
MF4-2
191
MF4-3
Average
Sample: MF2
H
I
J
K
Mass of
Sample
Mass of
Amount Water Volume of
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Mass of
of DI Level with Original
Filter**
and
Dried Density
Container
and
Mass*
Container
Water Sample* Sample
and
container Sample
(g)
container
(g)
(g)
(g/cm3 )
3
(mL)
(mL) (mL=cm )
Container and filter Mass (g)
(g)
(g)
(g)
Average MF2-3 MF2-2 MF2-1
A
B
C
D
E
F
G
n (%)
3.45
6.6
3.15
15
17
2
3.74
5.03
8.1456
3.1156
1.5578 41.215
3.45
7.2
3.75
14
16
2
3.74
5.03
8.6815
3.6515
1.8258 31.104
3.45
12.49
9.04
43
47
4
3.74
5.03
14.045
9.015
2.2538 14.953
3.74
5.03
10.2907
5.2607
1.8791 29.091
3.45
8.76333 5.3133
24
26.6667 2.666667
Standard Deviation:
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
L
13.246
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
192
Sample:MF2
100
90
80
Porosity (%)
70
60
50
40
41.22
30
31.10
29.09
20
14.95
10
0
MF2-1
MF2-2
193
MF2-3
Average
Sample: MF1
H
I
J
K
Mass of
Sample
Mass of
Amount Water Volume of
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Mass of
of DI Level with Original
Filter**
and
Dried Density
Container
and
Mass*
Container
Water Sample* Sample
and
container Sample
(g)
container
(g)
(g)
(g/cm3 )
3
(mL)
(mL) (mL=cm )
Container and filter Mass (g)
(g)
(g)
(g)
Average MF1-3 MF1-2 MF1-1
A
B
C
D
E
F
G
n (%)
3.45
8.76
5.31
53
55
2
3.74
5.03
10.2863
5.2563
2.6282 0.8245
3.45
7.67
4.22
49
51
2
3.74
5.03
9.1907
4.1607
2.0804 21.496
3.45
6.14
2.69
38
39.5
1.5
3.74
5.03
7.7909
2.7609
1.8406 30.543
48.5
1.833333
3.74
5.03
9.0893
4.0593
2.183
3.45
7.52333 4.0733 46.667
Standard Deviation:
3
Particle density =
2.65 g/cm
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
L
17.621
15.234
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
194
Sample:MF1
100
90
80
Porosity (%)
70
60
50
40
30
30.54
20
21.50
17.62
10
0.82
0
MF1-1
MF1-2
MF1-3
195
Average
Sample: B3
H
I
J
K
Mass of
Sample
Mass of
Amount Water Volume of
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Mass of
of DI Level with Original
Filter**
and
Dried
Density
Container
and
Mass*
Container
Water Sample* Sample
and
container Sample
3
(g)
container
(g)
(g)
(mL)
(mL) (mL=cm3 )
Container and filter Mass (g) (g/cm )
(g)
(g)
(g)
Average B3-3 B3-2 B3-1
A
B
C
D
E
F
G
n (%)
3.45
11.17
7.72
57
60
3
3.74
5.03
12.175
7.145
2.3817 10.126
3.45
7.41
3.96
40
42
2
3.74
5.03
8.755
3.725
1.8625 29.717
3.45
7.39
3.94
55
57
2
3.74
5.03
8.6919
3.6619
1.831
30.908
53
2.333333
3.74
5.03
9.873967 4.84397
2.025
23.583
3.45
8.65667 5.2067 50.667
Standard Deviation:
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
L
11.67
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
196
Sample:B3
100
90
80
Porosity (%)
70
60
50
40
30
30.91
29.72
20
23.58
10
10.13
0
B3-1
B3-2
197
B3-3
Average
Sample: B2
A
B
C
D
E
F
G
H
I
J
K
Average B2-3 B2-2 B2-1a
Mass of
Sample
Mass of
Amount Water Volume of
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Mass of
of DI Level with Original
Filter**
and
Dried
Density
Container
and
Mass*
Container
Water Sample* Sample
and
container Sample
3
(g)
container
(g)
(g)
(mL)
(mL) (mL=cm3 )
Container and filter Mass (g) (g/cm )
(g)
(g)
(g)
n (%)
3.45
7.49
4.04
53
54.5
1.5
3.74
5.03
9.3348
4.3048
2.8699 -8.297
3.45
5.78
2.33
53
54
1
3.74
5.03
7.2364
2.2064
2.2064
3.45
5.05
1.6
23
23.75
0.75
3.74
5.03
6.6139
1.5839
2.1119 20.307
3.45
5.415
1.965
38
38.875
0.875
3.74
5.03
6.92515
1.89515 2.1591 18.523
Standard Deviation:
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
L
16.74
2.5225
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
198
Sample:B2
100
90
80
Porosity (%)
70
60
50
40
30
20
20.31
16.74
18.52
10
0
B2-1a
B2-2
199
B2-3
Average
Sample: B1
H
I
J
K
Mass of
Sample
Mass of
Volume
of
Amount Water
Coffee
Mass**
Oven
Bulk
Mass of Sample* Sample
Mass of
Original
of DI Level with
Filter**
and
Dried
Density
Container
and
Mass*
Container
Water Sample* Sample
and
container Sample
3
(g)
container
(g)
(g)
3
(mL)
(mL) (mL=cm )
Container and filter Mass (g) (g/cm )
(g)
(g)
(g)
Average B1-3 B1-2 B1-1
A
B
C
D
E
F
G
n (%)
3.45
11.49
8.04
61
64
3
3.74
5.03
12.9224
7.8924
2.6308 0.7245
3.45
10.19
6.74
63
66
3
3.74
5.03
11.7213
6.6913
2.2304 15.833
3.45
12.89
9.44
48
52
4
3.74
5.03
14.4137
9.3837
2.3459 11.475
3.74
5.03
13.01913 7.98913 2.4024 9.3439
3.45
11.5233 8.0733 57.333
60.6667 3.333333
Standard Deviation:
Particle density =
2.65 g/cm3
* Before Oven Drying at 104C for 24 Hours
** After Oven Drying at 104C for 24 Hours
a
L
7.7762
Porosity: n=100 [1-(bulk density/particle density)] = porosity %
Bulk density= Oven dried sample mass/original sample volume
Volume of Sample= water with sample -original water
sample run was not included in average calculation
200
Sample:B1
100
90
80
Porosity (%)
70
60
50
40
30
20
15.83
10
11.47
0.72
9.34
0
B1-1
B1-2
B1-3
201
Average
Appendix I- Grain Size Distribution Charts
202
203
204
205
206
207
208
209
210
211
212
213
214
5.72"
215
216
217
218
219