PROCEEDINGS, 43rd Workshop on Geothermal Reservoir Engineering
Stanford University, Stanford, California, February 12-14, 2018
SGP-TR-213

Overview and Preliminary Results from the PoroTomo project at Brady Hot Springs, Nevada: Poroelastic Tomography by
Adjoint Inverse Modeling of Data from Seismology, Geodesy, and Hydrology
Kurt L. FEIGL
Department of Geoscience, University of Wisconsin-Madison, 1215 West Dayton Street, Madison, WI, 53706 United States
feigl@wisc.edu

The PoroTomo Team, including Elena C. REINISCH(1), Jeremy R. PATTERSON (1), Samir JREIJ(7),Lesley PARKER(1), Avinash NAYAK(1), Xiangfang ZENG(1,9), Michael A. CARDIFF(1), Neal E. LORD (1),Dante FRATTA(1), Clifford H. THURBER(1), Herbert F. WANG(1), Michelle ROBERTSON(2),Thomas COLEMAN(3), Douglas E. MILLER(3), Paul SPIELMAN(4), John AKERLEY(4), Corné KREEMER(5),Christina MORENCY(6), Eric MATZEL(6), Whitney TRAINOR-GUITTON(7), Nicholas C. DAVATZES(8)(1) University of Wisconsin-Madison, Department of Geoscience, Madison, WI, United States;(2) Lawrence Berkeley National Laboratory, Berkeley, CA, United States;(3) Silixa, Houston, TX, United States;(4) Ormat Technologies Inc., Reno, NV, United States;(5) University of Nevada Reno, NV, United States;(6) Lawrence Livermore National Laboratory, Livermore, CA, United States;(7) Colorado School of Mines, Golden, CO, United States;(8) Temple University, Philadelphia, PA, United States;(9) State Key Laboratory of Geodesy and Earth's Dynamics,Institute of Geodesy and Geophysics, Chinese Academy of Sciences
http://geoscience.wisc.edu/feigl/porotomo/
Keywords: EGS, DAS, DTS, GPS, INSAR
Abstract
In the geothermal field at Brady Hot Springs, Nevada, subsidence occurs over an elliptical area that is ~4 km by ~1.5 km. Highly
permeable conduits along faults appear to channel fluids from shallow aquifers to the deep geothermal reservoir tapped by the
production wells. Results from inverse modeling suggest that the deformation is a result of volumetric contraction in units with depth
less than 600 m [Ali et al., 2016]. Characterizing such structures in terms of their rock-mechanical properties is essential to successful
operations of Enhanced Geothermal Systems (EGS). The goal of the PoroTomo project is to assess an integrated technology forcharacterizing and monitoring changes in the rock-mechanical properties of an EGS reservoir in three dimensions with a spatial
resolution better than 50 meters. In March 2016, we deployed the integrated technology in a 1500-by-500-by-400-meter volume at
Brady Hot Springs. The data set includes: active seismic sources, fiber-optic cables for Distributed Acoustic Sensing (DAS) andDistributed Temperature Sensing (DTS) arranged vertically in a borehole to ~400 m depth and horizontally in a trench 8700 m in lengthand 0.5 m in depth, 244 seismometers on the surface, three pressure sensors in observation wells, continuous geodetic measurements atthree GPS stations, and seven InSAR acquisitions. The deployment consisted of four distinct time intervals ("stages"). Between eachmeasurement interval, the hydrological conditions were intentionally manipulated by modifying the rates of pumping in the injection
and production wells. To account for the mechanical behavior of both the rock and the fluids, we are developing numerical models forthe 3-dimensional distribution of the material properties. In this paper, we provide a snapshot of work in progress, including thehighlights listed in the Conclusions below. The work presented herein has been funded in part by the Office of Energy Efficiency and
Renewable Energy (EERE), U.S. Department of Energy, under Award Number DE-EE0006760.
1. INTRODUCTION
In the geothermal field at Brady Hot Springs, Nevada, subsidence occurs at a rate of the order of a centimeter per year over an ellipticalarea that is ~4 km by ~1.5 km, as measured by satellite interferometric synthetic aperture radar (InSAR) and mapped in Figure 1.
Results from inverse modeling suggest that the deformation is a result of volumetric contraction in units with depth less than 600 m. [Aliet al., 2016]. Highly permeable conduits along faults appear to channel fluids from shallow aquifers to the deep geothermal reservoir
tapped by the production wells, as sketched in Figure 2.
The objective of the PoroTomo project is to assess an integrated technology for characterizing and monitoring changes in an enhanced
geothermal system (EGS) reservoir in three dimensions with a spatial resolution better than 50 meters. The targeted characteristics
include: saturation, porosity, Young’s modulus, Poisson’s ratio, and density, all of which are “critically important” to a viable EGS
reservoir (DOE GTO, 2014). Estimating these parameters and their uncertainties will contribute to the overarching goal ofcharacterizing the reservoir in terms of its effective permeability and/or fracture transmissivity. The technology performance metric forthe project is resolution in meters of a feature in the modeled 3-D distribution of a rock mechanical property (e.g., Poisson’s ratio), as
determined by the dimension of a visible checkerboard pattern at 200 m depth in a test using simulated data. Resolution is controlled by:
the number of parameters to be estimated in the inverse problem, the number of measurements, and the distribution of the sensors. For
1

Feigl and PoroTomo teamseismic data, the wavelength and distribution of the sources also play crucial roles. During Phase I of the project, we accomplished a
proof of concept. We have validated the computational analysis techniques by adapting and applying them to existing data sets [Ali etal., 2016; Lancelle, 2016; Lord et al., 2016; Zeng et al., 2017a].
Figure 1. Map
showing location of
the Brady Hot
Springs geothermal
field, with faults(thin black lines,[Faulds et al., 2010],
surfacehydrothermal
activity, including
fumaroles (yellow
circles), warm
ground (yellow
squares), and silica
deposits (magenta
circles) from precise
field mapping[Coolbaugh et al.,2004]. Injection
wells are shown by
blue triangles andproducing wells areshown by red
triangles. Fiducial
crosses indicate
1000-meter grid ineasting and northingof the Universal
Transverse Mercator(UTM) projection(Zone 11). The SARinterferogram in the
background shows
the change inwrapped phase overthe 308-day intervalfrom December 24,
2011 to October 27,
2012. One coloredfringe corresponds
to one cycle of phasechange, or 16 mm ofrange change. The
dotted and dashedgrey line delimits
the broad subsiding
zone. The black
rectangle delimits
the study area of the
PoroTomo project.

2

Feigl and PoroTomo teamFigure 2. Sketch of vertical cross section,
showing the key idea that highly permeable
conduits along faults channel fluids fromshallow aquifers to the deep geothermal
reservoir tapped by the production wells.
Vertical cross section based on geologic
model of Jolie, Moeck and Faulds and geologic
mapping by Faulds [Faulds et al., 2004; Faulds
et al., 2006; Faulds et al., 2011; Shevenell etal., 2012; Jolie et al., 2015]. Elevation in km.
V:H = 1:1.

2. DEPLOYMENT AT BRADY HOT SPRINGS DURING MARCH 2016

12

13

14

2016-03-22

Wednesday 2016-03-23

2016-03-24

2016-03-25

ThursdayFriday2016-03-20
Sunday10

Tuesday

2016-03-19
Saturday9

2016-03-21

2016-03-18
Fridayday of week8

Monday2016-03-17
Thursday2016-03-15
Tuesday7

2016-03-14

6

Monday5

2016-03-13

4

Sunday3

2016-03-12

2

Saturday1
2016-03-11

beforeFridayDay of experiment
Calendar Date

Wednesday 2016-03-16

In Phase II of the project, we are working to demonstrate a prototype of an integrated technology at the EGS field at Brady Hot Springs,
Nevada. The study area is a shallow volume with length ~1500 m, width ~500 m, and depth ~400 m, as delimited by the black rectangle
in Figure 1. In March 2016, we deployed the proposed technology during four distinct time intervals, as illustrated in Figure 3. Between
each measurement interval, the hydrological conditions were intentionally manipulated by modifying the rates of pumping in theinjection and production wells. By comparing the four sets of results, we expect to quantify any temporal changes in the characteristicsof the study volume.
11

15

after

Stage 1: Normal operations
Stage 2: shutdown- stop injection and production
Stage 3: divert all injection to infield wells
Stage 4: Resume normal operations

Expected water level

Acquire SAR Image
Operate Active Seismic Source
Operate seismometers
Operate DAS and DTS
Operate Pressure Sensors

Figure 3. Schedule of operations during deployment at Brady Hot Springs in March 2016, showing pumping operations(upper rows), expected level of groundwater (arrows), and data streams (lower rows).
We are analyzing measurements from three data sets: (1) seismic waveforms recorded by seismometers and distributed acoustic sensors(DAS); (2) the deformation of the Earth’s surface recorded by satellite geodesy, including the Global Positioning System (GPS) andInterferometric Synthetic Aperture Radar (InSAR); and (3) time series of hydraulic pressure, flow, and temperature measured in wells
for production, injection, or observation. The details of the data sets are described elsewhere [Feigl and PoroTomo_Team, 2017].
3

Feigl and PoroTomo team
Below, we update that report. All of the data became available to the public on October 1st, 2017 at ftp://roftp.ssec.wisc.edu/porotomo.
Metadata describing each of these data sets are available at the Geothermal Data Repository (GDR):
https://gdr.openei.org/search?q=porotomo&submit=Search.

T191
T190
T189
T188

-100

T143

T119

T95

T166

T142

T118

T94

T165

T141

T117

T164
T163

T186

T162

T184

300

T167

T187

T185

100

T140
T139

T161

T138 81B-1
T137

T160

T136

T183

T159

T135

T182

T158

T134

T181

T157

T133

T180

T156

T132

T116

500

T71

T47
T46

T93

T69

T45

T92

T68

T44

T67

T43

T66

T42

T65

T41

T91

T114

T90
T89
T88

1400

1200

1000

T64

T40

T63

T39

T86

T62

T38

T85

T61

T37

T87

T84

T60

T83

T59

T36
T35

T179

T155

T131

T178

T154

T130

T82

T58

T34

T177

T153

T129

T81

T57

T33

T176

T152

T128

T80

T56

T175

T151

T127

T79

800

600
T9

T150

T126

T78

T54

T173

T149

T125

T77

T53

T172

T148

T124

T76
SP-2

T52

T171

T147

T123

T51

T170

T146

T122

T50

T8

T32
T31

T55

T174

GPS BRD1DASh
DASv (well 56-1)
Nodal
ReftekVibroseisObs. wells

T7

400

T30 T6
T29

200

T28
T27
T26

0

T49

56A-1

900

T48

T70

T115

700

SW <---- location in Brady rotated Y coordinate [m] ----> NE-300

Brady Deployment Observation Locations
NW <---- location in Brady rotated X coordinate [m] ----> SEN

Figure 4. Map of study area, showing volume targeted for tomography (gray shading), vibroseis points (blue hexagons, labeledTnnn), Nodal seismometers (red diamonds), Reftek seismometers (green crosses), fiber-optic cable (black line) for distributed
acoustic sensing (DAS) and distributed temperature sensing (DTS), GPS station BRD1 (yellow triangle). A separate fiber opticalcable for DAS and DTS was deployed vertically in the borehole of Well 56-1 (blue star). The Y-axis of the rotated coordinate
system is approximately parallel to the northeast-striking fault system.

4

Feigl and PoroTomo teamIn the following sections, we describe the networks of instrumentation deployed in March 2016. The 3-dimensional position of each
instrument was surveyed with centimeter-level accuracy using Real Time Kinematic (RTK) GPS. In addition, we present somepreliminary results from each data set.
3. HYDROLOGYIn March 2016, we monitored pressure and temperature at several wells. The details of the instrumentation as well as the metadata
describing the monitoring boreholes are available on the GDR [Lim, 2016]. Briefly, the monitoring locations consisted of a shallow wellnear where water is re-injected (81B-1), a deep well near the main area of production (56A-1), and a well located halfway between thesetwo points (SP-2) [Lim, 2017]. In addition, pumping data, including flow rates, temperature and pressure are available for the production
and injection wells that were in operation during the deployment [Akerley, 2016].
To analyze the hydrological data sets, we are estimating reservoir properties from the time series of pressure data. Specifically, we seek
to estimate hydraulic conductivity K and specific storage Ss. Multiple conceptual models are being tested to determine the necessarylevel of model complexity required to fit the collected data. The numerical groundwater flow model is developed using MODFLOW[Harbaugh, 2005]. The model domain is 7 km x 13 km x 6 km (x, y, z). The model is oversized to prevent boundary effects on the
numerical solutions, with model boundaries determined using simulated dipole pumping tests and determining the distance at which
drawdown is less than 10 cm. The model takes an equivalent porous medium approach and assumes that at a large enough scale, thereservoir can be modeled as a non-fractured porous medium with effective hydraulic properties.
To determine these effective hydraulic properties, parameter estimation is conducted using PEST [Doherty, 2001], a common parameter
estimation software package that interfaces nicely with MODFLOW. The parameters to be estimated are K_x, K_y, K_z, and S_s. The
reservoir is assumed to be isotropic; therefore, K_x = K_y. K_z is tied to K_x so that each parameter will change in the same direction
by the same value. This reduces the number of adjustable parameters per zone from 4 to 2, reducing the model run times and the overall
inversion time. There is no regularization imposed on the inversion. The current conceptual model that provides the best fit to theobserved data consists of 3 hydraulic zones. These zones are grouped based on identified lithologic units [Jolie et al., 2012; Siler et al.,2016] that are assumed to have similar hydraulic properties. Zone 1 (purple) consists of Tertiary sedimentary deposits, Zone 2 (yellow)
consists of grouped Tertiary lacustrine sedimentary deposits, and Zone 3 (green) consists of grouped volcanic and metamorphic
basement rock. Pressure data collected during plant shutdown (Stage 2) are used for the parameter estimation. Five points, out of morethan 6000 data points, are chosen for fitting with PEST. The best-fitting estimated parameters and associated misfit are shown in Figure
5. More details have been described by Patterson et al. [2017a].

Figure 5. Results of hydrogeological analysis, showing: At upper left, pressure changes (as meters of water) as observed (solid
blue curve) and simulated (dashed red curve) in two different wells. At right: two vertical slices of the model immediately prior
to shutdown showing effect of extraction and injection on initial pressures with contours of hydraulic head in meters. Strikenormal fiducial point is (Xp= 0, Zp= -0.598). Strike-parallel fiducial point at (Yp= 0, Zp= 0.649). At lower left, table showing
estimated values of hydraulic conductivity and specific storage for each of the three zones in the model.
5Feigl and PoroTomo team
4. GEODESYAs described previously, the geothermal field at Brady Hot Springs, Nevada has subsided over the past decade [Ali et al., 2016; Reinischet al., 2017; Reinisch et al., in prep]. Between 2004 and 2014, the rate of downward vertical displacement was of the order of 10
mm/year, as measured by geodetic techniques: Interferometric synthetic aperture radar (InSAR) and Global Positioning System (GPS).
Here we consider the deformation field mapped by InSAR data spanning 2016-July-22 to 2017-August-22 (Figure 6). The observed
deformation field forms an approximately elliptical bowl that is 4 km long and aligned with the trace of the normal fault system thatstrikes NNE, as shown in Figure 6.

Figure 6. InSAR data spanning 2016-July-22 to 2017-August-22, showing deformation in unwrapped range change rate
[mm/yr]: as observed by the TerraSAR-X satellite mission (left), as modeled under the hypothesis of thermal
contraction (center), and the residual field as calculated from the observed field minus the modeled field (right).
[Reinisch et al., 2017; Reinisch et al., in prep].
We also GPS measurements of displacement [Reinisch et al., 2017; Reinisch et al., in prep]. Three GPS stations operated continuously
during the deployment. Station BRD1 was installed on the head of completed Well 18-1 on 10 March 2016. It is located inside the study
area, near the center of the area undergoing rapid subsidence, as mapped by InSAR. GPS stations BRAD and BRDY are located outside
the study area at distances of approximately 5 and 3 km from BRD1, respectively. All three GPS stations are part of the MAGNET
network operated by the Geodesy Lab at the University of Nevada in Reno. The procedures for analyzing the data have been described
previously [Blewitt et al., 2013].
To validate the InSAR measurements of deformation, we compare them to the GPS measurements. We convert the GPS measurementof the displacement at BRD1 with respect to BRDY to range change by taking the negative scalar product with a unit pointing vector
from the pixel on the ground toward the satellite. We use the location of BRDY to characterize the deformation in the far field. From theinterferogram in Figure 6, we select a set of pixels the southeast corner of the study area, where little deformation is observed. Similarly,
we select another set of pixels in the subsiding bowl near BRD1. The difference between the InSAR range change values averaged overthese two sets gives the component of relative displacement along the line of sight from the satellite to the ground. The results listed
Table 1 indicate that the difference between the two types of measurements is insignificantly different from zero with 95 percent
confidence [Reinisch et al., 2017; Reinisch et al., in prep].
Table 1. Comparison of range change values between
points located at stations BRD1 and BRDY as measured by
GPS and InSAR on 2016-July-22 to 2017-August-22[Reinisch et al., 2017; Reinisch et al., in prep].

To simulate the deformation field, we use a formal inversion to estimate the parameters in a model of contracting dislocations buried ina half space with uniform elastic properties [Reinisch et al., in prep]. Using Bayesian inference, we test two hypotheses to explain the
deformation field: (1) declining pore-fluid pressure and (2) thermal contraction. As shown in Figure 7, the model that best fits the
InSAR data spanning the time interval between 22 July 2016 and 22 August 2017 estimates the rate of volume change in the modeled
6

Feigl and PoroTomo teamreservoir to be (–2.9 ± 0.1) × 104 cubic meters per year, the total volume of the cooling part of the modeled reservoir to be 1.1 × 108
cubic meters, and the rate of thermal work in the cooling part of the reservoir to be −56 to −75 megawatt.
Figure 7. Results of inverse modeling of InSAR
data under the hypothesis of thermal contraction,
showing the values of estimated parameters ofthermal strain rate per voxel (from inversion witha temperature-defined model) color-coded andinterpreted in terms of volume change rate andthermal energy change rate. Black filled circles
are fumarole locations. Dashed lines are faults.
Inverted triangles are injection well locations.
Upright triangles are production well locations.
Area where PoroTomo focused on during
deployment is shown with rectangular outline.
Letters correspond to cross sections done from
3D analysis (not shown or discussed here).

5. SEISMOLOGY
Instrumentation
A crew from the University of Texas-Austin operated a vibrating source named T-REX. The vibration protocol included three sweeps
for each of P, transverse S, and longitudinal S at each of approximately 200 locations during each of the four stages of the 15-day field
experiment. Using associated coder/decoder telemetry units, the vibroseis truck operated in synchronization with the DAS array and theconventional seismometers. For each of the three modes, the vibroseis source made three sweeps over 20 seconds. Each sweep increasedin frequency from 5 Hz to 80 Hz.
The seismic instrumentation included 238 Zland 3-component sensors manufactured by FairfieldNodal. Each of these instruments(dubbed “Nodals”) has a corner frequency of 5 Hz, a 24-bit digitizer with a dynamic range of 127 dB, and a timing accuracy of±10 microseconds from a GPS receiver [FairfieldNodal, 2015]. Between March 11 and March 26, 2016, these instruments operated andrecorded autonomously and continuously with sampling interval of 0.002 s (i.e. a sampling rate of 500 samples per second) for theentire two-week deployment without any human intervention. Arrays of single-component, 10-Hz Nodal instruments have been utilized
for many scientific projects, including at Long Beach, CA [Lin et al., 2013], but the PoroTomo experiment is one of the first academic
uses of the newer three-component 5-Hz Nodal instruments.
The seismic network also included six 3-component seismic stations from the national instrumentation program named Portable Array
Seismic Studies of the Continental Lithosphere (PASSCAL). Each station included an L28 model, 4.5-Hertz, 3-component geophone,
Reftek RT130 seismograph, and a GPS timing clock.
We also used Distributed Acoustic Sensing (DAS) — the first such deployment in a geothermal field in North America. The“intelligent” Distributed Acoustic Sensing (iDASTM) system uses a continuous length of fiber-optic cable as an array of seismic sensors[e.g., Parker et al., 2014]. This technology transmits pulses of light into the fiber and records the response from its Rayleigh backscatter.
The resulting measurement is sensitive to the rate of transient strain along the axis of the cable. At Brady, the instrumentation included
8700 meters of DAS cable buried horizontally in a shallow trench and 400 meters of DAS cable hanging vertically in Well 56-1. The
DAS data were recorded continuously, archived in 30-second files in SEG-Y format [Barry et al., 1975]. More than 60 Terabytes of
DAS data were collected over the four stages of the PoroTomo experiment in March 2016. To understand the DAS recordings, we
review a few of their characteristics [Bakku, 2015; Daley et al., 2015]. DAS measures the strain rate 𝜀̇ in the fiber by averaging its
elongation over a segment of cable (called the “gauge length”) during a temporal sampling interval. The elongation represents the phaseshift of the backscattered light pulse. These data were written with dimensions of radians per millisecond in the SEG-Y files. In thisDAS system, one radian of phase change corresponds to 116 nanometers of elongation. The wavelength of the laser light is 1550
nanometer. The temporal sampling interval was set to 1 millisecond and the spatial sampling length was set to 1 meter. The spatial
resolution of the DAS strain rate measurement equals the gauge length of 10 m. Further comparisons of the ground motions recorded by
the two arrays (DAS cable and Nodal seismometers) are underway [Wang et al., 2016; Wang et al., submitted 2017/10/03].
7

Feigl and PoroTomo teamP-wave velocity from 3-D seismic tomography
We have performed 3-dimensional seismic tomography to estimate P-wave velocity [Parker, 2017; Thurber et al., 2017]. The crosscorrelation method was utilized to remove the sweep signal from the geophone and DAS records. The first P arrivals were automaticallypicked from the cross-correlation results using a combination of methods, and the travel times were used to invert for the 3D P-wave
velocity structure. Models with horizontal node spacing of 50 m and 100 m were obtained, with vertical node spacing of 10 to 50 m.
The travel time data were fit to about 30 ms, close to our estimated picking uncertainty. As seen in Figure 8, the boundaries between
high and low velocity zones agree with previous surveys of local faults. Low velocity zones near the surface correspond to fumarole
locations.

1 | US DOE Geothermal Office

Yp = 400 m

Yp = 1000 m

Yp = 600 m

Yp = 1200 m

eere.energy.gov
1

Figure 8. P-wave velocity estimated from body-wave tomography [Thurber et al., 2017]. Each panel shows a vertical slice
perpendicular to the NE-SW strike of the fault system. Black lines show faults tessellated from a geologic model [Jolie et al.,2015] based on field observations .
To evaluate the performance metric of spatial resolution of the estimated P-wave velocity, we performed a checkerboard test [Thurber etal., 2017]. The results appear in Figure 9. For the 100-meter checkerboard test, the velocity at 200 m depth was recovered within 1% of
the true value for 69% of the natural lab area, and for the central part of the natural lab, recovery was within 0.1% on average. For the
50-meter checkerboard test, the velocity at 200 m depth was recovered within 1% of the true value for 32% of the natural lab area, andfor the central part of the natural lab, recovery was within 0.2% on average.

8

Feigl and PoroTomo teamFigure 9. Results of a checkerboard test of resolution of P-wave velocity at 200 m depth, showing, from left to right, input (true)
values with 100-meter spacing, output (recovered) values with 100-meter spacing, input (true) values with 50-meter spacing, andoutput (recovered) values with 50-meter spacing.
Sweep Interferometry
Sweep interferometry uses the energy from the vibroseis sweeps as sources of high frequency energy [Matzel et al., 2017b]. The data
recorded at one seismometer are correlated with the data recorded at another to obtain an estimate of the Green's function between the
two. Figure 10 shows the values of several material properties in a horizontal slice at a depth of 225 m as inferred by this method.
Further details are presented at this meeting [Matzel et al., 2018].

Figure 10. Material properties at a depth of 225 m below mean ground surface estimated using sweep interferometry: From left:P-wave velocity, S-wave velocity, Poisson’s ratio, and Young’s modulus (calculated using the density values from a gravimetricstudy [Witter et al., 2016]). Black lines show faults tessellated from a geologic model [Jolie et al., 2015] based on fieldobservations .
9

Feigl and PoroTomo team
MASW on NCFs from DAS data
Using the DAS data recorded continuously over hours, we have calculated Noise Correlation Functions [Zeng et al., 2017b]. These
functions are then inverted via Multichannel Analysis of Surface Waves (MASW) to estimate shear-wave velocity at shallow depth. The
results appear in horizontal slices in Figure 11. The uppermost deposits are very heterogeneous and include diatomaceous earth, silt,
sand, and hardened silica associated with the presence of fumaroles, as observed by field mapping [Faulds and Garside, 2003;
Coolbaugh et al., 2004; Faulds et al., 2004; Faulds et al., 2010].

Horizontal slices at depth:
10 m

20 m

30 m

50 m

Vs
[m/s]

Figure 11. Shear-wave velocity estimated from Multichannel Analysis of Surface Waves (MASW) on Noise Correlation
Functions (NCFs) derived from Distributed Acoustic Sensing (DAS) data. Horizontal slices at four different depths are shown.

DAS data recorded in a borehole
We consider the vibration response as a function of depth and time from sweeps at two different vibroseis stations recorded by thevertical DAS array in the borehole of Well 56-1. The raw DAS data were converted to fiber strain and correlated with the accelerometer
response of the vibrating baseplate in the vibroseis truck. In particular, we compare the responses from two vibroseis stations with about
260 m offset distances but on opposite sides of the well. Both records show compressional arrivals, some of which may be reflections.
The strong blue amplitudes indicate a down-going shear signal. An up-going shear signal converted from a down-going P wave at aboundary near 300-m depth is also apparent. The compressional signals match the modeled values more closely than do the shear-wave
arrivals. These disagreements are likely caused by differences in the near-surface properties in the vicinity of the two source locations.
More details are presented at this meeting [Miller et al., 2018].

10

Feigl and PoroTomo teamDistributed Temperature Sensing
The same fiber optic cables also performed distributed temperature sensing – DTS [e.g., Coleman, 2013]. At Brady, the DTS data havebeen analyzed and published [Patterson et al., 2017b]. Further details are presented at this meeting [Patterson et al., 2018].
CONCLUSIONS
Body-wave seismic tomography achieved the target resolution of 50 meters for P-wave velocity at 200 m depth for much of the naturallab using P-wave arrival times picked from the waveforms recorded by Fairfield Nodal seismometers and Distributed Acoustic Sensing(DAS) in fiber-optic cable [Parker, 2017; Thurber et al., 2017].
Seismic sweep interferometry using data recorded by geophones during active source sweeps estimated a 3-D model of Vs, Vp, Q, andPoisson's ratio with a resolution of the order of ~100 m at a depth of 200 m [Matzel et al., 2017a; Matzel et al., 2017b; Matzel et al.,2018].
Deploying DAS effectively quadrupled the number of seismic sensors, provided DTS capability simultaneously, and demonstrated thevalue of the technology for seismic characterization of a geothermal reservoir.
Shear-wave velocities estimated from surface wave dispersion curves based on Noise Correlation Functions (NCF) from DAS data
showed horizontal variations with resolution ~100 m and vertical variations with resolution of ~10 m at depths less than 50 m.
Analysis of InSAR data spanning 2004-2017 using a dislocation model estimates the rate of volume change to be (29 ± 1) thousand
cubic meters per year and the rate of thermal work in the cooling part of the reservoir to be −56 to −75 megawatt.
GPS data at 3 stations BRDY, BRAD and BRD1 from 2016 through 2017 have been collected, archived, distributed, and analyzed toyield time series of daily estimates of relative, 3-dimensional position [https://gdr.openei.org/submissions/999].
Structured parameter estimation on pump testing data estimates hydraulic conductivity and storage coefficient with a spatial resolution
comparable to the 500-meter distance between sensors [Patterson et al., 2017a].
The DTS data set has been analyzed to characterize heat flow within the reservoir during production and injection with a verticalresolution of the order of 0.1 meter [Patterson et al., 2017b].
Data on pressure, temperature, production, and injection at Brady for the time interval 2004-2014 favor the hypothesis that injecting
cooled water causes thermal contraction as the dominant process driving the deformation field observed by geodetic data [Reinisch etal., in prep].
ACKNOWLEDGMENTS
We are extremely grateful to Fan-Chi Lin (University of Utah), Amanda Thomas (University of Oregon), and Marianne Karplus(University of Texas-El Paso) for contributing their Fairfield Nodal Zland 3-component sensors to our project. We thank all those who
lent helping hands in the field (sorted in reverse alphabetical order by given name): Xuyang Liu, Xiangfang Zeng, Thomas Coleman,Tanner Whetstone, Stoyan Nikolov, Sin-Mei Wu, Scott Nelson, Robert Kent, Rob Skarbek, Paul Spielman, Neal Lord, Mike Cardiff,Michelle Robertson, Marianne Karplus, Lesley Parker, Kurt Feigl, John Akerley, Joe Greer, Janice Lopeman, Herb Wang, Elizabeth
Berg, Dante Fratta, Craig Stenzel, Corné Kreemer, Cliff Thurber, Chelsea Lancelle, Cecil Hoffpauir, Bret Pecoraro, Bill Foxall, Ben
Duggan, Athena Chalari, Andy Valentine, Amanda Thomas, and Alex Jensen. We also thank Jeff Wagoner, Drew Siler, Nicholas Hinz,
and James Faulds for assistance with their geologic models.
Raw Synthetic Aperture Radar (SAR) data from the ERS, and Envisat satellite missions operated by the European Space Agency (ESA)
are copyrighted by ESA and were provided through the WInSAR consortium at the UNAVCO facility. SAR data from the ALOS
satellite mission operated by the Japanese Space Agency (JAXA) were acquired from NASA’s Distributed Active Archive Center at the
Alaska Satellite Facility (ASF). SAR data from the TerraSAR-X and TanDEM-X satellite missions operated by the German Space
Agency (DLR) were acquired through Research Project RES1236.
Elena C. Reinisch was supported by the National Science Foundation Graduate Research Fellowship under grant DGE-1256259. The
work presented herein was funded in part by the Office of Energy Efficiency and Renewable Energy (EERE), U.S. Department ofEnergy, under Award Numbers DE-EE0006760 and DE-EE0005510.
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