University of Nevada, Reno
Analysis of remote sensing data for geothermal exploration over Fish Lake Valley, Esmeralda County, Nevada
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Geophysics
By
Elizabeth F. Littlefield
Dr. Wendy M. Calvin/Thesis Advisor
December, 2010
THE GRADUATE SCHOOL
We recommend that the thesis prepared under our supervision by
ELIZABETH F. LITTLEFIELD
entitled
Analysis of remote sensing data for geothermal exploration over Fish Lake Valley, Esmeralda County, Nevada
be accepted in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE
Wendy M. Calvin, Ph.D., Advisor
Mark F. Coolbaugh, Ph.D., Committee Member
Jill S. Heaton, Ph.D., Graduate School Representative
Marsha H. Read, Ph. D., Associate Dean, Graduate School
December, 2010
i
Abstract
The purpose of this study was to identify and map hydrothermal alteration and geothermal deposits in northern Fish Lake Valley, Nevada using both visible, near, shortwave infrared (0.4-2.5 µm) and thermal infrared (8-12 µm) remote sensing data.
Visible, near, and shortwave infrared data were collected by four airborne instruments
including NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and
MODIS-ASTER (MASTER) instruments, HyVista Corporation’s HyMap sensor, and
SpecTIR Corporation’s ProSpecTIR instrument. MASTER also collected thermal
infrared data over Fish Lake Valley. Hydrothermal alteration minerals and hot spring
deposits were identified using diagnostic spectra extracted from the remote sensing data.
Mapping results were verified in the field using a portable spectrometer. Two areas of
opaline sinter and travertine deposits were identified west of the Fish Lake Valley playa.
Field observation reveals the alternating nature of these beds, which likely reflects
fluctuating hot spring fluid chemistries. Sinter and travertine were likely deposited
around fault-related hot springs during the Pleistocene when the water table was higher.
Previously undiscovered Miocene crystalline travertine was identified within the
Emigrant Hills near Columbus Salt Marsh. Argillic alteration was mapped in parts of the
ranges surrounding Fish Lake Valley. Kaolinite, and to a lesser extent, muscovite and
montmorillonite, were used as indicator minerals for argillic alteration. In these regions,
thermal fluids were likely discharged from faults to alter rhyolite tuff. Mineral maps
were synthesized with previously published geologic data and used to delineate four new
targets for future geothermal exploration. The abundant hot spring deposits along the ii edge of the Volcanic Hills combined with argillic alteration minerals mapped in the ranges suggest geothermal influence throughout much of the valley.
iii
Acknowledgements
I would like to thank my advisor, Dr. Wendy Calvin, who has provided me with the wonderful combination of freedom and support that has allowed this project to remain both meaningful and reasonable. I have appreciated her kindness, patience, and vast intelligence. Thanks to my committee members, Drs. Mark Coolbaugh and Jill Heaton, for their interest in my project. Thank my other academic mentors, specifically Drs.
Brigette Martini, Greg Vaughan, Fred Kruse, John Louie, Bob Nelson, Don Allen, and
Steve Jacobsen who have provided advice throughout the various stages of my academic journey. Thanks to those who provided opportunities for me to see so much of Nevada and California, as well as Hawaii, Yellowstone, and Alaska! Thanks to my colleagues at
UNR, especially to Brad Cantor, Greg Rhodes, Jayne Bormann, Laura Huebner, Jeff
Shoffner, Todd Morken, Laura Garchar, and Amie Lamb who have been particularly helpful in guiding my efforts. Thanks also to my old friends who have listened to countless tales of thesis life and have graciously accepted my absence from the East
Coast. Thanks to Dan Pace who has been a loving supporter and necessary distraction.
Dan is also one of the best geologists I know, such a helpful feature in a boyfriend. Our impending trip to South America has certainly given me great incentive to finish my thesis in a timely manner. Thanks to my brothers, Robbie and Billy, who share my desire to use science as means to change the world; they inspire me daily and remind me to
strive for excellence. Finally, I thank my parents, Josh and Suzy, for teaching me to set
high standards, for their enthusiastic support, and for their incredible financial
contribution to my future. Much love. iv
Table of Contents
Chapter 1. General Introduction 1
Chapter 2. Location and Geology of Fish Lake Valley 6
2.1 Regional Geology 6
2.2 Fish Lake Valley Geology 11
2.3 Fish Lake Valley Geothermal Geology 18
2.3.1 Emigrant Prospect 21
2.3.2 Fish Lake Valley Prospect 25
Chapter 3. Infrared Spectroscopy and Remote Sensing Background 27
3.1 Electromagnetic Radiation 27
3.2 Infrared Spectroscopy 29
3.3 Electronic Processes 30
3.4 Vibrational Processes 33
3.5 Spectral Libraries 37
3.6 Remote Sensing 38
3.7 Previous Alteration Mapping Using Remote Sensing Data 40
Chapter 4. Instrumentation and Calibration 42
4.1 Remote Sensing Instrumentation 42
4.1.1 Multispectral 43
4.1.2 Hyperspectral 44
4.2 Calibration of Radiance Data 46
4.2.1 VNIR/SWIR Calibration 46
4.2.2 TIR Calibration 46
4.3 Field Measurements 47 v
Chapter 5. Data Processing 49
5.1 Statistical Approach 49
5.2 Decorrelation Stretch 50
5.3 Mineral Mapping 53
5.4 GIS Integration 55
Chapter 6. Results and Validation 56
6.1 MASTER 57
6.1.1 TIR 57
6.1.2 VNIR/SWIR 62
6.2 ProSpecTIR 66
6.3 AVIRIS 72
6.4 HyMap 81
6.5 FTIR Analysis of Opal 91
Chapter 7. Interpretation and Discussion 94
7.1 Mineral Map Synthesis and Interpretations 94
7.2 Volcanic Hills Target 98
7.3 Emigrant Hills Target 100
7.4 Fish Lake Valley Target 103
7.5 Silver Peak Range Target 109
7.6 Emigrant Prospect 111
Chapter 8. Summary and Conclusions 114
8.1 Fish Lake Valley Geothermal Prospects 114
8.2 Comparison of Remote Sensing Data Sets 116
8.3 Implications and Recommendations 119 vi
References 121
Appendix I. Field Site Locations 140
Appendix II. Location of Figures within the Text 143
Appendix III. ASD Field and Laboratory Spectra 144
vii
List of Tables
Chapter 3. Infrared Spectroscopy and Remote Sensing Background
3.1 Infrared wavelength regions 29
Chapter 4. Instrumentation and Calibration
4.1 Details for remote sensing instruments 45
viii
List of Figures
Chapter 1. General Introduction
1.1 Location of study area 3
Chapter 2. Location and Geology of Fish Lake Valley
2.1 Shaded relief map of the Great Basin 7
2.2 Map showing Walker Lane 8
2.3 Mina Deflection fault map 10
2.4 Geologic map of Fish Lake Valley 13
2.5 Location of geothermal prospects in Fish Lake Valley 19
2.6 Emigrant prospect alteration map 24
Chapter 3. Infrared Spectroscopy and Remote Sensing Background
3.1 Blackbody radiation curves 28
3.2 Absorption features caused by electronic transitions 31
3.3 Geothermal minerals spectra 35
3.4 Image cube 39
Chapter 4. Instrumentation and Calibration
4.1 Coverage of remote sensing data 43
4.2 Field verification sites 48
Chapter 5. Data Processing
5.1 MASTER false color composite and decorrelation stretch comparison 52
5.2 Illustration of additive color 53
Chapter 6. Results and Validation
6.1 Decorrelation stretch image of MASTER bands 48, 45, and 44 58
6.2 MASTER TIR DCS areas of interest 60 ix
6.3 MASTER emissivity spectra 62
6.4 MASTER reflectance spectra 63
6.5 MASTER VNIR/SWIR mineral map 65
6.6 ProSpecTIR reflectance spectra 67
6.7 ProSpecTIR mineral map 69
6.8 ProSpecTIR mineral map comparison 71
6.9 AVIRIS reflectance spectra 73
6.10 AVIRIS mineral map of Silver Peak Range 75
6.11 Photo from Site 31 77
6.12 AVIRIS mineral map of southern Volcanic Hills 79
6.13 Examples of sinter deposits 80
6.14 Examples of sinter textures 81
6.15 HyMap reflectance spectra 82
6.16 HyMap mineral map 84
6.17 Photo from Site 52 85
6.18 Reflectance spectra from Site 58 87
6.19 Photo from Site 28 90
6.20 FTIR spectra for samples from Sites 7, 19, and 42 93
Chapter 7. Interpretation and Discussion
7.1 Synthesized mineral map showing four target areas 97
7.2 Volcanic Hills target mineral map 99
7.3 Emigrant Hills target mineral map 102
7.4 Fish Lake Valley target mineral map 104
7.5 New Fish Lake Valley target area mineral map 107 x
7.6 Silver Peak Range target mineral map 110
7.7 Emigrant prospect mineral map 112
1
Chapter 1. General Introduction
A geothermal reservoir is a body of fractured or permeable rock heated by the
earth. A well drilled into a geothermal reservoir may bring hot water or steam to the
surface where it can be used to generate electricity in a power plant. Geothermal energy
is an attractive renewable source for electricity production in Nevada. As of 2009,
Nevada is home to 20 geothermal power plants that produce 433 MW of electricity, and
86 other projects in development [Jennejohn, 2010]. In addition to these projects, many
undiscovered geothermal systems are likely to exist in Nevada. The state has a
renewable energy portfolio standard of 25% by 2025 [Database of State Incentives for
Renewables & Efficiency, 2010], which means geothermal energy will continue to play
an important role in Nevada.
Geothermal systems in Nevada are unlike most geothermal systems, which require shallow magma bodies to heat the water. Most Nevada geothermal systems are extensional-type or amagmatic; the water is heated by deep circulation within the crust without the presence of upper crustal magma chambers. Crustal extension provides fault pathways which allow for the deep circulation and subsequent heating of meteoric water
[Wisian et al., 1999]. Faults may also act as conduits for ascending thermal water, which may result in hot springs or fumaroles, surface expressions of the geothermal systems.
Systems without such fluid features are termed “blind,” but many blind systems have more subtle surface expressions. These indicators may include siliceous sinter, travertine, or tufa deposits, and/or hydrothermally altered rocks. Playas above a geothermal system may display borate or sulfate crusts. Vegetation may serve as an 2
indicator as well, concentrating around faults leaking water, or suffering near faults
leaking toxic gasses.
Fish Lake Valley is located in Esmeralda County, Nevada along the California
border (Figure 1.1). The region has been selected for geothermal exploration due to high temperatures in drill holes, the presence of Quaternary borate deposits, and young
displacements along nearby faults. The northern part of the valley is a pull-apart basin opened where the right-lateral strike-slip Fish Lake Valley fault zone makes a right step into the central Walker Lane via the Emigrant Peak fault zone [Reheis and Dixon, 1996].
Two geothermal prospects exist in the Fish Lake Valley region, and are referred to as the
Fish Lake Valley and Emigrant prospects. The systems are not well understood and their spatial extents are relatively unconstrained. While commercial temperatures were reported in geothermal wells drilled at both the Fish Lake Valley and Emigrant prospects, neither project has been developed. Approximately 50 km of transmission lines would need to be built to connect Fish Lake Valley to the electric grid [Hulen et al., 2005a].
Other commercial geothermal systems identified in Fish Lake Valley would provide further incentive to build costly transmission lines. 3
Figure 1.1 The grey box indicates the location of the study area in northern Fish Lake Valley, Nevada.
Surface expressions of the geothermal systems in Fish Lake Valley are limited.
The Fish Lake valley prospect includes a cement tub of hot water piped from a deep
artesian well, but no natural hot springs or fumaroles. The prospect is also associated
with siliceous sinter deposits and some travertine. The Emigrant prospect includes a
sulfur deposit, small fumarole, argillic alteration near faults, limited silicification, and
some quartz and calcite veining [Hulen et al., 2005b]. Within each of the geothermal
prospects, there may be additional undiscovered hydrothermal alteration and/or
geothermal deposits. There may also be additional unrelated geothermal systems in the
Fish Lake Valley region. 4
Remote sensing may be used to remotely identify and map mineralogy based on spectral signatures of materials in the visible to thermal infrared region of the electromagnetic spectrum (0.4-12 µm). Hydrothermal alteration minerals are spectrally distinct and can be classified over very large areas. This method has previously been used to successfully identify and map surface expression of geothermal systems [e.g.
Kruse, 1999; Martini et al., 2003; Hellman and Ramsey, 2004; Martini et al., 2004;
Vaughan et al., 2005b; Kratt et al., 2006; Kratt et al., 2009]. While some studies have characterized the alteration and deposits of known geothermal systems, others have identified new systems. Remote sensing data are collected by instruments onboard satellites, or mounted on airplanes to attain higher spatial resolution. Airborne instruments were used to collect the spectral data over Fish Lake Valley at resolution varying from 2-11 m per pixel. Four instruments were used, each to collect data over a different part of Fish Lake Valley. The Airborne Visible/Infrared Imaging Spectrometer
(AVIRIS), ProSpecTIR, and HyMap instruments were used to collect high spatial and spectral resolution data in the visible to near infrared (0.4-2.5 µm). The MODIS/ASTER
(MASTER) instrument collected data at lower spatial and spectral resolution than the other instruments, but it collected data in the visible to near infrared and the thermal infrared (8-12 µm). All data were collected during daylight hours.
The purpose of this study was to:
1. Use the MASTER thermal infrared data to identify silica- and clay-rich
deposits which may represent geothermal deposits or hydrothermal alteration. 5
2. Use the MASTER, ProSpecTIR, AVIRIS, and HyMap visible to shortwave
infrared data to identify and map geothermal deposits and hydrothermal
alteration minerals.
3. Produce a mineral map of Fish Lake Valley by combining mapping results
with previously published geologic data in a Geographic Information System
(GIS).
4. Using mineral distribution, identify areas where fluid has been discharged
along faults and make interpretations about the geothermal systems.
5. Identify specific areas of Fish Lake Valley with hydrothermal alteration
minerals and/or geothermal deposits as targets for future exploration.
6. Compare the effectiveness of MASTER, ProSpecTIR, AVIRIS, and HyMap
data for geothermal exploration.
This thesis also provides an overview of previous studies of the tectonics, geologic history, and geothermal geology of Fish Lake Valley. This work should delineate areas to focus future geothermal exploration efforts and give background information about each area. The results of this research should encourage the use of remote sensing in preliminary reconnaissance exploration for geothermal systems in many other parts
Nevada. 6
Chapter 2. Location and Geology of Fish Lake Valley
2.1 Regional Geology
The Fish Lake Valley study area is located along the California-Nevada border in the Great Basin, an internally drained physiographic province (Figure 2.1). The Great
Basin is bounded by the Wasatch Mountains and Colorado Plateau to the east, the Sierra
Nevada Mountains to the west, and the Snake River Plain to the north. Cenozoic extension has resulted in the characteristic Basin and Range structure of tectonically down-dropped basins and uplifted mountains. Extension is generally east-west directed and is typically accommodated by high-angle normal faults and large low-angle detachment faults that have exposed metamorphic core complexes [Stewart, 1971].
Basin and Range extension began 17-14 Ma [Stewart, 1980; Miller et al., 1999; Surpless et al., 2002] as a result of interaction between the Pacific and North American plates
[Wernicke, 1992]. 7
Figure 2.1 Shaded relief map of western U.S. states showing the Great Basin in the red outline. [Coolbaugh, 2004; ESRI, 2009]
8
Figure 2.2 Regional map showing seismic hazard faults. The blue area represents the Walker Lane and the dashed outline shows the location of the Mina Deflection, and Figure 2.3 [modified from Oldow et al., 2001; faults from U.S. Geological Survey, 2006].
The late Cenozoic Walker Lane is located at the western edge of the Great Basin and is a 700 km-long zone of strike-slip faulting [Stewart, 1988; Wesnousky, 2005]
(Figure 2.2). The Walker Lane has more diverse topography than the rest of the Great
Basin, which has typical Basin and Range-style topography. The zone is characterized by northwest-trending right-lateral faults [Wesnousky, 2005], and accommodates ~25% 9 of the relative movement between the Pacific and North American plates [Oldow et al.,
2001]. The central Walker Lane is characterized by a belt of east-northeast-trending left- lateral faults known as the Mina deflection [Wetterauer, 1977] (Figure 2.2). The Mina deflection connects the northwest-striking faults of the central Walker Lane with the
Death Valley-Furnace Creek and Owens Valley fault systems, acting as a right step in a right-lateral fault system. Pull-apart basins are common to the Mina deflection; these basins form as a result of extension in the step between strike-slip faults. Northern Fish
Lake Valley is a pull-apart basin that was formed by the northward transfer of right- lateral displacement from the Fish Lake Valley fault zone. 10
Figure 2.3 Active faults in the central Walker Lane at the location of the Mina Deflection [after Wesnousky, 2005]. Bold arrows indicate direction of movement on faults; orange shaded area shows the location of Fish Lake Valley (Figure 2.4).
11
Extensional geothermal systems are common in the Great Basin because faults allow for deep circulation and heating of meteoric water and act as conduits for up- flowing hot water [Wisian et al., 1999]. Extensional geothermal systems are different from magmatic geothermal systems, which require magma to heat the water. Extensional systems are largely unique to the Great Basin, whereas magma-heated systems occur worldwide, including some locations in the Great Basin where they are related to young silicic volcanism [Arehart et al., 2003]. The Fish Lake Valley geothermal systems are classified as extensional; there is no known upper crustal magmatic heat source.
2.2 Fish Lake Valley Geology
Fish Lake Valley sits between the White Mountains to the west and the Silver
Peak Range to the east. The study area is located in northern Fish Lake Valley. Figure
2.4 shows a generalized geologic map of the study area and Plate 1 shows a more detailed geologic map. The northern White Mountains are composed predominantly of granitic plutons, partially overlain by Tertiary volcanic rocks [Albers and Stewart, 1972]. The
Silver Peak Range is composed of Cambrian and Ordovician metasedimentary rocks and overlain by Tertiary volcanic and sedimentary rocks [Albers and Stewart, 1972]. The
Cambrian rocks include the Poleta Formation (limestone, siltstone, and quartzite),
Harkless Formation (shale, phyllite, siltstone, and quartzite), Mule Spring Limestone, and
Emigrant Formation (limestone and shale) [Albers and Stewart, 1972; Hulen et al.,
2005b; Reheis and Block, 2007]. The Emigrant Formation has been thrust over the
Ordovician Palmetto Formation, which is locally a mélange of limestone and chert blocks
[Hulen et al., 2005b]. Tertiary rocks in the northern Silver Peak Range include basalt, 12 andesite, and rhyolite flows, silicic tuffs, and tuffaceous sedimentary rocks [Albers and
Stewart, 1972].
13
Figure 2.4 Simplified geologic map of Fish Lake Valley. EPFZ – Emigrant Peak fault zone; GMF – Green Monster fault zone; RFFZ – Range Front fault zone; FLVFZ – Fish Lake Valley fault zone [geology modified from Turner and Bawiec, 1996; faults from U.S. Geological Survey, 2006]. 14
The northwest-trending Fish Lake Valley fault zone (FLVFZ) marks the eastern
side of the White Mountains and represents the northern end of the 250 km-long Death
Valley-Furnace Creek fault system. The right-lateral FLVFZ is very active with a long-
term slip rate of 5 mm/yr since ca. 10 Ma [Reheis and Sawyer, 1997]. The fault zone
accommodates half the shear transferred from the Pacific-North American plate boundary
into the Basin and Range [Reheis and Sawyer, 1997]. The San Andreas fault system is
the only fault system in the U.S. that is more active than the FLVFZ. Fish Lake Valley is
bounded to the north by the east-trending Coaldale fault, a main structure of the Mina
deflection [Bradley et al., 2003]. The Coaldale fault is a left-lateral fault which experienced 60-80 km of movement before the middle Cretaceous [Stewart, 1985]. The fault has experienced local reactivation as recently as the Holocene [Bradley, 2005; Lee et al., 2009]. Cenozoic movement on the Coaldale fault is supported by offset drainages and ridges, although no scarps are observed [Bradley et al., 2003].
Beginning ca. 12 Ma, the Fish Lake Valley region was dominated by east-west extension, which resulted in uplift and eastward tilting of the White Mountains and the formation of a low-angle detachment fault system in the Silver Peak-Lone Mountain region [Oldow, 2002; Stockli et al., 2003]. Upper plate rocks slid northwest down the shallowly dipping décollement known as the Mineral Ridge detachment fault, leaving lower plate rocks exposed as the Silver Peak-Lone Mountain metamorphic core complex
[Petronis et al., 2002; Diamond and Ingersoll, 2002]. Paleozoic sedimentary rocks comprise the upper plate assemblage, and Cambrian and Ordovician metasedimentary rocks comprise the lower plate assemblage. Lower plate rocks are not exposed within the study area but are believed to exist at depth [Hulen et al., 2005b]. 15
At ca. 6 Ma, the FLVFZ began to experience right-lateral strike-slip faulting
[Stockli et al., 2003]. At approximately the same time, activity on the Silver Peak-Lone
Mountain detachment fault ceased [Stockli et al., 2003] and volcanic rocks were erupted from the Silver Peak volcanic center, located within the present-day Silver Peak Range
[Robinson, 1972]. Rhyolitic tuff and breccias were initially erupted, followed by andesite and latite, and finally ash-flow tuff [Robinson, 1972]. The volcanism was the result of a magmatic arc related to the subduction of the Juan de Fuca plate beneath western North
America [Robinson, 1972]. Also at ca. 6 Ma, normal faulting along the Emigrant Peak fault zone (EPFZ) resulted in the formation of the northern Fish Lake Valley pull-apart basin [Stockli et al., 2003]. The valley was formed as a result of a right step between two right-lateral fault zones, the FLVFZ and central Walker Lane.
Gravity data suggest the Fish Lake Valley pull-apart basin is approximately 1.5 km deep [Black and Stockli, 2006]. It is bounded to the east by the EPFZ, which includes the main Emigrant Peak normal fault and associated off-fault deformation. EPFZ normal faults occur in the upper plate above the Mineral Ridge detachment fault, with which they likely merge at depth [Oldow et al., 1994]. Ground penetrating radar and shallow seismic reflection data indicate complex off-fault deformation beneath recent alluvial deposits
[Christie, 2005]. Subsurface faults occur up to hundreds of meters from the main fault, and their dip angles increase from east to west, from 45-70°, potentially due to the westward movement of activity over time [Christie, 2005; Reheis and Sawyer, 1997].
Complex hanging wall structure may include colluvial wedges and antithetic faults that have formed small grabens [Christie, 2005]. The EPFZ Holocene vertical slip rate is 2.5-
4 mm/yr, which agrees with the high slip rate of the kinematically linked FLVFZ [Reheis 16
and Sawyer, 1997]. The large amount of movement on the FLVFZ and EPFZ likely
allows for increased fracture permeability in the active Fish Lake Valley pull-apart basin
[Hulen et al., 2005b]. Hulen et al. [2005b] discuss other nearby moderately- to steeply- dipping normal faults including the Green Monster, Gator, and Rangefront faults, which parallel the EPFZ, and the Sorrel fault zone, which trends northeast (Figure 2.4). Like the EPFZ, these faults are likely superimposed upon the Mineral Ridge detachment fault
[Oldow, 2002].
The Volcanic Hills are located in the north-central part of Fish Lake Valley.
These hills are relatively low-lying deposits of Tertiary basalt flows and rhyolite ash flow tuffs [Albers and Stewart, 1972]. Reheis et al. [1993] observed opaline silica, travertine, and siliceous root casts at the base of the southern Volcanic Hills; the deposits were likely derived from a hot spring environment. Reheis et al. [1993] proposed a relationship between the opal and spring water in northeastern Fish Lake Valley, which is relatively silica-enriched [Macke et al., 1990]. The 1:24,000 scale Fish Lake Valley surficial geology map by Reheis and Block [2007] indicates that the siliceous sinter and nearby travertine deposits occur along a fault. The deposits are the only sinter and travertine outcrops mapped in Fish Lake Valley. For this paper, the term “sinter” refers to siliceous hot spring deposits, and “travertine” to carbonate spring deposits deposited subaerially, as per White et al. [1964].
Pluvial Lake Rennie occupied Fish Lake Valley from before 2.0 Ma to ca. 0.5 Ma
[Reheis et al., 1993]. At ca. 0.77 Ma, The Gap likely connected Pluvial Lake Rennie to a lake in Columbus Salt Marsh [Reheis et al., 1993]. The Gap is a narrow pass between
Fish Lake Valley and Columbus Salt Marsh where there are several springs, including 17
one referred to as Gap Spring. During a visit to The Gap in August 2009, I observed abundant salt grass, evaporite deposits, wet ground, and a small cool pond. Gap Spring and a nearby unnamed spring have temperatures of 22°C and 23°C, respectively [Garside and Schilling, 1979]. As recently as in 1967, runoff from the White Mountains overflowed Fish Lake Valley and flowed northward through The Gap into Columbus Salt
Marsh [Beaty, 1968]. Currently, a large playa is present in northern Fish Lake Valley, which contains seasonally-dependent volumes of water. During the 1870’s Pacific Borax
Company ran a borax mining operation in this region; “cotton ball”-textured ulexite
(NaCa[B5O6(OH)6]•5H2O) was extracted from the east side of the playa. Ulexite also
occurs in the Silver Peak Range 5 km east of the playa, at the location of the Emigrant geothermal prospect [Papke, 1976] where it was last mined in 1939 [Albers and Stewart,
1972]. The boron deposit was estimated by U.S. Borax to be the second largest boron
deposit in the country [Deymonaz et al., 2008]. Borates can be indicative of geothermal
systems; they generally form when boron-rich water is evaporated [Coolbaugh et al.,
2006a]. Boron-rich water tends to come from deep in the Earth where thermal waters
have had the opportunity to dissolve boron from rocks [Coolbaugh et al., 2006a]. Water
at such depths is generally heated, resulting in the statistical correlation between high
boron concentrations and thermal springs in Nevada [Coolbaugh et al., 2002]. According
to Hulen et al. [2005b], U.S. Borax geologists who have studied Fish Lake Valley
hypothesize that the borax deposit is related to thermal springs.
18
2.3 Fish Lake Valley Geothermal Geology
Fish Lake Valley was identified as an area with high geothermal potential
according to evidence compiled by Coolbaugh et al. [2006b]. The U.S. Department of
Energy named Fish Lake Valley as a “top pick” for near-future geothermal development
based on lease type and resource potential as determined by Southern Methodist
University (SMU) [Farhar and Heimiller, 2003]. SMU mapped geothermal resource
potential using heat flow, thermal gradient, and sediment thickness data, as well as
location of hot springs and volcanoes [Richards and Blackwell, 2003]. Fish Lake
Valley’s geothermal potential was originally recognized in 1970 when high temperatures were reported in a deep oil exploration well drilled [Garside and Schilling, 1979]. In the
1980s AMAX Exploration, Inc. reported high temperatures in shallow boreholes drilled
for mineral exploration in the Silver Peak Range [Hulen et al., 2005b]. There are currently two geothermal prospects in Fish Lake Valley corresponding to each of these discoveries: the Fish Lake Valley and Emigrant prospects (Figure 2.5). 19
Figure 2.5 National Agriculture Imagery Program (NAIP) imagery over Fish Lake Valley. The general locations of the Fish Lake Valley and Emigrant geothermal prospects are indicated by the black outlines.
Martini et al. [2004] used the HyMap data from this study to map hydrothermal alteration minerals as a way to target future field work. Montmorillonite, kaolinite, jarosite, alunite, and pyrophyllite were mapped at the theoretical intersection of the EPFZ
and Coaldale fault [Martini et al., 2004]. The intersection theoretically occurs at the northern end of the EPFZ within the area labeled Emigrant Hills on Figure 2.4. Faulds et al. [2004] noted that many Nevada geothermal fields (e.g. Steamboat Springs, Kyle Hot
Springs, Leach Hot Springs, Jersey Valley Hot Springs, and Rye Patch) occur where two 20
major faults intersect. The intersection between the EPFZ and Coaldale fault may be similar, with increased permeability allowing geothermal systems to form. Martini et al.
[2004] observed that hydrothermal alteration mineral distribution was primarily
controlled by faults and contact boundaries. Field verification of the results was not
completed.
The ProSpecTIR data used for this study have previously been used to map sulfates and borates in Columbus Salt Marsh, where 2 m-deep temperature measurements
were also made [Kratt et al., 2009]. A temperature anomaly was identified directly up
hydrologic gradient from the sulfates and borates mapped in southwestern Columbus Salt
Marsh [Kratt et al., 2009]. Minerals were also mapped in the Volcanic Hills and
Emigrant Hills, and results agreed with maps by Martini et al. [2004]. Kaolinite,
chlorite, and some opal were mapped at fault intersections; upon field validation, the
authors found the alteration was not associated with any recent hydrothermal activity.
Kratt et al. [2009] used ProSpecTIR’s two commercial reflectance products, produced
using the ATCOR MODTRAN atmospheric correction program and the Virtual
Empirical-Line Calibration (VELC) procedure. There are problems with these data
products; the VELC data only span the 2.0-2.5 µm region and the ATCOR data are
overly smoothed rendering absorption features difficult to identify. For this study, a
different atmospheric correction was used to produce optimized reflectance data. While
the newly-derived reflectance data may allow for additional findings in Columbus Salt
Marsh, this study focuses only on the Volcanic Hills and Emigrant Hills.
21
2.3.1 Emigrant Prospect
The Emigrant geothermal prospect was first discovered in the 1980s when high temperatures were reported in shallow mineral exploration holes drilled by U.S. Borax for AMAX Exploration, Inc. Leases for the land in the northwest part of the Silver Peak
Range were held by Magma and then by Esmeralda Energy Company (EEC) of
Esmeralda Truckhaven, LLC., a wholly-owned subsidiary of Geo Energy Partners-1983.
GeothermEx, Inc. [2004] evaluated the geothermal potential of the Emigrant prospect and estimated a minimum electrical generation capacity of 49 MW. In 2004 EEC was awarded a Geothermal Resources Evaluation and Demonstration Program III grant
(GRED-III) for the Emigrant prospect. The grant was given by the U.S. Department of
Energy to support geothermal exploration of the prospect by means of mapping, drilling, and well-testing. The Emigrant prospect was mapped by Hulen et al. [2005b] using field observations and an ASTER scene fused with panchromatic data. The results of the project, known as the Emigrant Slimhole Drilling Project, were published in a technical report by Deymonaz et al. [2008].
Temperature readings from 44 U.S. Borax shallow boreholes and 13 AMAX shallow gradient holes indicate a thermal anomaly at the Emigrant prospect [Hulen et al.,
2005b] (Figure 2.6). The elongate anomaly is oriented in a northwest-southeast direction and spans major north-striking faults; Hulen et al. [2005b] hypothesized that deep northeast-striking faults (e.g. Sorrel fault zone) may connect some of the larger normal faults of the EPFZ, including the Range Front, Gator, and Green Monster faults (Figure
2.4). The en echelon steeply dipping normal faults within the Emigrant prospect have structural analogs at other Nevada geothermal fields (e.g. Brady’s, Desert Peak, and Salt 22
Wells), which are discussed by Faulds et al. [2004]. Hulen et al. [2005b] hypothesized that thermal water ascends along the Mineral Ridge detachment fault and Gator fault zone, and then moves further upward along the high angle normal faults, primarily at major fault intersections. The Green Monster fault zone appears to be the principal conduit for ascending thermal waters [Deymonaz et al., 2008]. In October 2006, slimhole
17-31 was drilled to 2938 ft where the temperature was 162°C, which is considered a commercial temperature. The well did not reach the geothermal reservoir, which is believed to exist below 2939 ft [Deymonaz et al., 2008]. The drilling location was chosen based on the geologic mapping and modeling done by Hulen et al. [2005b].
Drilling revealed an impermeable cap of sheared and brecciated Paleozoic rocks above the extensively fractured and hydrothermally altered metamorphic rocks from the lower plate beneath the Mineral Ridge detachment fault; the resulting model predicts an ideal hydrologic cap and geothermal reservoir [Deymonaz et al., 2008].
Surface expression of the geothermal system includes a native sulfur deposit, a small fumarole, warm ground, and some hydrothermal alteration [Hulen et al., 2005b].
Hulen et al. [2005b] generalize the alteration in a map that shows silicification, quartz and calcite veins, and pervasive argillic alteration near faults, some of which is overprinted on illite (clay-sericite alteration) (Figure 2.6). Specific observations of the alteration are discussed by Hulen et al. [2005b] and summarized here. The Palmetto
Formation contains localized occurrences of alteration and mineralization including deposits of barite and jasperoid, quartz veins, de-calcification, and quartz-sericite alteration, and igneous intrusions within the formation display propylitic alteration.
Hulen et al. [2005b] note that much of the alteration in the Palmetto Formation likely 23 occurred before the rocks were moved to their current location, but some of the alteration appears to be fault-controlled. Emigrant Formation carbonates have been altered by de- calcification forming dissolution openings in rocks adjacent to normal faults. Smectite minerals, commonly nontronite, are abundant in the tuffaceous sedimentary rocks.
Alluvium near the Green Monster fault has been altered to kaolinite, thernardite, and gypsum by low-temperature, low-pH fluids. Calcite veins are observed within the prospect area, including a large 5 m-thick vein near the hottest AMAX borehole. 24
Figure 2.6 Map showing alteration within the Emigrant prospect [from Hulen et al., 2005b]. 25
2.3.2 Fish Lake Valley Prospect
The Fish Lake Valley geothermal prospect covers the southern part of the
Volcanic Hills into the valley proper (Figure 2.5). The prospect was first discovered in
1970 when a deep oil exploration well drilled by Nevada Oils and Minerals revealed high
temperatures [Garside and Schilling, 1979]. Since then, 13 geothermal wells have been
drilled in Fish Lake Valley to define the resource [Davis and Hess, 2009]. AMAX
Exploration, Inc. drilled three holes in 1982 [Davis and Hess, 2009]. In the mid-1980’s
Steam Reserve Corporation drilled two holes; the first was an observation well with a
maximum temperature of 157°C reported at 442 ft depth [Edmiston and Benoit, 1984].
The second was a deeper well with temperatures over 200°C at 8152 ft depth [Martini et
al., 2004]. Fish Lake Power Company, owned by Magma Power Company, drilled eight holes between 1985 and 1993 to various depths [Davis and Hess, 2009]. Of the publically available temperature data, the hottest measurement was 204°C in an 8149 ft hole. GeothermEx, Inc. [2004] estimated a minimum electrical generation capacity of 30
MW for the Fish Lake Valley prospect, and reported that the spatial extent of the geothermal reservoir was unknown. Near the base of the Volcanic Hills is an artesian well with a small cement pool of hot water for recreational use, maintained by Esmeralda
County.
Reheis et al. [1993] described springs and a sinter mound located along a fault.
The sinter mound was deposited in a hot spring environment as suggested by opaline
silica layers and silicified root casts [Reheis et al., 1993]. The silica layers are similar to
those observed at Steamboat Springs, Nevada [White et al., 1964]. The sinter mound also
contains reworked Bishop ash and lapilli tuff, erupted from Long Valley caldera at 0.77 26
Ma, which suggests the mound was deposited at the edge of Pluvial Lake Rennie [Reheis
et al., 1993]. At several locations in Nevada, siliceous sinter occurs along faults, where
hot silica-saturated water moves upward and cools below 100°C (e.g. Beowawe geyser
area [Hose and Taylor, 1974], Bradys Hot Springs [Kratt et al., 2006], the Humboldt
House geothermal area [Johnson et al., 2003], northern Dixie Valley [Lutz et al., 2002], and Steamboat Springs [Lynne et al., 2008]).
27
Chapter 3. Infrared Spectroscopy and Remote Sensing Background
3.1 Electromagnetic Radiation
Electromagnetic (EM) radiation refers to energy-carrying waves that travel at the speed of light. The waves are a result of oscillating in-phase electric and magnetic fields that self-propagate. The fields are orthogonal to each other, and the direction of wave propagation is perpendicular to both. Photons are the quanta of energy transported by
EM radiation. EM waves have a specific wavelength and the EM spectrum is a representation of all possible wavelengths of radiation.
A blackbody is a conceptual object that absorbs all wavelengths of EM radiation, and then emits all radiation to maintain thermal equilibrium. The wavelengths at which radiation is emitted by blackbodies is a function of temperature, shown by Planck’s law
2 1 ′ , (3.1) 1 where ′ , is spectral radiance as a function of wavelength and temperature of the blackbody, h is the Planck constant, c is the speed of light, λ is wavelength, k is the
Boltzmann constant, and T is temperature of the blackbody [ Rybicki and Lightman ,
1979]. The Sun and Earth both emit radiation; they are the sources for the infrared radiation detected by remote sensing instruments. The Sun approximates a blackbody at
6000 K and the Earth approximates a blackbody at 300 K. Lower temperature blackbodies emit radiation at longer wavelengths than higher temperature blackbodies.
Planck’s Law (Equation 3.1) demonstrates that the Sun emits most radiation at visible wavelengths whereas the Earth emits radiation at thermal infrared wavelengths. Figure 28
3.1 shows Planck curves for the Sun and Earth, calculated using T equals 6000 and 300
K, respectively, for Equation 3.1.
Figure 3.1 Blackbody radiation curves for the Sun and Earth, which approximate 6000 K and 300 K blackbodies, respectively. Shading shows position of the visible, near and shortwave infrared (VNIR/SWIR) and thermal infrared (TIR) wavelength regions.
Emissivity is a measure of how well a mineral radiates thermal energy; it is the ratio of radiation emitted by an object ( Mr) to radiation emitted by a blackbody at the same temperature ( Mb) [ Jensen , 2000]:
(3.2)
Blackbodies have an emissivity of 1 for all wavelengths. Real objects ( Mr) have selective radiation, which means their emissivity changes with wavelength. Emissivity is 0 if the 29 object does not emit any radiation or 1 if the object acts as a blackbody. Emissivity may be any value between 0 and 1 for any wavelength.
Atmospheric windows are wavelength regions that are not strongly affected by atmospheric gasses (O 3, H 2O, O 2, CO 2, and N 2O), which absorb radiation [ Jensen , 2000].
Solar radiation detected by remote sensing instruments passes through the atmosphere en route to the Earth, and again after reflected by the surface. Terrestrial radiation passes through the atmosphere between the Earth’s surface and the detector. Geologic remote sensing studies use spectral data at infrared wavelengths within atmospheric windows.
The remote sensing community has divided the infrared region into the visible near infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) (Table 3.1).
This nomenclature will be used hereafter.
Region Wavelengths Visible near infrared (VNIR) 0.325-2.0 µm Shortwave infrared (SWIR) 2.0-2.5 µm Thermal infrared (TIR) 8-13 µm Table 3.1 Infrared wavelength regions.
3.2 Infrared Spectroscopy
Infrared spectroscopy is the study of how EM radiation interacts with matter as a function of infrared wavelengths. Minerals selectively absorb and reflect radiation at specific wavelengths due to electronic and vibrational processes [ Hunt , 1977]. Electronic processes occur in the VNIR, at shorter wavelengths than vibrational processes, which occur in the SWIR and TIR regions. Electronic processes include crystal field effects, charge transfer, color centers, and conduction band transitions [ Hunt , 1977]. Vibrational processes refer to the vibration of bonds within molecules. 30
3.3 Electronic Processes
Four types of electronic transitions result in the absorption features observed in the VNIR region. One type is related to an electrostatic field that surrounds transition metal ions in a solid. The electrostatic field is generated by a crystal field of negatively charged ions, or ligands. Electrons in unfilled outer orbitals of transition metal ions are strongly affected by crystal fields. The energy levels of these orbitals split when crystal fields surround the ions [ Hunt , 1977]. Electrons in the outer orbitals can then move into a higher energy state by absorbing the energy difference between the lower and higher energy levels [ Clark , 1999]. Spectral absorption features are dependent on the valence state, coordination number, and site symmetry of the transition metal ion; these factors determine the energy levels of the outer orbitals [Hunt , 1977]. In remote sensing studies, most crystal field-related absorption features are caused by iron-bearing minerals because iron is the most common terrestrial transition metal (Figure 3.2). 31
Figure 3.2 VNIR spectra for some minerals with absorption features caused by electronic transitions [modified from Vaughan , 2004]. Sulfur and cinnabar spectra exemplify a conduction band edge (blue); chlorite spectra exemplify charge transfer between Fe 2+ to Fe 3+ (red) and crystal fields affecting Fe 2+ ions (orange); hematite, goethite, and jarosite spectra exemplify absorption features caused by crystal fields around Fe 3+ ions (green); and hematite exemplifies an absorption feature resulting from charge transfer between Fe 3+ to O 2- (purple). Spectra are from the USGS spectral library.
32
Charge transfer causes some absorption features in the VNIR region. Electrons can move between adjacent ions when they absorb energy. Electrons may migrate from ligand to metal, metal to ligand, or between metal ions of the same element with different valence states [ Hunt , 1977]. A common example of electronic charge transfer in remote sensing studies is the redox reaction between Fe 2+ and Fe 3+ , which causes a broad absorption feature at blue and green wavelengths, rendering iron oxides and hydroxides red in color [ Clark , 1999].
Color centers are responsible for absorption features seen primarily in halide mineral spectra. Radiation excites electrons that can then fall into and become bound to unique energy levels produced by lattice defects [ Hunt , 1977]. Defects typically result from chemical impurities in the crystal. Fluorite displays absorption features related to color centers; these features can cause fluorite to be yellow, purple, or blue [ Hunt , 1977].
Color centers are not responsible for the absorption features of minerals identified by this work.
In some minerals, outer shell electrons exist in two distinct energy levels known as the conduction and valence bands. In the lower energy valence band, electrons are attached to the atom. In the higher energy conduction band, electrons have sufficient energy to move freely around the crystal lattice. The amount of energy between the valence and conduction bands is known as the forbidden gap because electrons may not occupy this energy region [ Hunt, 1977]. The width of the forbidden gap is different for metals, dielectrics, and semiconductors. Metals have a narrow forbidden gap and abundant conduction band electrons, whereas dielectrics have a wide forbidden gap as 33 valence electrons are kept tightly around the atom [ Hunt , 1977]. Semiconductors have an intermediate-width forbidden gap that can be seen in VNIR spectra as an absorption edge
[Hunt , 1977] (Figure 3.2). Absorption is the result of the conduction band and it ends abruptly at longer wavelengths where the spectrum shows maximum reflectance representing complete transmission through the forbidden gap [ Hunt , 1977].
3.4 Vibrational Processes
Molecular bonds naturally resonate at frequencies dependent on the strength of the bond and mass of the atoms [ Hunt , 1977]. A vibrating molecular system has simple motions known as normal modes. A molecular system with N atoms may have 3N-6 normal modes that each vibrate at a normal frequency. When energy is absorbed, normal modes are excited and produce additional vibrations known as overtones. Overtones occur at integer multiples of the normal frequency. When multiple normal modes or overtones occur, a combination tone occurs at the sum of their frequencies [ Hunt , 1977].
Overtones and combination tones result in spectral absorption features in the
SWIR, primarily for minerals containing OH. The metal bonded to OH, and the location of OH within the lattice, determine the wavelength of the associated absorption features
[Hunt , 1977]. Water, carbonate, borate, sulfate, and phosphate also display absorption features in the SWIR due to vibrational processes. SWIR features are generally diagnostic of mineralogy because they occur at predictable wavelengths. Many minerals with diagnostic SWIR absorption features are associated with geothermal systems, 34 including opal, gypsum, montmorillonite, kaolinite, muscovite, illite, pyrophyllite, alunite, jarosite, tincalconite, mirabilite, chlorite, and calcite (Figure 3.3). 35
Figure 3.3 Geothermal minerals with absorption features in the SWIR. Spectra are from the USGS spectral library. 36
Vibrations in silicate, carbonate, and sulfate minerals produce spectral absorption features in the TIR atmospheric window. Electronic transitions and vibrational processes appear as reflectance minima in the VNIR/SWIR, while vibrations cause emissivity minima in the TIR region [ Salisbury et al ., 1991]. Solar radiation is reflected at
VNIR/SWIR wavelengths, but in the TIR region, radiation detected by remote sensing instruments is emitted by the Earth. Emissivity minima correspond to reflectance maxima as stated by Kirchhoff’s Law:
1 (3.3) where ε is emissivity and R is reflectance [ Nicodemus , 1965]. The counterintuitive link between vibrations and reflectance maxima is due to a high absorption coefficient associated with the vibrational modes, which causes a “mirror-like opacity” that results in high surface reflectance at corresponding wavelengths [ Salisbury et al ., 1991].
The wavelength of emissivity minima associated with the Si-O vibration in silicates is dependent upon the degree of isolation of SiO 4 [Farmer , 1974]. The absorption band for Si-O shifts from 9 to 11 µm for tecto-, phyllo-, and cyclosilicates
[Kahle et al ., 1993]. Carbonates also display emissivity minima, the strongest of which occurs near 7 µm, just short of the TIR atmospheric window. A second feature occurs near 11.4 µm and varies in wavelength depending on the cation bonded to CO 3 (Ca, Mg,
Fe, Mn, Zn) [ White , 1974; Lane and Christensen , 1997]. This spectral variation allows for identification of mineralogy. Sulfates display emissivity minima resulting from vibrational modes of SO 4. Sulfate absorption features vary in position and number depending on the cation to which the SO 4 is bonded, and the presence of OH, H 2O, or 37
CO 2 [Ross , 1974; Lane , 2007]. The major sulfate absorption feature occurs between 8 and 9.5 µm; the feature occurs at longer wavelengths in less hydrated minerals [ Lane ,
2007].
3.5 Spectral Libraries
Publically available online spectral libraries are useful for identifying minerals based on their infrared spectra. A spectral library is a compilation of mineral spectra acquired using laboratory spectrometers. Mineral composition is typically identified by
X-ray diffraction (XRD) together with X-ray fluorescence (XRF), electron microprobe analysis, and/or inductively coupled plasma mass spectroscopy (ICP-MS) [ Clark et al .,
2007]. Several libraries with VNIR, SWIR, and TIR spectra are available, including U.S.
Geological Survey (USGS), Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER), and Reflectance Experiment Laboratory (RELAB) libraries. The
USGS spectral library is an extensive compilation of spectra from minerals, elements, soils, rocks, coatings, liquids, artificial materials, plants, and microorganisms [ Clark et al ., 2007]. The ASTER spectral library is a compilation of data from the Johns Hopkins
University (JHU), Jet Propulsion Laboratory (JPL), and USGS spectral libraries
[Baldridge et al ., 2008]. The RELAB spectral library was created by Brown University
(http://www.planetary.brown.edu/relabdocs/relab_disclaimer.htm). The University of
Arizona Thermal Emission Spectrometer research group has compiled a TIR spectral library [ Christensen et al ., 2000].
38
3.6 Remote Sensing
Remote sensing instruments are cameras or spectrometers mounted on satellites or airplanes to collect images of the ground. Sensors are receptive to radiation at certain wavelengths, thus a sensor may collect data for a multiple number of wavelength intervals or bands (spectral channels). Multispectral instruments collect data in several wavelength bands whereas hyperspectral instruments collect data in hundreds of wavelength bands (Table 3.1). Remote sensing instruments record infrared radiation that has left the Earth; the radiation was either emitted by the Earth or emitted by the Sun and then reflected by the Earth. For remote sensing image data, each pixel represents a known area of the ground. A data set is an image cube with two geographic dimensions and one spectral dimension (Figure 3.4). The remote sensing data used in this study were collected by the MASTER multispectral instrument, and the hyperspectral AVIRIS,
HyMap, and ProSpecTIR instruments. These instruments will be discussed in Chapter 4. 39
Figure 3.4 Image cube showing two spatial dimensions (x, y) and one spectral dimension (z). Each pixel in the scene has a spectral dimension (z) which is represented by a spectrum. 40
3.7 Previous Alteration Mapping Using Remote Sensing Data
Spectral data have been used to identify hydrothermal alteration in laboratory studies for almost 40 years [ Hunt and Salisbury , 1970, 1971; Hunt et al. , 1971a, 1971b;
Hunt , 1979]. More recently, hydrothermal alteration has been mapped using remotely sensed spectral data. VNIR/SWIR data collected by multispectral instruments have been used to map minerals and identify altered areas [ Rowan et al. 1974; Abrams et al ., 1977;
Kahle and Rowan , 1980]. Hyperspectral instruments have allowed for mapping of more specific alteration minerals. Kruse [1988] initiated this trend by using Airborne Imaging
Spectrometer (AIS) data to map alteration over the northern Grapevine Mountains in
Nevada and California. Subsequent studies have mapped hydrothermal alteration using data from aircraft and satellite sensors with a range of spatial and spectral resolutions. Of particular relevance to this work are previous studies over sites in California and Nevada where the topography and climate are similar to those of Fish Lake Valley.
The TIR wavelength region has been used since the 1980s to map minerals. Data from the airborne Thermal Infrared Multispectral Scanner (TIMS) were used to map silicates, carbonates, and clays [ Kahle and Goetz , 1983], and evaporite minerals [ Crowley and Hook , 1996]. Beginning in 2000, multispectral TIR data were collected by the spaceborne MODIS and ASTER instruments on board NASA’s Terra satellite. MODIS and ASTER data have been used to map a variety of materials but are not well suited for geothermal exploration because of their low spatial resolution. The airborne MASTER,
Airborne Hyperspectral Imager (AHI), and Spatially Enhanced Broadband Array
Spectrograph System (SEBASS) sensors collect TIR data at higher spatial resolution than the spaceborne instruments. SEBASS and AHI are considered hyperspectral instruments 41
[Hackwell et al. , 1996; Lucey et al ., 2003], whereas MASTER only collects TIR data in
10 bands [ Hook et al ., 2001].
MASTER data have been used to identify hot spring deposits and hydrothermal alteration in Nevada over the northern Grapevine Mountains, [ Kruse , 2000], Steamboat
Springs [ Vaughan et al ., 2005b], the Cuprite mining district [ Kruse , 2002], and Buffalo
Valley [ Littlefield and Calvin , 2009].
HyMap data have been used to map hydrothermal alteration over Long Valley
Caldera, California [ Martini et al. , 2003] and Dixie Valley, [ Martini et al. , 2003; Pal and
Nash , 2003; Kennedy-Bowdoin et al., 2004], Pyramid Lake [ Kratt et al ., 2005], and the
Brady-Desert Peak geothermal fields [ Kratt et al. , 2006]. Martini [2004] used the
HyMap data from this study to map alteration minerals in Fish Lake Valley.
AVIRIS data were used to map alteration mineralogy in northern Death Valley,
Nevada and California [Kruse et al ., 1993]. Baugh et al . [1998] mapped buddingtonite abundance in the southern Cedar Mountains, Nevada. Hot spring deposits and alteration minerals at Steamboat Springs, Nevada were mapped by Kruse [1999] and Vaughan
[2004]. Vaughan and Calvin [2006] also used AVIRIS data over Virginia City, Nevada to map hydrothermal alteration.
ProSpecTIR data have been collected over the well-studied areas of Cuprite and
Beatty, Nevada [ Aslett et al ., 2008] and processed for comparison to other remote sensing data. The ProSpecTIR data from this study have been used to map hydrothermal alteration at Columbus Salt Marsh, Nevada [ Kratt et al. , 2009]. 42
Chapter 4. Instrumentation and Calibration
4.1 Remote Sensing Instrumentation
Important attributes can be compared between remote sensing instruments. The signal-to-noise ratio (SNR) measures the strength of a signal compared to the strength of the noise in the data. SNR is equal to the mean signal level divided by the standard deviation of the signal fluctuations [Swayze et al., 2003]. Data with a high SNR show absorption features clearly, whereas data with low SNR have low contrast between spectral features and noise. The instantaneous field of view (IFOV) is the angle through which the instrument collects data at any moment. The sensor also scans the ground and collects data by rotating though the total field of view (TFOV) angle. Spectral resolution refers to the number of spectral channels for a given wavelength range. High spectral resolution allows for narrow absorption features to be identified in mineral spectra.
Spatial resolution refers to the amount of area represented by one image pixel. Spatial resolution is dependent on sensor altitude and the IFOV. High spatial resolution allows for the identification of small features on the ground. 43
Figure 4.1 Outlines showing the coverage of remote sensing data used in this study, overlain on NAIP imagery. Red is AVIRIS, purple is HyMap, blue is ProSpecTIR, and yellow is MASTER.
4.1.1 Multispectral
The MASTER airborne simulator was designed by the Ames Research Center and the Jet Propulsion Laboratory and simulates a combination of the Moderate Resolution 44
Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission
and Reflection Radiometer (ASTER). The MASTER instrument maintains the wide
spectral range of MODIS and ASTER; data are collected in 50 spectral channels in the
VNIR, SWIR, and TIR (0.45-12.88 µm). Of the 50 channels, 25 cover the VNIR/SWIR
and 10 cover the TIR region (Table 4.1). While spaceborne MODIS and ASTER are onboard the Terra satellite, MASTER was designed as an airborne instrument to achieve higher spatial resolution, rendering it more useful for mapping hydrothermal alteration minerals [Hook et al., 2001]. The MASTER instrument has an IFOV of 0.14° and a
TFOV of 85.92° [Hook et al., 2001]. For this study, MASTER data were acquired from a
King Air Beachcraft B200 at an altitude of 4.5 km above ground level (AGL). The data were collected over Fish Lake Valley on June 1, 2006 and have a spatial resolution of
11.3 m (Figure 4.1).
4.1.2 Hyperspectral
Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is operated by the Jet
Propulsion Laboratory and collects data in 224 channels from 0.4-2.5 µm. The data
typically have a very high SNR (Table 4.1). The AVIRIS instrument has an IFOV of
0.057° and a TFOV of 34° (http://aviris.jpl.nasa.gov/). AVIRIS data were collected over
Fish Lake Valley on 19 March 2003 (Figure 4.1). The data were acquired from a DHC-
6-200 Twin Otter at an altitude of ~5.05 km AGL. The data have a spatial resolution of 2
m. 45
Commercial HyMap data are a product of HyVista Corporation based in
Australia. HyMap data are collected in 126 channels from 0.45-2.5 µm and generally
have a high SNR (Table 4.1). The HyMap instrument has an IFOV of 0.057-0.17° and a
TFOV of 30-65° (http://www.intspec.com/products/HyMap/specifications/). HyMap data were acquired on 4 July 2003 over Fish Lake Valley (Figure 4.1). The data have a spatial resolution of 3 m.
ProSpecTIR data are a commercial product of SpecTIR Corporation based in
Reno, Nevada. Data are collected in 368 channels from 0.39-2.4 µm by the ProSpecTIR
VS2 sensor. ProSpecTIR data generally have a lower SNR than AVIRIS and HyMap
data (Table 4.1). The ProSpecTIR instrument has an IFOV of 0.075° and a TFOV of 24°
(http://www.spectir.com/DUAL.htm). ProSpecTIR data were collected on 29 October
2008 from an altitude of 4.7 km AGL and have a spatial resolution of 4 m (Figure 4.1).
The principal focus area for the data collection was Columbus Salt Marsh, north of Fish
Lake Valley.
Instrument Region # of Signal-to- Spatial Wavelengths Name Covered Channels Noise Ratio Resolution VNIR/SWIR 0.46-2.39 µm 25 ~450:1 MASTER 11 m TIR 7.76-12.88 µm 10 ~200:1 HyMap VNIR/SWIR 0.45-2.6 µm 126 >1000:1 3 m AVIRIS VNIR/SWIR 0.4-2.5 µm 224 ~1000:1 2 m ProSpecTIR VNIR/SWIR 0.45-2.45 µm 368 ~500:1 4 m Table 4.1 Details for remote sensing instruments used in this study.
46
4.2 Calibration of Radiance Data
4.2.1 VNIR/SWIR Calibration
The MASTER, AVIRIS, and PropSpecTIR data were supplied as an at-sensor radiance product. Moderate resolution atmospheric transmission (MODTRAN) is a program that models transmission of visible/infrared radiation through the atmosphere.
The ENVI MODTRAN-based atmospheric correction, Fast Line-of-Sight Atmospheric
Analysis of Spectral Hypercubes (FLAASH), was used on the VNIR/SWIR data.
FLAASH removes atmospheric effects and creates a surface reflectance image. After the atmospheric correction, the EFFORT spectral polishing algorithm [Boardman, 1998] was used on the ProSpecTIR data to smooth noisy spectra. HyMap data were supplied as a reflectance product created using ATREM atmospheric correction and EFFORT. Data were georeferenced using provided ground look-up tables (GLT).
After atmospheric correction and smoothing, hyperspectral reflectance data were field-calibrated using the technique of Clark et al. [2002]. Calibration was required to scale remote sensing reflectance data to surface reflectance values. A multiplier was calculated from spectral measurements over a ground calibration target. The calibration targets used in this study were large, bright playa areas with little vegetation cover.
4.2.2 TIR Calibration
Winvicar was used to process the MASTER TIR data (bands 41-50)
(http://winvicar.jpl.nasa.gov/). The at-sensor TIR radiance data were corrected to at-
surface radiance using a MODTRAN model [Berk et al., 1989] in the Winvicar 47
MASTERTIR program. The input MODTRAN model included atmospheric data for the
date and time of collection. The Winvicar TES program was used to calculate surface
temperature and emissivity from at-surface radiance for bands 42-49. Since bands 41 and
50 displayed remnant atmospheric absorption they were excluded from the temperature- emissivity separation. MASTER temperature images were evaluated for thermal anomalies, however correction for aspect, elevation, and albedo would be necessary to derive trusted results as the data were collected during the day.
4.3 Field Measurements
An Analytical Spectral Devices, Inc. (ASD) portable FieldSpec Pro was used to collect spectra from surface materials in the field and laboratory samples. The spectrometer collects very high resolution data in 2151 spectral channels from 0.35-2.5
µm (VNIR/SWIR). For laboratory work, a halogen light source was used. In the field, the spectrometer was carried in a backpack and a handheld optic probe was connected to the instrument by a fiber optic cable. Reflected sunlight was measured in the field. A white halon plate was used as a reflectance standard to calibrate the instrument in both the field and laboratory. Mineralogy was identified from the spectra with reference to spectral libraries. Field sites were chosen to validate what was mapped using remote sensing data. Field validation therefore increases the confidence in mineral maps. Figure
4.2 shows the location of the 70 field sites and Appendix I gives details for each. 48
Figure 4.2 NAIP imagery of Fish Lake Valley showing locations of field sites where mapping results were verified.
49
Chapter 5. Data Processing
5.1 Statistical Approach
A statistics-based approach was used to identify spectral endmembers from the at- surface reflectance data. To remove noise from the data, a Minimum Noise Fraction
(MNF) transformation was performed [Green et al., 1988; Boardman and Kruse, 1994].
An MNF linear transformation consists of two principal component rotations. The first rotation produces noise-whitened data by removing band-to-band correlation and scaling the noise [Boardman, 1993; Boardman and Kruse, 1994]. A subsequent rotation of the noise-whitened data produces a set of images that are increasingly noisy with decreasing eigenvalues [Boardman and Kruse, 1994]. Noisy images display salt-and-pepper patterning and no recognizable geographic information. Coherent images with lower band numbers are used for further statistical processing and noisy images are discarded.
An MNF transformation reduces the number of spectral dimensions and speeds later calculations. In this study, an MNF transformation was performed on 224 bands of
AVIRIS data, which produced 50 bands containing data and 174 bands dominated by noise. The first 50 MNF bands were used for subsequent processing. Of the 348
ProSpecTIR bands, and 126 HyMap bands, 70 and 50 MNF bands, respectively, were used in further processing. Of the 25 VNIR/SWIR MASTER bands, 23 MNF bands contained data.
Coherent MNF bands were used in the ENVI Pixel Purity Index (PPI) calculation to identify spectrally unique pixels, or spectral endmembers. PPI calculations work best on MNF data because of the reduced dimensionality and noise [Boardman, 1994]. The 50
PPI calculation, originally developed by Boardman et al. [1994], systematically projects the data onto 10,000 random unit vectors, or skewers [Chaudhry et al., 2006]. For each projection of the data, pixels in extreme positions are recorded as pure. The output PPI image displays pure pixels above a user-defined threshold as white, and impure pixels as black. In the PPI image, each pixel is associated with a Digital Number (DN) that represents the number of times it was recorded as extreme.
MNF pixels with PPI DNs above a user-defined minimum threshold were selected using the ENVI Region of Interest (ROI) tool. These pixels were then viewed using the
ENVI n-D Visualizer tool, which allows the user to visualize the rotating pure pixel data cloud in n-dimensions. Endmember classes were defined by selecting pixels furthest from the main data cloud. The classes were exported as ROIs and their reflectance spectra were examined to determine mineralogy. Relevant classes were retained as endmembers for subsequent classification efforts.
5.2 Decorrelation Stretch
An analyst-biased approach was used to identify ROIs based on techniques that visually highlight spectral differences. A color composite image is an image where three chosen bands are displayed as red, green, and blue (RGB). Color composite images tend to be monochromatic and dull, so a decorrelation stretch (DCS) is used to produce an image that is more useful to the interpreter. A DCS removes correlation between three input bands to produce a highly saturated color image [Gillespie et al., 1986]. First, a new set of 3D axes are defined with their origin in the center of the data cloud. Data are 51 stretched along the new axes so the data cloud more fully occupies the 3D volume. The data are then retransformed to the original axes to be viewed as a more color saturated image [Gillespie et al., 1986].
Bands surrounding absorption features of interest were chosen to be displayed in
DCS images so that certain colors would display specific minerals or mineral groups.
DCS images were more useful than false color composites as the latter show little color variation due to high band-to-band correlation (Figure 5.1). 52
Figure 5.1 (A) A false color composite of HyMap bands 105, 108, and 110 (2.16, 2.21, and 2.24 µm) as RGB shows little variation because the bands are correlated. (B) A DCS of the same bands shown as RGB is more colorful and highlights reflectance (compositional) differences. In this image, blue is kaolinite, magenta is muscovite, red is muscovite+clinochlore, yellow is calcite, orange is opal, and green is silicic ash flow tuff.
For an example of how a DCS can highlight a specific mineral, in the HyMap data, kaolinite has an absorption doublet with reflectance minima near 105 and 108, and a maximum near band 110. A DCS of bands 105, 108, and 110 displayed as RGB shows kaolinite in blue (Figure 5.1). A color composite of the same bands shows only the 53 strongest kaolinite signatures in blue. In addition, muscovite is shown as magenta in the
DCS image (Figure 5.1). The relative reflectance highs in bands 105 and 110 (red and blue) for muscovite add to magenta in the additive color model (Figure 5.2). For each scene, a series of DCS images were produced. DCS images were viewed while linked to a true color image with an open z-profile window so that the interpreter could do a first- cut analysis of mineral distribution based on DCS colors and a quick identification of associated spectra.
Figure 5.2 Illustration of additive color. Primary colors are red, blue, and green, which add to form cyan, yellow, magenta, and white.
5.3 Mineral Mapping
Endmember ROIs were created using the statistical and DCS approaches to data processing. ROIs typically contained tens of pixels with nearly identical spectra.
Endmember ROIs were used as input reference spectra for the ENVI Matched Filtering
(MF) method [Boardman et al., 1995]. This method produces one image for each input reference spectrum. Each output image is a grayscale representation of the degree of similarity to the reference spectrum for every pixel. Bright pixels with high DNs are better matches to the reference spectrum than dark pixels with low DNs. 54
For each MF image, a minimum threshold was applied to select pixels with high
DNs. Selected pixels were grouped together in an ROI that could be overlain on any
image. The minimum threshold value was chosen with extreme care. A high threshold
resulted in a conservative selection where pixels that should have been selected were not.
A low threshold resulted in selection of pixels that were poor matches to the reference spectrum. Determining the correct threshold was an iterative process whereby a liberal
threshold was used and spectra of the selected pixels were examined for their similarity to
the reference spectrum. A slightly more conservative threshold was then applied, and the
process repeated. Selected pixels were also compared with DCS images to ensure that an appropriate threshold was applied. When an appropriate threshold was applied, spectra of selected pixels closely matched the reference spectrum. A close match included pixels with the spectral shape for the diagnostic absorption feature, but reflectance values ranging from approximately 35-170% of reference spectrum values. This wide range in reflectance values allowed for pixels with the same absorption features to be mapped as the same mineral, despite their brightness. Approximately 50-75% of the additional pixels identified using the thresholding technique had lower reflectance values and
therefore more subtle absorption features than the reference spectra. Mineralogy was
assigned to each ROI based on the comparison of scene spectra to library spectra.
Minerals were displayed using different colored ROIs; the ROIs were used to create a
classification map where each color represents a different surface mineral.
55
5.4 GIS Integration
ArcGIS was used to interpret relationships between mineralogy, geology, and
topography. Shapefiles from a surficial geologic map of Fish Lake Valley [Reheis and
Block, 2007] were provided by the USGS. A shapefile for the geologic map of Nevada
[Stewart and Carlson, 1978], and National Agriculture Imagery Program (NAIP) aerial
imagery were downloaded from the Keck Library website
(http://keck.library.unr.edu/datawarehouse.html). Shapefiles for wells and faults were downloaded from the Great Basin Center for Geothermal Energy website
(http://www.unr.edu/Geothermal/datalist.html). Dr. Greg Nash provided shapefiles for the geology and alteration within the Emigrant prospect [Hulen et al., 2005b]. A 10 m
DEM was downloaded from the University of Utah GeoSpatial Data Systems website
(http://www5.egi.utah.edu/index.html). Shapefiles for mineral maps were produced and viewed in ArcMap with other data. While all remote sensing data and results were georeferenced in ENVI using provided geographic information, some of the data was re- georeferenced by hand so that features from all layers were aligned in ArcMap.
56
Chapter 6. Results and Validation
The mineral maps produced for this study were viewed in ArcGIS with a 10 m
DEM from the University of Utah GeoSpatial Data Systems website
(http://www5.egi.utah.edu/index.html). Imagery from the National Agriculture Imagery
Program (NAIP) (http://keck.library.unr.edu/data/naips/naips.htm) was used in ArcGIS for reference. The locations of all field sites were acquired using a GPS receiver in the field and were added to the GIS project. Field location details are given in Appendix I.
Mineral maps produced from each remote sensing data set over Fish Lake Valley will be discussed in this chapter. The mineral maps for selected areas of interest are shown and consist of remotely mapped mineralogy overlain on NAIP imagery. The locations of selected figures are shown in Appendix II. Remotely mapped mineralogy is shown in different colors, which are identified in the legend. Field visits were made to important outcrops; the validation of remotely mapped mineralogy is also presented in this chapter. Validation was performed using the ASD spectrometer to collect spectra of field samples. To validate remotely mapped mineralogy, field spectra must have matched the image spectra for that location. Image spectra are shown compared with USGS library spectra and, if available, field spectra collected using the ASD. Appendix III shows ASD spectra with USGS library spectra for every field location. Remote sensing does not require validation of every pixel of remotely mapped mineralogy but does necessitate sufficient field work to instill confidence in the mineralogy of unvisited locations with spectra similar to those of validated outcrops. The goal for this field work was to validate every remotely mapped mineral at least once and focus on areas with 57 potential geothermal influence, specifically areas with opal, calcite, alunite, and kaolinite.
These are the remotely mapped minerals most likely to indicate a geothermal system; their geothermal relevance will be discussed in Chapter 7.
6.1 MASTER
6.1.1. TIR
Decorrelation stretch (DCS) images were produced to exploit emissivity differences in the MASTER data and highlight potential regions of siliceous sinter and clay-alteration. Silica- and clay-rich deposits show relative emissivity lows near 9.07 µm
(band 44) and 9.71 µm (band 45), respectively. A DCS of bands 48, 45, and 44 (11.30,
9.71, and 9.07 µm, respectively) displayed as RGB highlights silica- and clay-rich regions as yellow and magenta, respectively [Vaughan et al., 2005b] (Figure 6.1).
Vaughan et al. [2005b] used this MASTER DCS band combination to identify the extent of siliceous sinter at Steamboat Springs, Nevada. Pixels with an emissivity low in band
44 (blue) and relative highs in bands 48 and 45 (red and green, respectively) are shown in yellow (red + green) and should represent silica-rich minerals. Pixels with an emissivity low in band 45 (green) and relative highs in bands 48 and 44 (red and blue, respectively) are shown in magenta (red + blue) and should represent clay minerals. Other surface materials are displayed as various colors in the DCS image. The Fish Lake Valley playa is displayed as green and blue. Alluvium is displayed as magenta, yellow, green, or blue depending on the nearby bedrock. Tuffs and siliciclastic rocks generally appear magenta and carbonates are yellow, cyan, and green. 58
Figure 6.1 DCS of MASTER bands 48, 45, and 44 (11.30, 9.71, and 9.07 µm) displayed as RGB. Yellow represents silica-rich deposits and magenta represents clay-rich deposits. MASTER spatial resolution is 11.3 m. 59
While the DCS of MASTER bands 48, 45, and 44 has the potential to highlight
areas with siliceous sinter or argillic alteration, no such areas were immediately identified
in Fish Lake Valley using this approach. Upon comparison with the HyMap mineral map
(discussed in Section 6.4), it was apparent that the MASTER TIR DCS image does show
siliceous sinter near the northwestern part of the playa as yellow (Figure 6.2). Before
reference to the hyperspectral data, the sinter deposit was overlooked in the MASTER image because siliciclastic sedimentary rocks and some alluvium are also shown in yellow and comprise much of the MASTER scenes. Vaughan [2004] notes that spectra
of pixels mapped in yellow may represent quartz, alunite, or opal and specific mineralogy
cannot be determined due to low spectral resolution. Upon comparison with the
Emigrant prospect alteration map [Hulen et al., 2005b] it is apparent that silicification is generally shown as yellow in the TIR MASTER DCS image (Figure 6.2). Some silicification within the Emigrant prospect is not shown as yellow in the DCS image, and is instead shown as purple. This may be due to the presence of additional clay minerals. 60
Figure 6.2 MASTER DCS bands 48, 45, and 44 (11.30, 9.71, and 9.07 µm) displayed as red, green, and blue, respectively. (A) Emigrant prospect; black outlines show silicification mapped by Hulen et al. [2005b]. Some silicification is shown as yellow in the DCS image. (B) Northwest part of playa; black outlines show opal mapped using HyMap data, white outlines show calcite mapped using HyMap data. Opal is generally displayed as yellow-orange and calcite is generally cyan in the DCS image. Location of the image is shown in Appendix II.
Magenta should indicate clay-rich deposits and in some areas of Fish Lake
Valley, it does correlate with kaolinite, montmorillonite, and muscovite mapped using hyperspectral data. Magenta also highlights some alluvium and silicic ash flow tuffs, likely due to the presence of clays as weathering products. Not all clay deposits mapped using hyperspectral data are shown as magenta in the TIR MASTER DCS image. 61
Hydrothermal clay minerals are not effectively identified using the MASTER DCS image.
In the TIR DCS image, calcite is displayed as blue-cyan due to an emissivity low near 11.30 µm; travertine deposits northwest of the playa are shown in cyan (Figure 6.2).
In addition, cyan is also shown in the Silver Peak Range where it correlates with calcite mapped using the HyMap data in this study (Section 6.4), within carbonate sedimentary rocks. The emissivity low near 11.30 µm in the MASTER spectra is often accompanied by another low near 9.71 µm, likely due to the presence of both calcite and clay minerals within the 11 m pixels of MASTER data. The mineral map produced using endmembers derived from the DCS image was not useful because many pixels were misclassified due to low spectral resolution. Figure 6.3 compares image emissivity spectra with spectra from the JPL spectral library.
62
Figure 6.3 MASTER emissivity spectra compared with library spectra. Blue spectra are from the MASTER data, thick red spectra are JPL library spectra convolved to MASTER wavelengths, and thin red spectra are high resolution JPL library spectra.
6.1.2 VNIR/SWIR
Kaolinite, muscovite, and calcite were identified using MASTER VNIR/SWIR data. Figure 6.4 compares image reflectance spectra with spectra from the USGS spectral library. Low spectral and spatial resolution did not allow for mapping of minerals which may comprise deposits smaller than the pixel size of 11 m or have narrow absorption features not detectable by the MASTER instrument. While the MASTER data cover large areas of the Fish Lake Valley region, kaolinite, muscovite, and calcite were primarily mapped in the southern part of the western scene, over the Silver Peak Range
(Figure 6.5). This is an area of complicated faulting due to a large bend in the Emigrant
Peak fault. The rest of the scenes are dominated by spectra of unidentifiable mineralogy.
Some of the spectra may be noisy while most represent spectral mixing of multiple 63
minerals within an 11 m pixel. The low spectral resolution prevents accurate spectral
unmixing.
Figure 6.4 MASTER reflectance spectra compared with library spectra. Blue spectra are from the MASTER data, thick red spectra are USGS library spectra convolved to MASTER wavelengths, and thin red spectra are high resolution USGS library spectra.
The MASTER calcite reflectance spectrum has an absorption feature at 2.32 µm,
in band 24. MASTER spectra generally show a false high in band 25, which can make many pixels appear to have the carbonate absorption feature. For this study, only spectra with relatively deep absorption features at 2.32 µm were identified as calcite. This conservative mapping of calcite may have excluded other carbonate pixels with weaker absorption features. Calcite mapped using MASTER reflectance data is confined to the massive limestone of the Cambrian Poleta Formation. Kaolinite was also mapped using
MASTER data. The kaolinite spectrum is characterized by an absorption doublet at 2.16 64 and 2.22 µm which is subtle in MASTER spectra (Figure 6.4). According to the geologic map [Robinson et al., 1976], kaolinite is mostly distributed throughout Tertiary rhyolite ash flow tuff and a Tertiary sedimenatry unit of tuffaceous sandstone and conglomerate, and less often within Tertiary andesite. Abundant kaolinite occurs along a contact between Tertiary rhyolite ash flow tuff and andsite. Kaolinite and some muscovite were also distributed along a mapped fault. Muscovite is identified by the absorption feature at
2.2 µm. Muscovite was mapped within the rhyolite ash flow tuff, which was confirmed in the field at Site 62. Figure 6.5 shows the section of MASTER mineral map where the most minerals were mapped. 65
Figure 6.5 Distribution of minerals mapped using MASTER VNIR/SWIR data, overlain on NAIP imagery. Black dots show field locations. Location of the map is shown in Appendix II.
In some studies MASTER TIR data are used for geologic mapping and the
VNIR/SWIR data are disregarded. MASTER TIR data are generally used to map rock types [e.g. Hook et al., 2005; Vaughan et al., 2005a, 2005b]. A few authors have used both MASTER VNIR/SWIR and TIR data. Dmochowski [2005] used MASTER 66
VNIR/SWIR data to map surface materials and TIR data to map silica abundance. Kruse
[2002] used MASTER SWIR and TIR data to map minerals related to hot spring activity:
carbonate, kaolinite, alunite, buddingtonite, muscovite, and hydrothermal silica.
However, Kruse [2002] used AVIRIS data to guide mapping efforts and noted that
MASTER SWIR spectra are often confusing, especially when mixed. The absence of alunite, buddingtonite, hydrothermal silica, and mixtures of hydrothermal minerals from the VNIR/SWIR mineral map over Fish Lake Valley does not necessarily preclude their presence but they are not detectable using MASTER data alone
6.2 ProSpecTIR
Abundant kaolinite, muscovite, and calcite were mapped using ProSpecTIR data over the Emigrant Hills. Figure 6.6 shows average reflectance spectra for mapped
minerals compared with library spectra. Calcite was mapped using the 2.33 µm
carbonate absorption feature. Some calcite is correlated with the Cambrian Harkless
Formation, but the majority of calcite is not correlated with any carbonate unit mapped by Robinson et al. [1976]. Field work validated the presence of calcite around Site 69
(Figure 6.7). The deposit consists of banded crystalline calcite which represents a large travertine unit that has not been identified before. Samples display a gentle bumpy surface that suggests deposition above an algal mat. Robinson et al. [1976] mapped the unit as a sandstone and conglomerate, consistent with the Esmeralda Formation mapped by Stewart [1989] at the same location. 67
Figure 6.6 ProSpecTIR spectra for remotely mapped minerals compared with USGS library spectra and some ASD spectra.
Within the Emigrant Hills, muscovite was remotely mapped within the Palmetto
Formation along faults. Minor amounts of montmorillonite and jarosite were mapped in 68
the region. The ProSpecTIR montmorillonite spectrum shows a shallow absorption
feature at 2.2 µm (Figure 6.6). The ProSpecTIR jarosite spectrum shows a shallow
absorption feature at 2.26 µm and a slight feature near 2.21 µm.
Kaolinite was identified by the absorption doublet at 2.16 and 2.2 µm. The
kaolinite map by Martini et al. [2004] produced using HyMap data agrees with the
ProSpecTIR mineral map from this study. As noted by Martini et al. [2004], kaolinite is
distributed along the mapped faults and contacts between Tertiary rhyolite ash flow tuff and the Palmetto Formation. Kaolinite alteration of the rhyolite ash flow tuff was verified in the field at sites 65, 66, 67, 68, and 70 along the edge of the tuff unit (Figure
6.7). Some kaolinite and muscovite pixels are mapped in linear trends which may
represent fluid leaking along faults. 69
Figure 6.7 ProSpecTIR-derived mineral mapping results overlain on NAIP imagery. Black dots show field locations. Location of the map is shown in Appendix II.
The ProSpecTIR data over the Volcanic Hills, west of the area shown in Figure
6.7, were not processed for this study. All of the data were used by Kratt et al. [2009] to map hydrothermal minerals. Kratt et al. used reflectance data provided by SpecTIR whereas this study used reflectance data produced using the FLAASH atmospheric correction. The ProSpecTIR mapping results from this study are very similar to the results from Kratt et al. [2009] despite the difference in atmospheric correction (Figure
6.8). The similarity between the maps generates confidence in the results of both studies.
It was considered unnecessary to process the remainder of the ProSpecTIR data over the 70
Volcanic Hills because Kratt et al. [2009] had already done so and the results would likely be the same.
The results from Kratt et al. [2009] were used to compare different atmospheric corrections and mapping techniques. Not only were different reflectance data used for this study, but the data were also processed differently. Kratt et al. [2009] combined 18
ProSpecTIR flightlines of data into one file whereas each scene was processed individually for this study. Different processing approaches likely resulted in differences in the mineral maps. The principal difference between the map by Kratt et al. [2009] and the map produced for this study is kaolinite abundance (Figure 6.8). Some of the pixels mapped as kaolinite by Kratt et al. [2009] but not recognized as kaolinite in the
ProSpecTIR results of this study may be false positives while other such pixels were also classified as kaolinite using HyMap data in this study. This consistency between kaolinite mapped by Kratt et al. and the HyMap data (Section 6.4) validates results from
Kratt et al. [2009] and suggests the ProSpecTIR FLAASH-derived reflectance data were not ideal. Kratt et al. [2009] also mapped minor mirabalite and opal with the
ProSpecTIR data, which were not detected in this study. 71
Figure 6.8 Comparison between ProSpecTIR minerals maps produced by Kratt et al. [2009] (A) and this study (B), overlain on NAIP imagery. 72
6.3 AVIRIS
AVIRIS data were used to map calcite, opal, montmorillonite, kaolinite,
muscovite, and a combination of muscovite and clinochlore. Figure 6.9 shows AVIRIS
spectra for remotely mapped minerals, compared with USGS library spectra and some
spectra collected from field samples using the ASD. Minerals were primarily distributed
throughout the Silver Peak Range and the Volcanic Hills. The AVIRIS mineral map for
the Silver Peak Range (Figure 6.10) is similar to results from the MASTER VNIR/SWIR
data (Figure 6.5). Within the Silver Peak Range, calcite is correlated with limestone of
the Cambrian Poleta Formation [Krauskopf, 1971; Robinson and Crowder, 1973;
Robinson et al., 1976]. At the southern end of the Volcanic Hills, calcite represents
travertine and was verified at Sites 38 and 39 (Figure 6.12). Calcite was remotely mapped along a fault near the main sinter apron discussed by Reheis et al. [1993] and a spring, and correlates with travertine mapped by Reheis and Block [2007]. All calcite pixels show a carbonate absorption feature at 2.33 µm.
73
Figure 6.9 AVIRIS spectra for remotely mapped minerals, compared with USGS library spectra and some ASD spectra convolved to AVIRIS wavelengths.
AVIRIS data were used to identify muscovite and a combination of muscovite and clinochlore. Muscovite spectra show absorption features at 2.2 and 2.35 µm. The 74
muscovite+clinochlore combination shows an additional feature at 2.26 µm. As confirmed in the field at Site 12 (Figure 6.7), distribution of both muscovite and the
muscovite+clinochlore combination correlates with outcrops of the Cambrian Harkless
Formation, a green phyllitic siltstone [Krauskopf, 1971; Robinson and Crowder, 1973;
Robinson et al., 1976]. The Harkless Formation is composed of silt-sized quartz grains in a matrix of chlorite and muscovite [Albers and Stewart, 1972]. All rocks in the formation
are at least slightly metamorphosed [Albers and Stewart, 1972] so clinochlore is
attributed to low-grade metamorphism unrelated to recent geothermal activity. Rowan et
al. [1998] also mapped the Harkless Formation as a muscovite+chlorite mix using remote
sensing data over Cuprite, Nevada. Muscovite is also mapped in the Silver Peak Range,
likely where the Harkless Formation is less metamorphosed. The USGS spectral library
contains a reference muscovite spectrum from a relatively unaltered sample of Harkless
Formation at Cuprite [Clark et al., 2007] similar to the AVIRIS muscovite spectrum
(Figure 6.9). Muscovite was observed at Sites 62, 63, and 64 (Figure 6.7) within the
Silver Peak Range where it occurs within the light grey and brownish-grey sandstone
described as Ts3 by Robinson et al. [1976]. 75
Figure 6.10 Distribution of minerals mapped using AVIRIS data, overlain on NAIP imagery. Field Black dots show field locations. Location of the map is the same as that for Figure 6.5, and is shown in Appendix II.
At the southern edge of the Volcanic Hills, kaolinite was remotely mapped
adjacent to the drill pad for Fish Lake Power Company’s wells 88-11 and 88-11A near
Site 31 (Figure 6.12). At this location, kaolinite was remotely mapped within rhyolite
tuff mapped by Robinson and Crowder [1973]. This is in the vicinity of the Riek 76
property where there is a tuff-hosted mercury deposit with associated chalcedony, sulfur,
gypsum, and cinnabar [Bailey and Phoenix, 1944]. The mercury deposit was formed by
hot springs at or close to the surface [Bailey and Phoenix, 1944]. A field visit verified
the remote mapping of kaolinite at this location which appears to be a large exploration
trench with prospect pits and smaller trenches nearby. Chalcedony, sulfur, cinnabar, and
alunite were also observed. Figure 6.11 shows the distribution of minerals identified
using the ASD spectrometer. Many slickensides along small faults were observed
nearby, and anomalous shrub vegetation indicated there may be water close to the
surface. In 1985 commercial temperatures of 204°C and 147°C at 8149 and 8589 ft
depths were reported for wells 88-11 and 88-11A, respectively [Davis and Hess, 2009].
Plans for a power plant near this location have not come to fruition due to reasons unavailable to the public. The high cost of transmission lines that would need to be built to Fish Lake Valley has likely been a deterring factor.
77
Figure 6.11 Photo taken at Site 31. Spectral analysis of field samples indicated the presence of kaolinite and alunite. Other minerals observed include chalcedony, cinnabar, and sulfur.
The AVIRIS-derived mineral map shows opal distributed along the southern part
of the Volcanic Hills (Figure 6.12). Opal is characterized by a broad SiOH absorption
feature at 2.2 µm (Figure 6.9). AVIRIS data were used to identify the large sinter apron
discussed by Reheis et al. [1993] and mapped by Reheis and Block [2007], as well as
other sinter deposits to the northeast. Field visits to Sites 6 and 7 validated the presence
of opal at the sinter apron (Figure 6.12). The sinter deposits to the northeast of the sinter
apron were also visited and opal was observed at Sites 3, 4, and 41-50. AVIRIS mineral
mapping classified both silicified sands and opaline sinter as opal (Figure 6.13). In Fish 78
Lake Valley, sinter deposits may have opaline layers, variable clast size, and conchoidal fracture (6.14). Sinter is sometimes inconspicuous because it is not obviously different from other sedimentary deposits. Sinter was observed in a variety of settings in Fish
Lake Valley; on the valley floor, within mounds, and within small hills. On the valley floor, sinter is often buried by sand, outcrops are scarce, and deposits are dominated by broken pieces (Figure 6.13). Opal was mapped north of the large sinter apron where a field visit to Site 37 revealed silicified pumice. ASD spectral analysis confirmed the presence of opal at this location.
Several pixels of opal were also mapped along the Emigrant Peak fault on the east side of Fish Lake Valley using the AVIRIS data. A field visit to this location (Site 61 on
Figure 6.16) revealed a large mound of sand and gravel, and ASD spectroscopy validated
the opal spectrum for this deposit. The deposit was mapped as QTg by Robinson et al.
[1976], composed of poorly sorted cobbles, grit, sand, and silt. The deposit is lighter in
color than surrounding deposits presumably because it is very quartz-rich. The opal
spectrum was likely derived from silica-rich volcanic glass contained within the sand and
gravel mound; the deposit does not appear to be related to geothermal activity.
AVIRIS-derived mineral maps also show opal distributed at higher elevations in
the Volcanic Hills within Tertiary latite and rhyolite tuffs [Robinson and Crowder, 1973]
(Figure 6.12). A field visit to one of these deposits (Sites 36A and 36B on Site 6.12)
revealed a belt of outcropping rhyolite tuff. The tuff is poorly sorted and contains gravel-
sized rock fragments. The entire belt was remotely mapped as opal and confirmed by
ASD spectroscopy. Opal was also mapped in the Silver Peak Range within a small
outcrop of Tertiary tuff. At these locations, the remotely mapped opal does not appear to 79
be related to geothermal activity and likely represents silica-rich volcanic glass contained
within the tuff. Weakly to unaltered volcanic glass may show a spectral signature similar to that of opal [Milliken et al., 2008].
Figure 6.12 AVIRIS-derived mineral map of the southern end of the Volcanic Hills overlain on NAIP imagery. Black dots show field locations and blue does show relevant geothermal wells. Location of the image is shown in Appendix II.
80
Figure 6.13 Examples of sinter deposits in Fish Lake Valley, all mapped as opal using AVIRIS data.
81
Figure 6.14 Various textures for siliceous sinter.
6.4 HyMap
HyMap data were used to map calcite, kaolinite, montmorillonite, muscovite, alunite, opal, and a combination of muscovite and clinochlore. Figure 6.15 shows
HyMap spectra for remotely mapped minerals, compared with USGS library spectra and spectra collected from field samples using the ASD. Muscovite, kaolinite, calcite, montmorillonite, opal, and the muscovite+clinochlore combination were all identified based on spectral features discussed in the previous sections of this chapter. Minerals 82 were mapped throughout the HyMap scenes, primarily within the Volcanic Hills and
Emigrant Hills, and along the west side of the playa.
Figure 6.15 HyMap spectra for remotely mapped minerals, compared with USGS library spectra and some ASD spectra convolved to HyMap wavelengths. 83
Within the Volcanic Hills (Figure 6.16), calcite is correlated with the Cambrian
Harkless and Emigrant Formations [Robinson and Crowder, 1973]. Both formations contain limestone [Albers and Stewart, 1972]. Calcite is also mapped near The Gap in
Columbus Salt Marsh. Three deposits of travertine were mapped by Reheis and Block
[2007] in this region, two of which were identified using HyMap data (Sites 24 and 25 on
Figure 6.16). The travertine often contains hematite and has textures associated with biologic activity. The third deposit of travertine, not mapped using HyMap data, is a small spring surrounded by a calcite and clay combination (Site 26 on Figure 6.16). The spring has formed a mound covered in salt and vegetation. Travertine samples analyzed with the ASD showed spectra with low reflectance and shallow absorption features at 2.2
µm and 2.33 µm, which are indicative of a mix of clay and carbonate. This type of deposit is difficult to detect using remote sensing data because it is wet and hidden by vegetation. 84
Figure 6.16 Distribution of minerals mapped using HyMap data overlain on NAIP imagery. Black dots show field locations.
Abundant calcite is also mapped in parts of Fish Lake Valley, primarily northwest of the playa. In this area, grey calcite was observed at Sites 18, 52, 54, 55, 57A, 58, 59, and 60A (Figure 6.16). A shallow pit at Site 52 revealed near-surface crystalline calcite 85 interbedded with carbonate-cemented beach gravels (Figure 6.17). The calcite is likely travertine deposited by springs during the Pleistocene, similar to the deposits mapped by
Reheis and Block [2007]. Interbedded gravel suggests either fluctuating spring contributions or fluctuating input of clastic sediments.
6.17 Gravels with interbedded travertine at Site 52. Handheld GPS unit for scale.
86
Remotely mapped calcite and opal were identified in close proximity to one
another in an area northwest of the playa. Field observations indicate that alternating layers of travertine and siliceous sinter comprise hills in this region. For example, at Site
58 moving up a 4 m vertical section, carbonate, then sinter, then carbonate, and then
sinter were collected. Carbonate was differentiated from sinter in the field using HCl.
ASD spectroscopy of the carbonate rocks revealed a component of opal in addition to
calcite which suggests carbonate and opaline sinter were coprecipitated. The reflectance
spectra from these mixed samples is unusual and shows the characteristic carbonate
absorption feature at 2.33 µm and a shallow feature at 2.22 µm, diagnostic of opal
(Figure 6.18). A linear mix of library opal and calcite spectra does not accurately model
the mixture spectrum; more work is necessary to understand the spectral features of the
mixture. Alternating sinter and travertine were also observed at Sites 57, 59, and 60
(Figure 6.16). The hot spring deposits comprise hills mapped by Robinson et al. [1976]
as a Tertiary sedimentary unit composed of greenish-grey claystone with minor siltstone
and sandstone.
87
Figure 6.18 ASD reflectance spectra for samples collected from a 4 m vertical section at Site 58. A was collected from the bottom of the section and D from the top. Spectra of A and D show a combination of opal and calcite whereas spectra of B and C show pure opal.
Calcite and opal were also mapped further south near the southern edge of the
Volcanic Hills. Opal was mapped within the sinter mound discussed by Reheis et al.
[1993] at Site 7 (Figure 6.16), and calcite along the adjacent fault. Where the HyMap and AVIRIS data coverage overlaps, the two mineral maps agree very well. Calcite was also mapped along the Emigrant Peak fault zone on the eastern side of Fish Lake Valley at Site 15 (Site 6.16). The remotely mapped calcite at this location represents mounds of floated carbonate material containing silt to pebble sized clasts. The deposits are mapped as greenish-grey claystone by Robinson et al. [1976], an accurate description despite the 88
unconsolidated nature of this deposit. Hulen et al. [2005b] map the same deposits as
Tertiary tuffaceous lacustrine mudstone.
Using the HyMap data, opal was mapped in the Volcanic Hills near Columbus
Salt Marsh. Kratt et al. [2009] mapped opal in the same location using ProSpecTIR data.
A field visit one of the remotely mapped opal deposits (Site 29A) confirmed the presence
of Tertiary rhyolite tuff, which shows an opal reflectance spectrum. The bottom of the
tuff unit is bleached white while the top is orange from hematite. These tuff units are not
related to geothermal activity; instead, they likely contain silica-rich volcanic glass which results in the opal spectrum.
Alunite was mapped at one location (Site 2 on Figure 6.16), within Tertiary rhyolite ash flow tuff [Robinson et al., 1976]. Alunite was identified by the broad absorption feature at 2.16 µm and shallower feature at 2.32 µm (Figure 6.15). ASD spectral analysis in the field verified the presence of alunite, which occurs in a localized area at the end of a mineral exploration trench where the top layer of the ground has been disturbed (Site 1B on Figure 6.16). Several small faults with slickenlines were also observed within the rhyolite ash flow tuff. Faults are filled with opal and nearby rocks contain abundant hematite, as confirmed by ASD spectral analysis. Appendix III shows spectra from field samples compared with USGS library spectra.
Using HyMap data, kaolinite was mapped at four locations within the Miocene rhyolite tuff of the Volcanic Hills [Robinson and Crowder, 1973]. The presence of kaolinite was verified at all four locations. At the westernmost location, kaolinite is found within a slot canyon where the rocks have been completely altered to kaolinite 89
(Site 28 on Figure 6.16). A fault was found to cut through the kaolinite deposit. The fault plane contains a hard brown mixture of kaolinite and goethite with slickensides.
(Figure 6.19) Kaolinite was also mapped and verified in the next canyon to the east at
Site 29B (Figure 6.16). At this location, a small amount of kaolinite was mapped at the base of the rhyolite tuff mapped as opal using HyMap data (Site 29B on Figure 6.16).
Kaolinite was also remotely mapped and verified within rhyolite tuff near Route 773
(Sites 27A and 30B on Figure 6.16). The rocks have been altered and contain kaolinite, which is inconspicuous in the field. Finally, kaolinite was mapped in the rhyolite tuff near wells 88-11 and 88-11A, where it was also mapped using AVIRIS data. This area is described in Section 6.3 and is the location of Sites 8B and 31 (Figure 6.16).
90
Figure 6.19 Fault plane surrounded by kaolinite at Site 28. Handheld GPS unit for scale.
Kaolinite was also mapped in the Emigrant Hills within the Tertiary rhyolite ash
flow tuff [Robinson et al., 1976]. Distribution generally agrees with the mineral map
produced using the ProSpecTIR data (Figure 6.7) though kaolinite is better resolved and
therefore mapped in greater abundance in the HyMap-derived map (Figure 6.16). Some
new areas of kaolinite are identified by the HyMap mineral map. Kaolinite was also mapped further south within the Emigrant Hills, beyond the coverage of the ProSpecTIR data. Kaolinite was confirmed at Sites 65-68 and 70 (Figure 6.16) where it occurs in altered rhyolite ash flow tuff near the contact with the Palmetto Formation. A large 91
strike-slip fault was also observed at Site 70 (Figure 6.16), which may be part of the
Coaldale fault system.
The muscovite+clinochlore combination is mapped within the Volcanic Hills and along the Emigrant Peak fault (Figure 6.16). Distribution of the mineral assemblage correlates with the Cambrian Harkless Formation, as discussed in Section 6.3. Muscovite was remotely mapped throughout Fish Lake Valley within several units. In the Volcanic
Hills area, muscovite was remotely mapped along the thrust fault that exposes the
Harkless Formation adjacent to the Cambrian Mule Springs limestone and Emigrant
Formation, and the Tertiary rhyolite tuff of the Volcanic Hills. Montmorillonite was remotely mapped within the Tertiary rhyolite tuff of the Volcanic Hills. Montmorillonite was confirmed at Site 30A (Figure 6.16) as a white mineral within a small arroyo on the valley floor, where it is likely a weathering product.
6.5 FTIR Analysis of Opal
Samples of sinter from Sites 7, 19, and 42 (Plate 1) were examined using Fourier transform infrared (FTIR) spectroscopy to confirm their opaline mineralogy. Each of these samples was identified as opal by its SWIR spectrum. The FTIR instrument collects high resolution data at longer wavelengths than the ASD, from 3.3 to 24 µm.
Spectra from each sample matched the library spectrum for opal (Figure 6.20). Opal is spectrally unique when compared with other silica polymorphs including tridymite, cristobalite, quartz, and coestite [Michalski et al., 2003]. Information about crystallinity 92
and therefore diagenesis may be attained because opal-A, opal-C, and opal-CT have slightly different FTIR spectra [Michalski et al., 2003; Adamo et al., 2010]. Opal-C is
differentiated by a Si-O bending absorption feature at 16.13 µm [Adamo et al., 2010],
which is absent from all Fish Lake Valley samples. Opal-A and opal-CT are more
difficult to differentiate using FTIR spectroscopy. The Fish Lake Valley samples are
tentatively identified as opal-A based on the steep shoulder near 8 µm [Michalski et al.,
2003], but may well be opal-CT. Using thin sections and XRD data, Reheis et al. [1993]
found that most of the silica cement in the sinter apron (Site 7) is opal-CT. 93
Figure 6.20 FTIR spectra for samples from Sites 7, 19, and 42 compared with example opal and quartz spectra.
94
Chapter 7. Interpretation and Discussion
7.1 Mineral Map Synthesis and Interpretations
The mineral maps derived from the MASTER, ProSpecTIR, AVIRIS, and HyMap data were viewed in ArcGIS with other geologic data including a 1:10,000-scale geologic map of the Emigrant geothermal prospect [Hulen et al., 2005b], a 1:24,000-scale surficial geologic map [Reheis and Block, 2007], the 1:62,500-scale Rhyolite Ridge quadrangle geologic map [Robinson et al., 1976], which was digitized by the Nevada Bureau of
Mines and Geology [2007], the 1:62,500-scale Davis Mountain quadrangle geologic map
[Robinson and Crowder, 1973], the 1:62,500-scale Mt. Barcroft quadrangle geologic map
[Krauskopf, 1971], and a 1:250,000-scale geologic map of Esmeralda County [Albers and
Stewart, 1972]. The ProSpecTIR-derived mineral map by Kratt et al. [2009] was georeferenced in ArcGIS and used for comparison of mapping results. The HyMap- derived mineral mapping results by Martini et al. [2004] were also used for comparison.
A shapefile showing geothermal wells [Zehner, 2007] was also used. Plate 1 shows the mineral map overlain on digitized geologic maps.
Targets for future geothermal exploration efforts were identified and are discussed in this chapter. Four areas of hydrothermal alteration and mineralization were identified using the mineral map. These areas were identified as having abundant hydrothermal minerals, specifically alunite, kaolinite, opal, and calcite. Alunite can indicate alteration of potassium feldspars as a reaction with sulfuric acid or it can form from fumarolic activity. Kaolinite may be a product of argillic alteration of feldspars, a low temperature reaction which may result from acidic thermal fluids moving through the rock, or 95
chemical weathering. It also forms in shallow steam-heated and/or fumarolic environments where rising steam condenses and/or mixes with groundwater. Opal is not actually a mineral as it lacks a definite crystal structure; it is an amorphous silica gel deposited in low temperature environments. Opal may be fracture filling or it may comprise siliceous sinter deposits of hot springs. Siliceous sinter is generally composed of noncrystalline opal-A, which decomposes over time to paracrystalline opal-CT or opal-C, and finally microcrystalline quartz [Herdianita et al., 2000]. Crystallization to opal-CT and/or opal-C generally occurs between 10,000 and 60,000 years after deposition [Herdianita et al., 2000]. Phases may be differentiated by XRD analysis and to some extent, infrared spectroscopy. Identification of siliceous sinter gives important information about where thermal fluids have ascended to the ground surface. The presence of sinter is of particular relevance in geothermal exploration because significant sinter deposits typically do not form unless the thermal waters have been at temperatures greater than 180°C at depth because of the enhanced solubility of silica at high temperatures. Calcite (or aragonite) can be an important geothermal indicator as it comprises travertine and tufa deposits. Hot springs derived from water containing calcium and bicarbonate deposit CaCO3 travertine as the water is depressurized
subaerially [Pentecost, 1995]. As CO2 is released, the water becomes more basic and
CaCO3 is precipitated. Tufa is deposited when Ca-rich spring water reacts with CO2-rich
lake water [Benson, 1994]. Not all tufa is associated with hot springs; thermal tufa may
be distinguished from non-thermal tufa using lithium/magnesium and lithium/sodium
ratios and trace metal concentrations [Coolbaugh et al., 2010]. The distinction between
travertine and tufa is important and requires field observation. Geothermal systems that 96
produce travertine are generally not economic for electricity production [Coolbaugh et
al., 2009]. CO2 has retrograde solubility so it is dissolved in lower temperature fluids.
Tufa may be associated with higher temperature fluids because CO2 is not necessarily
dissolved in the thermal fluid, and instead may be provided as dissolved atmospheric
CO2 in the lake.
Target areas were also chosen based on the presence of mapped and observed faults. Faults may allow for thermal fluids to ascend toward the surface. Fault intersections and/or fault stepovers generally enhance permeability [Faulds et al., 2006]
and were considered when choosing targets in Fish Lake Valley. Target areas for future
exploration include the Volcanic Hills, Emigrant Hills, Silver Peak Range, and Fish Lake
Valley to the west of the playa (Figure 7.1). These areas were identified as targets for future exploration due to the presence of opal, travertine, alunite, and kaolinite. For each target location, a geologic analysis of the remotely mapped minerals was performed and is explained here. Figure 7.1 shows the locations of target areas discussed in this chapter.
Figures 7.2 to 7.4 are subsets of Plate 1.
The Emigrant prospect will also be discussed in this chapter. The Emigrant prospect (Section 2.3.1) is the location of a known geothermal system, identified as a thermal anomaly by drilling data and further explored with a deep geothermal well, detailed geologic mapping, and structural consideration. Unlike the other areas discussed in this chapter, very little hydrothermal alteration and geothermal mineralogy were remotely mapped over the Emigrant prospect. 97
Figure 7.1 Synthesized mineral map over Fish Lake Valley showing five areas discussed in this chapter. 98
7.2 Volcanic Hills Target
The kaolinite mapped within the Volcanic Hills classifies the area as a target for
future exploration (Figure 7.2). The majority of kaolinite occurs within a slot canyon
where the rhyolite tuff of the Volcanic Hills has experienced argillic alteration. The rock
is pervasively altered to kaolinite but contains fragments of pumice and sedimentary
rock. Kaolinite distribution is constrained to the east by a contact with the Cambrian
Harkless Formation. A fault cuts across the zone of kaolinite and is represented by a
brown plane with slickenlines. The brown rocks within the fault plane are composed of kaolinite and goethite, according to ASD spectral analysis. While the goethite may be hypogene and produced by oxidizing fluids ascending along the fault, it is more likely a weathering product. The fault has likely acted as a conduit for meteoric water, which oxidized pyrite within the plane. Pyrite may have been a product of earlier hydrothermal fluids or may have been included within the tuff. A low pH fluid resulted in the argillic alteration of the nearby rhyolite tuff. The fluid may be an expression of a geothermal system at depth. The fault within the altered zone may be related to a parallel normal fault mapped by Robinson and Crowder [1973] ~250 m away (Figure 7.2). Small deposits of muscovite and montmorillonite are remotely mapped near the major kaolinite deposit and may also be related to hydrothermal alteration. 99
Figure 7.2 Mineral map of the Volcanic Hills target for future exploration, overlain on a digitized version of the Davis Mountains quadrangle [Robinson and Crowder, 1973]. The geologic map should be viewed at a 1:62,500 scale but is here displayed as 1:31,250, still at lower resolution than the overlying mineral map. Black dots show field locations.
Kaolinite and opal were mapped in the drainage east of the previously mentioned
zone of kaolinite (Figure 7.2). At this location, kaolinite was remotely mapped along the
base of a rhyolite tuff at Site 29A and verified in the field. The altered rhyolite tuff
contains abundant kaolinite with quartz phenocrysts and some iron oxide staining. There
are no indicators to determine whether the kaolinite was produced by weathering or
hydrothermal alteration. Opal was remotely mapped near the kaolinite within the rhyolite
tuff of the Volcanic Hills, which is not visibly silicified (Site 29A on Figure 7.2). The 100
tuff is a greenish-grey, porphyritic, welded rock with quartz phenocrysts and fragments of
obsidian, pumice, and sedimentary rock. At the top of the tuff unit, the bright reddish-
orange rocks have clearly been oxidized. The tuff was mapped as opal because of silica-
rich volcanic glass within the rock. Pixels with the opal spectrum were also identified
further south in the Volcanic Hills within Tertiary latite and rhyolite tuffs. A field visit to
Site 36 (Plate 1) with the ASD confirms the presence of a belt of rhyolite tuff with an
opal reflectance spectrum. While only confirmed at two locations, glass-bearing tuff is
likely represented by all remotely mapped opal within the Volcanic Hills. While the tuff
may have been influenced by hydrothermal fluids at one time, these rocks do not provide
relevant information about thermal fluid flow in the way that clay alteration minerals and
sinter deposits do.
7.3 Emigrant Hills Target
Travertine and kaolinite were remotely mapped within the Emigrant Hills. A
large travertine unit dips southward into a normal fault mapped by Robinson et al. [1976]
(Figure 7.3). The travertine was deposited by CO2-rich hot springs. The CO2 was likely
derived from limestone beds in the Palmetto and/or Harkless Formations, both of which lie beneath the unit [Robinson et al., 1976]. The travertine is part of the Esmeralda
Formation, generally composed of sandstones, shales, and marls that were originally deposited in Lake Esmeralda during the Middle and Late Miocene [Turner, 1900]. The unit is mapped as a siltstone, sandstone, conglomerate member of the Esmeralda
Formation by Stewart [1989] and a conglomerate and sandstone by Robinson et al. 101
[1976]. Spring-deposited travertine has been observed within the Esmeralda Formation
at other locations in the Silver Peak Range [Turner, 1990]. Fluids likely ascended to the
surface along faults; there are several normal faults mapped by Robinson et al. [1976] in
the area. There may also be unmapped faults in the area. As suggested by Martini et al.
[2004], this region of the Emigrant Hills is where the EPFZ and Coaldale fault theoretically intersect. The Coaldale fault is a left-lateral fault that is not explicitly mapped by Robinson et al. [1976]. An east-west trending fault with sub-horizontal slickenlines was observed adjacent to the travertine at Site 70 (Figure 7.3), and may be a segment of the Coaldale fault. The intersection of the Coaldale fault and EPFZ likely allows for the increased fracture permeability that facilitated the upward flow of thermal fluids.
As noted by Martini et al. [2004], there is a concentration of kaolinite in this area of theoretically intersecting faults. Some kaolinite and muscovite are distributed in linear trends and may be products of fluids leaking along faults (Figure 7.3). Kaolinite is generally confined to the Tertiary rhyolite tuff and often occurs along mapped contacts with the Ordovician Palmetto Formation [Martini et al., 2004]. Despite map appearances, the rhyolite tuff and Palmetto shale do not share vertical contacts so vertical fluid flow between the units is not possible. The rhyolite tuff is stratigraphically above the Palmetto shale and where kaolinite occurs along the map contact, it actually occurs as altered tuff along an eroded slope above the shale. The stratigraphic relationship between the units suggests that kaolinite along these slopes is weathering-derived and not a geothermal product unless proximal to a fault. 102
Figure 7.3 Mineral map of the Emigrant Hills target for future exploration, overlain on a digitized version of the Rhyolite Ridge quadrangle [Nevada Bureau of Mines and Geology, 2007]. The geologic map should be viewed at a 1:62,500 scale but is here displayed as 1:31,250, still at lower resolution than the overlying mineral map. Black dots show field locations.
The Emigrant Hills area should be the target for future exploration efforts. A
detailed geologic map showing alteration and faults would be especially useful for this
prospect as there are both geologic units and faults that are not included on the map by
Robinson et al. [1976]. The travertine indicates the presence of potentially thermal 103 springs during the Miocene. The pervasive argillic alteration signified by kaolinite indicates thermal fluids leaking along faults. The timing of the alteration is currently unknown.
7.4 Fish Lake Valley Target
The sinter apron discussed by Reheis et al. [1993] was remotely mapped as opal using both AVIRIS and HyMap data. Reheis et al. [1993] suggested the sinter apron was deposited ca. 0.77 Ma in a hot spring environment near the edge of Pluvial Lake Rennie.
The authors suggest most of the sinter mound was deposited just after the Bishop ash because it contains rework tephra, pumice, and bubble shards [Reheis et al., 1993]. Only near the top of the unit do pumice clasts have spherical vesicles; throughout the rest of the sinter unit, vesicles are flattened. This suggests that lighter pumices floated on the top of the lake and gives an approximate elevation for the paleoshoreline of 1460 m
[Reheis et al., 1993]. Similar rock textures and elevation suggest the sinter at the other locations may also have been deposited along the pluvial lake shoreline. The sinter apron and adjacent travertine occur along a fault (Plate 1) that offsets Holocene (?) deposits by
~1 m [Reheis et al., 1993].
104
Figure 7.4 Mineral map of the Fish Lake Valley target for future exploration, overlain on digitized versions of the Rhyolite Ridge [Nevada Bureau of Mines and Geology, 2007] and Davis Mountains quadrangles displayed at a 1:62,500 scale [Robinson and Crowder, 1973]. 105
The Fish Lake Valley area is a previously identified and explored geothermal
exploration target, but results from this study expand the target region to the northeast
(Figure 7.4). HyMap data were used to identify calcite and opal deposits within the hills
northwest of the playa in Fish Lake Valley, rendering this the most interesting geothermal
prospect identified by this study. Field observations revealed alternating layers of calcite
and opal, interpreted as travertine and siliceous sinter. Beds are flat lying and can be
sighted across drainages to adjacent hills. The travertine is generally dark grey, reacts
with HCl, and sometimes contains burrow casts and small plant fossils. The travertine is
often fine-grained but sometimes is expressed as carbonate-cemented gravel and sand,
likely deposited in a shallow water environment. The sinter often exhibits conchoidal
fracture and has a vitreous luster. Sinter deposits are similar in texture and morphology
to the hot spring deposits described by Reheis et al. [1993] within the sinter apron
deposited along the edge of Pluvial Lake Rennie. The alternating nature of the travertine
and sinter may reflect fluctuating geothermal fluid chemistries. Sinter may have been
deposited by thermal waters that ascended without mixing with shallower waters.
Travertine may have been deposited by thermal waters that mixed with shallow aquifers
containing dissolved CO2. Layers containing both opal and calcite suggest some stages of coprecipitation.
The distribution of sinter and travertine may be controlled by faults. Thermal
water likely ascended along conduits at the edge of Pluvial Lake Rennie. Reheis and
Block [2007] mapped a normal fault adjacent to remotely mapped opal and calcite northwest of the playa (Figure 7.5). Another fault was observed at Site 53 and may 106
extend in the direction of Site 58, bounding the travertine and sinter hill (Figure 7.6).
There may be other unmapped faults that are also responsible for the movement of fluid related to the sinter and travertine deposits. Alunite was remotely mapped and verified just uphill from travertine and sinter deposits (Figure 7.5). Two segments of faults were
observed nearby (Sites 1A, 2, and 20). The alunite may be a fumarolic expression of
thermal fluids ascending along faults. Perhaps the water table was too low to create fluid-derived geothermal deposits at this elevation and instead stream produced alunite.
The orientation of the faults in this region is consistent with normal faults to the southwest within the valley floor (Plate 1). While the Emigrant Peak fault zone on the eastern side of the valley is responsible for much of the opening of the northern Fish Lake
Valley pull-apart basin, these western faults are also expressions of extensional movement. Extension likely allowed for increased permeability along this series of normal faults, resulting in the upflow of thermal fluids. 107
Figure 7.5 Mineral map overlain on NAIP imagery showing distribution of remotely mapped opal, calcite, and alunite in the northern part of the Fish Lake Valley prospect. The potential locations for an extinct fumarole and extinct hot springs are shown. The fault indicated by a solid line was mapped by Reheis and Block [2007]. Dashed lines show the potential location of other faults. Black dots show field locations and blue dots show geothermal wells.
Parts of the Fish Lake Valley prospect have been explored for geothermal potential in the past. Section 2.3.2 summarizes the exploration efforts and publically available findings for the region within the original Fish Lake Valley prospect. The new addition to the prospect area (shown in Figure 7.5) has not been explored so thoroughly.
In 1992, Fish Lake Power Company drilled geothermal wells relatively close to the 108
remotely mapped opal and calcite deposits. Well FLP No. 1 was drilled to 2490 ft depth
near the remotely mapped alunite (Figure 7.5). A maximum temperature of 64°C and a thermal gradient of 84°C/km were reported [Zehner, 2007]. Closer to the Volcanic Hills,
Well FLP No. 3 was drilled to 3428 ft depth, and a maximum temperature of 81°C and thermal gradient of 77°C/km were reported (Plate 1) [Zehner, 2007]. While exploring for borates, AMAX drilled temperature gradient Hole 34 near the geothermal deposits
(Figure 7.5). No data from this hole are available. The two wells drilled by Fish Lake
Power Company do not suggest an economic geothermal system at depth; however a well drilled near the sinter and travertine deposits targeting a fault may reveal more encouraging thermal gradients. Although a geothermal system is likely to exist in this region, sinter and/or travertine are not currently forming because the water table is too low. The northern White Mountains have been uplifted ~1 km since the Pleistocene along the White Mountains fault system [DePolo, 1989]. Uplift of the range has resulted in an increased orographic effect; Fish Lake Valley now has a drier climate and lower water table than during the Pleistocene [Reheis et al., 1993].
A 4 km-long area with no remotely mapped geothermal deposits exists between the sinter apron of Reheis et al. [1993] and the deposits northwest of the playa shown in
Figure 7.5 (Figure 7.4). Perhaps other deposits have been eroded away, or maybe the two areas represent two different geothermal systems currently classified within the same prospect. Deposits from both ends of the prospect area are at similar elevations and were likely formed along Pluvial Lake Rennie during the Pleistocene.
109
7. 5 Silver Peak Range Target
The mineral map of Fish Lake Valley shows some hydrothermal mineralogy mapped within the Silver Peak Range (Figure 7.6). This is a zone of complicated faulting which may allow for more fracture permeability, acting as a preferential zone for fluid flow. Remotely mapped kaolinite with lesser montmorillonite and muscovite were mapped within Tertiary rhyolite tuff and a Tertiary sandstone and conglomerate. Some of these clay minerals occur in linear trends and may be represent hydrothermal alteration by fluids leaking from faults. The minerals are distributed along at least one fault mapped by Robinson and Crowder [1973] (Figure 7.6). The region remains relatively unexplored; further field work should delineate unmapped faults and help to determine whether remotely mapped alteration minerals are geothermal products.
110
111
Figure 7.6 Mineral map of the Silver Peak Range target for future exploration, overlain on digitized versions of the Rhyolite Ridge quadrangle [Nevada Bureau of Mines and Geology, 2007] and Davis Mountains quadrangle [Robinson and Crowder, 1973]. The geologic map should be viewed at a 1:62,500 scale but is here displayed as 1:31,250, still at lower resolution than the overlying mineral map. Black dots show field locations.
7.6 Emigrant Prospect
The Emigrant prospect has been a target for geothermal exploration since it was discovered in the early 1980s. The area was covered by MASTER and HyMap data.
Geology and alteration of the prospect were mapped by Hulen et al. [2005b] at a
1:10,000-scale. The Emigrant Peak fault zone and several other parallel normal faults are mapped within the area. Alteration was mapped throughout the region, predominantly within Quaternary alluvium, Tertiary tuffaceous sedimentary rocks and rhyolite ash flow tuff, the Ordovician Palmetto Formation, and Cambrian Emigrant Formation. As discussed in Section 2.3.1, rocks have experienced a range of argillic alteration, silicification, decalcification, and quartz and calcite veining [Hulen et al., 2005b]. The alteration mapped by Hulen et al. [2005b] is widespread and shows some spatial correlation with faults. Plate 1 shows the extent of all alteration mapped by Hulen et al.
[2005b]. Very little of this alteration was identified using remote sensing data (Figure
7.7). Some silicified areas were identifiable in the MASTER TIR data; these deposits are generally yellow in the DCS of MASTER bands 48, 45, and 44, as discussed in Chapter
6. 112
Figure 7.7 Mineral map of the Emigrant prospect overlain on NAIP imagery.
The Green Monster mine is where Hulen et al. [2005b] mapped the only obvious surface expression of the Emigrant geothermal system: a small fumarole, advanced argillic alteration (nontronite and kaolinite), and sulfur. The Green Monster mine area is 113
also the only place where geothermal minerals were remotely mapped within the
Emigrant prospect. The area was identified using HyMap data as kaolinite and opal.
These minerals were verified in the field, and alunite, jarosite, illite, and gypsum were
also observed. The other alteration mapped by Hulen et al. [2005b] was not detected by
the remote sensing instruments. According to Hulen et al. [2005b] hydrothermal
alteration and mineralization within the Palmetto Formation occurs in small patches,
which are likely too small to be imaged by remote sensing instruments. Alteration within
limestone units of the Emigrant Formation is characterized by calcite veining and
dissolution [Hulen et al., 2005b], which are difficult to detect with remote sensing data due to the small scale and consistent carbonate mineralogy. There is weak to moderate
silicification and argillic alteration to nontronite along low-angle normal faults within
ignimbrites and tuffs of the Emigrant Formation [Hulen et al., 2005b]; the alteration likely occurs on a small scale and/or along vertical faces that are not well imaged by remote sensing instruments. Finally, there is more argillic alteration within the tuffaceous sedimentary rocks of the Fish Lake Valley assemblage [Hulen et al., 2005b], which must exist on a scale too small to be detected by remote sensing instruments.
114
Chapter 8. Summary and Conclusions
The purpose of this study was to identify and map hydrothermal alteration
minerals within Fish Lake Valley, Nevada using remote sensing data from four different
airborne instruments. Data from each instrument were also examined for value as a
Nevada geothermal exploration tool. Each data set covers a different region of the
valley; mosaiced, the data cover the northern Fish Lake Valley pull-apart basin and surrounding ranges. The results from each data set were combined to create a mineral map for the entire region. Given the distribution of hydrothermal alteration minerals and geothermal deposits, four targets for future geothermal exploration were identified. The target areas were visited and primary geologic interpretations were made based on the distribution of minerals. The targets will be discussed in the following section.
8.1 Fish Lake Valley Geothermal Prospects
Remotely mapped kaolinite identified an area of interest within the Volcanic Hills where there has been argillic alteration of rhyolite tuff surrounding a normal fault. The kaolinite occurs locally, but the rocks have been strongly altered and suggest continued movement of fluid along the fault. The topography of the area is not well suited for remote sensing studies because of deeply incised drainages. Field studies should provide further evidence for a geothermal system in this region. Dating the argillic alteration would determine whether it is related to a potentially active modern geothermal system or an inactive ancient system. 115
The Emigrant Hills region was initially recognized by Martini et al. [2004] as a
geothermal target. Remote mapping of calcite and kaolinite further promote interest in
the area. Travertine is represented by the remotely mapped calcite and was likely
deposited in a Miocene hot spring environment associated with Lake Esmeralda. Nearby
faults associated with the Coaldale fault system likely allowed for the movement of
thermal fluids. Rocks in the Emigrant Hills region are probably highly fractured because
of the intersection of the Coaldale fault with the EPFZ [Martini et al., 2004]. Remotely
mapped kaolinite is generally distributed within the Tertiary rhyolite tuff along mapped
faults and linear trends. In these locations, kaolinite suggests argillic alteration of the tuff
by fluids discharged from the faults. In areas away from faults, remotely mapped
kaolinite is likely a weathering product not related to geothermal activity.
The Silver Peak Range was identified as a geothermal exploration target primarily
because of remotely mapped kaolinite, which occurs within the Tertiary rhyolite tuff and
a sandstone and conglomerate unit. Minor montmorillonite and muscovite were also
mapped in the region. The clay minerals occur along some faults and linear trends and may be products of hydrothermal alteration.
The established Fish Lake Valley geothermal prospect was extended to include
geothermal deposits ~6 km to the northeast of previously mapped sinter. The deposits
were identified as opal and calcite using remote sensing data, and are composed of
alternating layers of siliceous sinter and travertine. Nearby, a small outcrop of altered tuff was identified as alunite using remote sensing data. The sinter and travertine were
likely deposited in a hot spring environment during the Pleistocene when the water table 116 was higher, as proposed by Reheis et al. [1993] for the origin of the main sinter apron.
Hydrothermal fluids likely ascended along extension-related normal faults in the valley and were expressed at the surface as hot springs and fumaroles at the edge of Pluvial
Lake Rennie. One such fault is mapped within the geothermal deposits [Reheis and
Block, 2007], and others are thought to exist because short segments were observed in the field. While hot springs are not present at the surface, a well targeting one such fault may encounter hot water at depth. This new area of the Fish Lake Valley prospect should be the highest priority target for future exploration because of the abundance of geothermal deposits.
The alteration minerals remotely identified at the previously studied Emigrant geothermal prospect included kaolinite and opal at the Green Monster mine and silica- rich minerals at some of the silicified areas mapped by [Hulen et al., 2005b]. The Green
Monster Mine area is the location of the only obvious geothermal system surface expressions. More inconspicuous alteration mapped by Hulen et al. [2005b] likely exists on a scale too small to be detected by remote sensing instruments.
8.2 Comparison of Remote Sensing Data Sets
MASTER TIR data were used to produce DCS images that showed calcite, silica- and clay-rich deposits. This technique was useful for a preliminary analysis of the region. The MASTER TIR data for this study were used to identify silicification mapped by Hulen et al. [2005b] within the Emigrant prospect, and sinter and travertine deposits mapped using HyMap data. The colors used in the DCS for these deposits are not 117 necessarily unique and a priori information about the scene is required to identify them.
MASTER TIR data have low spectral resolution and may not always be useful for resolving specific mineralogy. MASTER VNIR/SWIR data were used to map kaolinite, muscovite, and calcite. Low spectral resolution does not allow for mapping of many other minerals. All MASTER data used in this study have 11 m spatial resolution and are not capable of identifying small deposits.
Part of the MASTER data overlaps with AVIRIS data, so the two instruments were directly compared for their usefulness in mapping geothermal minerals. Remotely mapped calcite and muscovite distribution for the two data sets are quite similar.
Kaolinite was mapped more abundantly using the MASTER data, though generally in places where at least a few pixels were mapped using AVIRIS data. Some of the kaolinite pixels are likely false-positives due to the low spectral resolution of the
MASTER data. Unlike MASTER data, AVIRIS data were also used to map montmorillonite and muscovite+clinochlore. While AVIRIS data may be used to map more minerals with better accuracy, MASTER data are only useful as a rough preliminary exploration tool. MASTER data should be especially useful in regions with carbonate geothermal products since the data are as effective as AVIRIS for identifying calcite. Though not demonstrated for this area of overlap, MASTER TIR data may also be used to identify siliceous sinter. However, in other parts of Fish Lake Valley, AVIRIS data were used to identify siliceous sinter by mapping opal, which has a characteristic reflectance spectrum. For the types of hydrothermal alteration and geothermal products in Fish Lake Valley, TIR data is not a necessary tool. Data with higher spectral and 118 spatial resolution (e.g. AVIRIS) is more useful for geothermal exploration in the study area.
While ProSpecTIR data are noisier than HyMap and AVIRIS data, a comparison between ProSpecTIR and HyMap mineral maps over the Emigrant Hills reveals many similarities. Kaolinite and some montmorillonite were mapped within the area of overlap. The HyMap mineral map shows kaolinite distributed in some places where the
ProSpecTIR map does not, likely because subtle absorption features are overprinted by noise in the ProSpecTIR data. Perhaps in the places where HyMap shows kaolinite but
ProSpecTIR does not, the kaolinite absorption features were indistinguishable from noise.
HyMap data are better for mapping minerals with weak signatures, but for minerals with deep absorption features, ProSpecTIR data is just as useful as HyMap data. The spatial resolutions of ProSpecTIR and HyMap data for this study are similar at 4 and 3 m, respectively.
AVIRIS and HyMap data were used to map the same suite of alteration minerals.
Field verification of many of the remotely mapped mineral deposits confirmed the accuracy of these two instruments. The two data sets overlapped in a small strip at the southern edge of the Volcanic Hills, so they were directly compared. HyMap data may be slightly more effective for mapping opal than AVIRIS data, but in general, the HyMap and AVIRIS mineral maps are the same. The spatial resolutions of HyMap and AVIRIS data for this study are similar at 3 and 2 m, respectively. The major difference between the two instruments is that HyMap is a commercial instrument whereas AVIRIS is a
NASA instrument that is therefore used primarily to collect data for NASA-sponsored research projects. 119
8.3 Implications and Recommendations
This study accomplished the following:
1. Used ProSpecTIR and HyMap reflectance data to map kaolinite,
montmorillonite, muscovite, and calcite within the Emigrant Hills. An
unmapped travertine unit was identified.
2. Used HyMap reflectance data to map calcite, kaolinite, montmorillonite,
muscovite, opal, and muscovite+clinochlore within the Volcanic Hills. A
localized zone of strongly altered tuff was identified.
3. Used HyMap reflectance data to map calcite, opal, and alunite within Fish
Lake Valley proper. Unmapped alternating deposits of travertine and
siliceous sinter were identified.
4. Used MASTER and AVIRIS data to map calcite, kaolinite, muscovite,
montmorillonite, and muscovite+clinochlore within the Silver Peak Range.
Linear distributions of clay minerals may indicate unmapped faults with fluid
discharge.
5. Identified four areas to target future exploration efforts in Fish Lake Valley.
This study combines publically-available geologic data with new results from
AVIRIS and MASTER data, and repeated results from ProSpecTIR and HyMap data.
According to GeothermEx [2004], much of the existing geothermal exploration data for
Fish Lake Valley is not available to the public. Eventually, the results of this study should be synthesized with private drilling data and results of other geologic studies. The
HyMap data from this study may be used to map borate minerals, which can serve as 120 geothermal indicators. Borates were identified along the western edge of the Fish Lake
Valley playa (C. Kratt, personal communication, 2010) but not mapped. More detailed work should be done to specify their mineralogy and distribution to determine if thermal fluids are leaking along the edge of the playa. While field validation of remotely mapped minerals is essential, detailed geologic mapping is beyond the scope of this study. The four exploration targets demand further attention given their remotely mapped mineralogy. Future studies should focus on detailed geologic mapping and structural analyses in the style of Hulen et al. [2005b]. Careful fault mapping should be performed, as linear distributions of geothermal minerals suggest there may be unmapped faults that act as thermal fluid conduits. Finally, it will be important to understand timing of alteration and age of geothermal deposits to determine whether the surficial mineralogy represents an economic geothermal system at depth.
Remote sensing is here demonstrated as an effective tool for primary geothermal investigations. Mapping minerals with confidence over such a large area is useful because it focuses future studies and negates areas with little potential; in this study, four small target areas were identified from a ~500 km2 region. Remote sensing compliments field geology as it can be used to identify minerals that are inconspicuous in the field, and saves a field geologist from exploring low potential areas. Remote sensing is a useful exploration tool for Nevada where there is a lot of land, little vegetation, and easily missed hydrothermal alteration and geothermal deposits. 121
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Appendix I. Field Site Locations
Site # Latitude (N) Longitude (W) Description ASD mineralogy 1A 37°54'52.0" 117°58'51.5" Small fault Opal Rock pile at end 1B 37°54'53.3" 117°58'44.1" Alunite of trench 2 37°54'50.5" 117°58'57.3" Small fault Opal 3 37°52'03.5" 117°58'26.9" Outcrop Opal 4 37°52'02.0" 117°58'36.3" Outcrop Opal, hematite, alunite The Crossing- 5 37°52'05.3" 117°58'13.5" Mirabilite? playa 6A 37°51'20.1" 117°59'22.9" Outcrop Opal 6B 37°51'12.4" 117°59'22.9" Stone walls Opal Outcrop along 6C 37°50'56.6" 118°00'06.9" Opal fault 7 37°51'04.6" 118°00'02.0" Sinter apron Opal, calcite Drill pad 88-11, 8A 37°51'39.4" 118°02'59.0" Tuff w/ chalcedony 88-11A Kaolinite, alunite, 8B 37°51'40.24" 118°02'54.53" 2 prospect pits sulfur, chalcedony, hematite 9 37°51'15.6" 118°03'12.7" Drill pad 54-14 Tuff w/ chalcedony 10 37°51'36.0" 118°02'36.7" Drill pad 31-13 Tuff w/ chalcedony 11 37°51'41.8" 118°01'56.8" Drill pad 81-13
12 37°48'27.5" 118°01'56.8" Outcrop Muscovite, clinochlore 13 37°52'51.3" 117°54'23.4" Muddy bowl Gypsum 14A 37°53'12.9" 117°54'39.1" Well 17-31
Green Monster Alunite, jarosite, 14B 37°53'09.4" 117°54'27.4" mine- outcrop kaolinite, opal, illite Float beneath 14C 37°53'05.9" 117°54'27.0" Gypsum outcrop 15 37°53'47.9" 117°55'13.6" Pile of float Calcite 16 37°55'23.4" 117°56'12.7" Outcrop Opal 17 37°55'22.1" 117°56'37.2" Outcrop Calcite 18 37°55'25.7" 117°57'35.6" Outcrop Calcite 19 37°54'58.1" 117°57'25.7" Outcrop Opal 20 37°54'46.5" 117°58'57.5" Small fault Opal, hematite, calcite 21 37°57'17.2" 117°59'24.3" Outcrop Calcite 22 37°57'13.5" 118°00'09.4" Siltstone outcrop Alunite 23 37°58'46.5" 117°59'34.4" Outcrop Calcite 24 37°59'08.5" 117°58'50.8" Man-made pit Calcite, hematite Cold spring & 25A 37°59'34.8" 117°59'08.6" Calcite outcrop 141
Standing water 25B 37°59'37.3" 117°59'08.4" w/ vegetation Travertine 26 37°59'31.3" 117°59'18.3" Weak calcite/clay deposit w/ spring 27A 37°58'10.9" 118°01'10.7" Pile of tuff Kaolinite 27B 37°58'09.9" 118°01'07.2" Mud pit Gypsum 27C 37°58'07.2" 118°01'11.3" Pile of tuff Kaolinite 28A 37°58'56.4" 118°03'51.4" Slot canyon Kaolinite 28B 37°58'56.1" 118°03'51.7" Slot canyon Kaolinite, goethite 29A 37°59'10.3" 118°03'27.5" Tuff outcrop Opal Base of tuff 29B 37°58'11.0" 118°03'28.4" Kaolinite outcrop 30A 37°58’12.4” 118°01’07.6” Small arroyo Montmorillonite 30B 37°58’11.7” 118°01’09.5” Outcrop Kaolinite Kaolinite, alunite, 31 37°51’40.0” 118°02’55.1” 2 prospect pits cinnabar 32 37°51’46.5” 118°02’54.2” Hillside float Chalcedony 33 37°51’48.6” 118°02’53.4” Trench Chalcedony 34 37°51’55.2” 118°03’05.7” Mound of rock Tuff 35 37°52’16.8” 118°03’03.9” Fault Tuff 36A 37°52’38.6” 118°02’40.1” Outcrop Opal? 36B 37°52’44.3” 118°02’35.7” Outcrop Opal? 37 37°51’54.7” 118°00’17.1” Outcrop Opal 38 37°51’56.5” 117°59’33.7” Outcrop Calcite, opal 39 37°51’58.6” 117°59’28.7” Outcrop Calcite, opal 40 37°51’58.3” 117°59’25.3” Outcrop Opal 41 37°51’41.8” 117°58’39.9” Outcrop Opal 42A 37°52’02.3” 117°58’31.5” Outcrop Opal 42B 37°52’03.4” 117°58’31.8” Outcrop Opal 43 37°52’02.8” 117°58’36.0” Outcrop Opal 44 37°52’00.9” 117°58’36.8” Outcrop Opal 45 37°51’59.8” 117°58’39.5” Outcrop Opal 46 37°52’32.3” 117°58’52.6” Outcrop Calcite 47 37°52’31.9” 117°58’53.6” Broken pieces Opal 48 37°52’31.8” 117°58’54.9” Outcrop Opal 49 37°52’31.8” 117°58’56.2” Broken pieces Opal 50 37°52’32.0” 117°58’57.6” Outcrop Opal 51 37°52’19.3” 117°59’05.4” Trench Opal 52 37°54’57.8” 117°58’51.7” 2 pits Calcite 53A 37°55’00.3” 117°58’46.9” Fault Kaolinite 53B 37°55’00.1” 117°58’47.2” Fault 53C 37°54’59.7” 117°58’47.5” Fault 142
53D 37°55’00.7” 117°58’46.6” Fault 54 37°55’01.8” 117°58’46.2” Outcrop Calcite 55 37°55’04.7” 117°58’42.0” Outcrop Calcite 56 37°55’25.2” 117°57’37.2” Outcrop Opal 57A 37°55’23.2” 117°57’38.2” Outcrop Calcite 57B 37°55’23.2” 117°57’38.4” Outcrop Opal 58 37°55’31.9” 117°57’50.6” Outcrop Opal, calcite 59 37°55’27.1” 117°58’04.4” Outcrop Calcite, opal 60A 37°54’59.2” 117°57’29.6” Outcrop Calcite 60B 37°55’01.1” 117°57’30.3” Outcrop Opal 61 37°51’20.9” 117°55’37.3” Hill of sand Opal 62 37°49’05.1” 117°57’02.3” Outcrop Muscovite 63 37°48’45.7” 117°56’30.4” Outcrop Muscovite 64 37°49’02.2” 117°56’21.0” Outcrop Muscovite 65 37°59’46.5” 117°54’21.0” Tuff outcrop Kaolinite 66 37°59’42.5” 117°53’59.8” Tuff outcrop Kaolinite 67 37°59’40.5” 117°53’52.3” Tuff outcrop Kaolinite 68 37°59’41.9” 117°53’50.6” Tuff outcrop Kaolinite 69 37°59’43.0” 117°53’45.6” Outcrop Calcite 70A 37°59’44.6” 117°53’43.0” Strike-slip fault Kaolinite 70B 37°59’44.5” 117°53’43.3” Strike-slip fault 70C 37°59’44.4” 117°53’43.6” Strike-slip fault
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Appendix II. Location of Figures within the Text
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Appendix III. ASD Field and Laboratory Spectra
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