1 of 16

PROCEEDINGS, Thirty-Eighth Workshop on Geothermal Reservoir Engineering

Stanford University, Stanford, California, February 11-13, 2013

SGP-TR-198

PRELIMINARY INVESTIGATION OF RESERVOIR DYNAMICS MONITORED THROUGH

COMBINED SURFACE DEFORMATION AND MICRO-EARTHQUAKE ACTIVITY:

BRADY’S GEOTHERMAL FIELD, NEVADA

Nicholas C. DAVATZES1, Kurt L. FEIGL

2, Robert J. MELLORS

3, William FOXALL

3,

Herbert F. WANG2, and Peter DRAKOS

4

1Temple University1901 N. 13 Street;

EES, Beury Hall, Rm. 307

Philadelphia, PA 19122

e: Davatzes@temple.edu, T: 215-204-2319 2University of Wisconsin Madison,

3Lawrence Livermore National Laboratory,

4ORMAT

ABSTRACT

Fluid pressure change accompanying pumping in

the Bradys Geothermal Field is associated with two

complimentary deformation responses: (1) surface

deformations sensible by InSAR corresponding to

thermo-poroelastic deformations resulting in volume

changes and (2) seismic slip on fractures induced by

either changes in effective normal stress or solid

stress with minimal impact to volume but potential

impact on permeability structure due to dilation or

compaction. We present an integrated data set that

compares the impulse from pumping to the

deformation response in order to investigate the

coupling of these behaviors, and to constrain the

geometry and rheology of the reservoir and

surrounding crust.

We find that surface deformation is strongly

associated with the location of the production wells,

with associated subsidence concentrated within a

bend in the Bradys normal fault. A broader region of

minor subsidence extends several kilometers to the

NNE and SSW along the fault trend. Interestingly,

MEQs are absent within the region of intense

subsidence where effective stress stabilizing fractures

are expected to be high but are broadly distributed in

and around the margins of the region of minor

subsidence below the reservoir depth. MEQs are also

densely distributed along the Bradys fault from

injectors to producers where effective stresses are

expected to be lower. Initial modeling indicates that

the pattern of subsidence is consistent with fluid

extraction along a vertical conduit from shallow

depths to approximately 1 km within the fault bend

and then extraction at ~1 km along the entire length

of the mapped Brady‟s fault indicating a reservoir

much larger than would be expected from the

footprint of the production wells. The planned EGS

stimulation well 15-12ST1 is located SSW of this

permeable volume associated with production along

the direction of SHmax , which is also the anticipated

direction of stimulation growth, and east of the

deeper reservoir volume in a low permeability

volume adjacent to the reservoir. Although the spatial

patterns provide relatively clear insights, we find that

temporal correlations are complex and that both

surface deformation and seismicity rates are highly

variable in time, but that simple correlations to

changes in pumping rate, which varies by ~15-20%,

are not yet obvious. This complexity, and resulting

limitations on predicting responses, is expected given

the long history of stressing by production and

geologic complexity and will be addressed in a future

study modeling the pressure diffusion due to

pumping and related deformation response.

INTRODUCTION

Management or stimulation of a geothermal

reservoir involves manipulating the fluid pressure at

depth to exploit, maintain, or create permeability.

Because relatively few tools are available to map the

permeability distribution, most of the reservoir

volume is poorly characterized with respect to fluid

flow. Stimulation via fluid injection at wells causes

two distinct effects throughout the reservoir and

beyond: (1) slip on fractures that can often be

detected as seismic events using seismology and (2)

expansion or contraction that can be detected as

deformation of the ground surface using geodesy.

The use of seismicity to map an evolving permeable

network presumes that the percolation of fluid

pressure initiates shear failure of fractures due to the

reduction of normal stress resulting in slip and/or

creates new opening-mode fractures, and that these

failure events are sensible through microseismic

monitoring thus revealing the extent of the volume

sustaining large fluid pressure changes. However, the

displacement of fracture walls may be either aseismic

or below the detection threshold of the local seismic

network, thus limiting the completeness of MEQ

mapping of fluid pressure variation. In addition,

pumping induces volume changes in the rock add an

additional source of stressing that further

complications the relationship between MEQs and

the transport of fluids and pressure within the

reservoir.

These same volume changes resulting from fluid

injection and extraction are manifested as surface

deformation that can be measured by Interferometric

Synthetic Aperture Radar (InSAR). The resulting

geodetic measurements provide an independent

constraint on models for the deforming reservoir.

By comparing these two responses to pressure

variation due to pumping in the reservoir, we

investigate three key issues:

1. Does the seismicity coincides with volume

changes at depth due to fluid pressure

fluctuations inferred from surface

deformations?

2. What proportion of the deformation observed

at the surface can be correlated to sensible

seismic processes?

3. How do pressure change, seismicity, and

permeability structure correlate in time?

At the Brady‟s geothermal field, we have begun

testing these relationships using the time series of

injection and extraction forcing the reservoir with

time series of micro-seismic events recorded by a

local seismic network and corresponding

measurements of surface deformation from InSAR.

In this paper, we investigate the first two issues

during a time interval of normal reservoir activity

from October 2010 through January 2013, and

preceding the planned EGS stimulation of well 15-

12ST1.

GEOLOGIC SETTING

The Brady‟s Geothermal Field is located in

western Nevada within a region of distributed

extension associated with the NE terminus of the

Walker Lane (Faulds et al., 2011; see summary in

Blake and Davatzes, 2012). The reservoir is located

along a complex series of normal faults (Faulds and

Garside, 2003; Faulds, 2010) at a prominent bend,

which is also associated with surface hydrothermal

activity (Coolbaugh et al., 2004) (Figure 1).

Production wells are generally clustered at this bend,

with injection wells primarily located to the NE along

the fault trace associated with surface hydrothermal

activity.

Standard production and injection of the

reservoir has been carried from 1992 to present. It is

characterized by pressure drawdown with minimal

cooling and subsidence. Six production wells and 2

primary injection wells are distributed over a 1.4 km

region along the fault with an average depth of 930

m; production wells range in depth from 400 m to

1770 m (Figure 1). Vertical displacements associated

with past and current production (Figure 1) center on

production wells and appear to extend over a 7 km

region, extending significantly beyond the surface

expression of the Brady‟s fault system but aligned

with it (Oppliger et al., 2006). Since 2010, seismicity

(Figure 1), albeit at low rates, has been detected by a

local seismometer array managed by Lawrence

Berkeley National Laboratory as part of the EGS

preparation (Nathwani et al., 2011) and deployed to

maximize sensitivity around the planned 15-12ST1

EGS well.

Early in 2013, a hydraulic stimulation is planned

in well 15-12ST1 to extend the reservoir, which will

be characterized by long-duration, variable pressure

injection, and once on-line, long-term low pressure

cold-water injection. This might be accompanied by

microseismicity indicating pore pressure invasion of

natural fractures and limited pore pressure rise in the

matrix porosity. Following stimulation, any newly

stimulated permeable network will experience pore

pressure perturbations from both injection and

production as it is incorporated into the reservoir.

DEFORMATION

The surface deformation measurements in this

study use an imaging geodetic technique called

InSAR (Interferometric Synthetic Aperture Radar).

This technique uses pairs of satellite radar images

taken at different times to infer the relative change in

surface position during the intervening period. The

change is calculated from the interference pattern

caused by the difference in phase between the two

images acquired by the space-borne radar sensor at

two distinct times. As the InSAR measurements are

made with respect to the line-of sight to the satellite,

the measurement includes both vertical and

horizontal components of surface motion.

Unfortunately, although the sensitivity to both

components can be calculated for a specific sensor, it

is not possible to uniquely distinguish vertical and

horizontal components with only a single

measurement. These results provide a high spatial

sampling density (~100 pixels/km2). Errors are

typically due to poorly imaged (decorrelated) areas,

atmospheric artifacts caused by variations in water

vapor, satellite orbit errors, or inaccuracies in the

digital elevation model used to make corrections.

3 of 16

Fig

ure

1:

Bra

dys

Fa

ult

, S

eism

icit

y, a

nd

Su

bsi

den

ce M

ap

s:

(a)

Geo

log

ic i

nd

ex m

ap

an

d s

tres

s m

od

el.

Th

e fa

ult

ma

p i

s fr

om

Fa

uld

s (2

01

0)

and

su

rfa

ce

hyd

roth

erm

al

act

ivit

y is

fro

m C

oo

lba

ugh

et

al.

(2

00

4).

Als

o i

nd

ica

ted

are

th

e lo

cati

on

s o

f in

ject

ion

and

pro

du

ctio

n w

ells

, a

nd

EG

S w

ell

15

-

12

ST

1.

Th

e st

ress

mo

del

is

fro

m M

oo

s (u

np

ub

lish

ed r

epo

rt,

20

11

) a

nd

ref

lect

s th

e a

na

lysi

s o

f in

sit

u r

ock

str

eng

th,

a d

ensi

ty l

og

, a

n

equ

ilib

rate

d P

TS

lo

g,

dri

llin

g-i

nd

uce

d b

ore

ho

le d

efo

rma

tio

n r

evea

led

in

im

ag

e lo

gs

of

15

-12

and

15

-12S

T1

, an

d a

min

i-h

ydra

uli

c fr

act

uri

ng

test

. T

he

stre

ss m

od

el i

s su

mm

ari

zed

as

an

ou

ter

red

rin

g c

ente

red

on

15

-12

ST

1 c

orr

esp

on

din

g t

o t

he

vert

ica

l st

ress

, a

n i

nn

er b

lue

rin

g

corr

esp

on

din

g t

o t

he

sta

tic

flu

id p

ress

ure

as

a f

ract

ion

of

the

vert

ica

l st

ress

, a

nd

th

e m

ean

ho

rizo

nta

l p

rin

cip

al

stre

ss d

irec

tio

ns

+/-

1

sta

nd

ard

dev

iati

on

are

in

dic

ate

d b

y th

e th

ick

bla

ck l

ine

an

d y

ello

w w

edg

es.

Th

e le

ng

ths

of

thes

e b

lack

lin

es

sum

ma

rize

th

e sc

ali

ng

bet

wee

n

the

ho

rizo

nta

l p

rin

cip

al

stre

ss m

ag

nit

ud

es a

nd t

he

vert

ical

stre

ss.

(b)

Sei

smic

ity

det

ecte

d b

y th

e lo

cal

seis

mo

met

er n

etw

ork

. T

he

ma

p o

f

seis

mic

ity

is c

olo

r-co

ded

by

da

te a

nd

is

con

sist

ent

wit

h a

ll s

ub

seq

uen

t fi

gu

res.

(c)

Su

rfa

ce d

efo

rma

tio

ns

ma

pp

ed b

y In

SA

R.

Th

e o

utl

ine

of

the

reg

ion

su

sta

inin

g s

ub

sid

ence

(g

reen

) a

nd

in

ten

se s

ub

sid

ence

(o

ran

ge-y

ello

w)

inte

rpre

ted

fro

m t

he

inte

rfer

og

ram

in

th

e ri

gh

t-h

an

d p

an

el a

re

als

o i

nd

ica

ted

. T

he

mo

del

of

def

orm

ati

on

so

urc

es u

sed

to

in

terp

ret

the

surf

ace

def

orm

ati

on

s in

clu

des

tw

o h

ori

zon

tal

Oka

da

rec

tang

ula

r a

nd

two

Mo

gi

po

int

sou

rces

are

plo

tted

in

(b).

Ma

ps

are

in

Un

iver

sal

Tra

nsv

erse

Mer

cato

r (z

on

e 1

1)

pro

ject

ion

.

4 of 16

Measurement precision depends on the sensor

wavelength, which varies among satellites, but 10

mm is a reasonable estimate. This remote-sensing

tool has been demonstrated and validated for many

actively deforming areas, including natural

earthquakes and anthropogenic activity (e.g.,

Massonnet and Feigl, 1998). InSAR data products are

typically registered to a digital elevation model

(DEM) in geographic or cartographic coordinates to

within ~10 m. The Brady‟s area, which is relatively

arid and lacks extreme topographic variations, is well

suited for InSAR.

InSAR observations

Previous studies using 5 cm wavelength (C-

band) data (e.g. Oppliger et al., 2006) demonstrated

the usefulness of InSAR for the Brady‟s area and

suggested 10 mm per year of line-of-sight motion.

We have analyzed synthetic aperture radar (SAR)

data acquired by four previous satellite missions and

from two ongoing missions. The archival missions

include data from 1992 to 2010 and includes 61

scenes from the C-band ERS-1, ERS-2, Envisat (e.g.

McLeod et al., 1998) as well as 36 images from the

21 cm (L-band) ALOS (Rosenqvist et al., 2007).

These data sets were acquired from the WINSAR

(http://winsar.unavco.org/) and Alaska SAR facility

data centers (http://www.asf.alaska.edu/). This

represents a much larger data set than previous

studies. In general, this dataset shows a consistent

pattern of deformation over time dating back to 1992.

The current study concentrates on SAR images

covering the study area from 2011 to 2012, which

overlaps with the available seismic data. Current data

collection (3.1 cm; X-band) is from the ongoing

TanDEM and TerraSAR-X satellites (Kieger et al.,

2007; Pitz and Miller, 2010). Thirty-six scenes have

been collected in 2011 and 2012. This new data is

processed using the Diapason InSAR processing

software (CNES, 2006) developed by the French

Space Agency, CNES, as described previously

(Massonnet and Rabaute, 1993, Massonnet et al.,

1993, Massonnet and Feigl, 1998). The wrapped

phase values are filtered and resampled using a quad-

tree algorithm called PHA2QLS (Ali and Feigl, 2012,

Feigl and Sobol, 2013).

The rate of deformation is not constant. After a

preliminary analysis of the deformation, we find

especially rapid deformation during a three-month

time interval in autumn 2011, using data from the

TanDEM-X satellite mission (Krieger et al., 2007)

(Figure 2). However, during other epochs,

preliminary analysis has revealed that this

deformation rate is highly variable (not presented

here). The principal signal in the interferogram is an

elongated pattern of range change centered on

Brady‟s Hot Springs consistent with subsidence. The

total line-of-sight deformation in this interferogram is

approximately 3 cm which corresponds to 2.1 cm of

subsidence (incidence angle of 44 from vertical) and

assuming pure vertical motion occurring within the

88 day period (Figure 3).

Figure 2. Interferograms for Brady’s Hot Springs, spanning the 88-day time interval from 2011 SEP 24 to 2011 DEC

21. The panels include (a) observed phase values; (b) modeled phase values calculated from the final

estimate of the parameters in the elastic model; (b) cartoon of modeled deformation sources; (d) final

residual phase values formed by subtracting final modeled values from observed phase values; and (e)

angular deviations for final estimate. Asterisk indicates the location of centroid of the modeled sill. One

cycle of phase denotes 15.5 mm of range change. The Tandem-X orbit numbers are 6984 and 8320 in

Strip 14 of Track 167.

5 of 16

InSAR signal is not an artifact

Before interpreting the InSAR observations in

terms of deformation on the ground, we consider, and

then reject, three other possibilities.

The first possibility is that uncertainties in the

orbital trajectories for the TANDEM-X spacecraft are

responsible for the observed fringe pattern. To test

this hypothesis, we estimate the orbital parameters

from the interferometric fringe pattern using the

approach proposed by Kohlhase et al. (2003),

implemented as the General Inversion of Phase

Technique (GIPhT) by Feigl and Thurber (2009), and

extended to the gradient of wrapped phase by Ali and

Feigl (2012). The estimated values do not differ

significantly from zero with 95% confidence for the

radial and along-track components of the spacecraft‟s

position and velocity vectors. Each of these four

parameters is estimated at each of the acquisition

epochs, except the first one in a tree, when the

adjustment is arbitrarily set to zero to regularize the

solution (Feigl and Thurber, 2009). This conclusion

is consistent with our intuitive expectation that orbital

errors do not generate closed fringes.

The second possibility is that atmospheric

perturbations contribute to the observed fringe

pattern. To evaluate this possibility, we estimate the

vertical component of the range change gradient

to be 10 ± 3 parts per million of

topographic relief using GIPhT. Although this value

indicates a relatively large tropospheric perturbation

(Hanssen, 2001), it would contribute less than ~1.5

mm in range or less than ~0.1 fringe to the C-band

interferograms considered here because the

(differential) topographic relief is less than ~150 m

across the study area.

The third possibility, that an error in the digital

elevation model (DEM) creates an artifact in the

InSAR results, can be excluded by two arguments:

one qualitative and one quantitative. First, the shape

of the observed fringe pattern does not resemble that

of the topographic relief or that produced by

interpolation of incorrect elevations (e.g., Massonnet

and Feigl, 1995). Second, the signal observed in the

fringe patterns is much too large to be explained by

an error in the DEM. Derived from the U.S. National

Elevation Dataset (Falorni et al., 2005, Gesch et al.,

2002), the DEM used to account for the topographic

contribution to the interferograms has an absolute,

vertical uncertainty of no worse than σh ~ 20 m in

elevation (Kellndorfer et al., 2004). For example, the

interferometric pair shown in has an orbital

configuration such that the altitude of ambiguity ha is

~400 m. The definition of ha (Massonnet and Feigl,

1998) implies that a hypothetical artifact in the DEM

would have to be 2ha ~ 800 m in elevation to create

the 2-cycle signature in the observed fringe pattern

(Figure a). Such an artifact is extremely unlikely

because it exceeds the DEM uncertainty by a factor

of 2ha/σh ~ (800 m)/(20 m) ~ 40. Furthermore, in the

case of a hypothetical artifact in the DEM, the

number of fringes would be inversely proportional to

the altitude of ambiguity ha in each pair (e.g.,

Massonnet and Feigl, 1995). In fact, however, the

number of fringes is roughly proportional to the time

interval spanned by the interferometric pair.

From the preceding three arguments, we

conclude that the signal observed in the InSAR

results is due to deformation on the ground.

Interpreting the deformation using an elastic

model of production and injection

In this section, we analyze the deformation on

the ground by estimating parameters in a geophysical

model using GIPhT (Feigl and Thurber, 2009, Ali

and Feigl, 2012). The model attributes the

deformation to changes in volume of the reservoir,

describing production wells as sinks and injection

wells as sources. The model assumes an elastic

rheology with uniform material properties

everywhere in a half space. The shear modulus (or

rigidity) G and Poisson‟s ratio ν are also adjustable

parameters. The model includes three sinks and one

source.

The sinks in the model interpret the observed

deformation as the result of extracting fluids from the

crust by geothermal production. The main part of the

producing reservoir is described as a horizontal,

rectangular prism at a depth of 1.1 ± 0.2 km. It

decreases in volume at a rate of dV/dt = –268 × 103

m3/yr (using a dislocation formulation with 9 free

parameters (Okada, 1985). The estimated values of

the model parameters, along with their uncertainties,

are listed in Appendix 1.

The second and third sinks are each modeled as

deflating spheres (Mogi, 1958, Segall, 2010).

Located at a depth of 299 ± 38 m, the first sink is

deflating with rate dV/dt = -10 ± 4 × 103 m

3/yr (–5 ±

2 gallons/minute). Located at a depth of 298 ± 66 m,

the second sink is deflating with rate dV/dt = -13 ± 5

× 103 m

3/yr (= –6 ± 2 gallons/minute).

The injection into wells 18D-31and 18B-31 is

modeled as a horizontal, rectangular prism at a (very

shallow) depth of 37 ± 38 m. Its volume increases at

a rate of dV/dt = 16 × 103 m

3/yr (= +8

gallons/minute). using a dislocation formulation with

9 free parameters (Okada, 1985).

The total net change in volume between 2011

SEP 24 and 2011 DEC 21 is ΔV = -67 ± 8 × 103 m

3

(= 18 ± 2 million gallons = 2.4 ± 0.2 million cubic

feet).

6 of 16

The resulting modeled phase values shown in

Figure b reproduce the elongated pattern of the

observed phase values shown in a. Their difference,

the wrapped residual phase values shown in Figure c,

exhibit less than one fringe of unexplained signal.

This model fits the resampled InSAR observations to

within 0.0494 cycles (0.7 mm), as measured by the

circular mean deviation of the phase residuals, the

objective function minimized by GIPhT. Considering

the angular deviations of all the pixels in the 88-day

interferogram (Figure d), we find their circular mean

deviation to be 0.0801 cycles (1.2 mm).

These models are most easily evaluated along

profiles of range change that reveal the agreement

between model and data for the interferogram. Two

profiles are shown, one trending south-to-north

profile along Easting 327.747 km capturing the

overall separation of production and injection wells,

and a second west-to-east along Northing 4406.662

km that is at a high angle to the trend of the fault

systems. Both transects pass through the region of

maximum subsidence (Figures 1 and 2). The solid

black curve shows the modeled value of range

change calculated from the final deformation model

that minimizes the misfit. The circles show the

observed range change calculated by multiplying the

unwrapped observed phase value by 15.5 mm/cycle.

The phase values have been unwrapped by adding the

final modeled values to the final residual values. By

construction, the filled circles must fall within ± ½

cycle of the model curve.

SEISMICITY

Microearthquake Locations, Magnitudes and

Seismic Moments

The microearthquake locations plotted on

Figures 1 and 3 are from the Lawrence Berkeley

National Laboratory (LBNL) Brady‟s seismicity

catalog for the period November, 2010 to January,

2013. The locations are routinely calculated from

data recorded by the Brady‟s local seismic network

and displayed in near-real time on the LBNL web site

(http://esd.lbl.gov/research/projects/induced_seismici

ty/egs/desert_peak_brady.html). The Brady‟s

network was installed around well 15-12ST1

specifically to monitor seismicity induced by the

upcoming stimulation in the well. In its current

configuration the network comprises five borehole

(depths 15 m to 90 m) and four surface three-

component sensors. All of the routine processing of

the Brady‟s data stream is carried out automatically.

Locations are calculated from automatic P- and S-

wave arrival time picks using the preliminary

velocity model shown on Figure 3. The shallow

velocities in the model are based on a vertical seismic

profile from the Desert Peak geothermal field located

approximately 6 km southeast of Brady‟s. Moment

magnitudes (Mw) are calculated from static seismic

moments estimated from the long-period levels of

automatically determined microearthquake (MEQ)

source displacement amplitude spectra. Cumulative

seismic moment release is shown on Figure 5. The

calculated magnitudes of events recorded during this

period range from -1 to 2.4.

The accuracy of the MEQ locations and size

estimates is limited by three factors. The first is the

precision of the automatic arrival time picks and

spectral fits, which would undoubtedly be improved

by manual checks. The second factor is that most of

the microearthquakes located to date fall outside of

the network coverage, and are therefore relatively

poorly constrained by the recorded data. This limits

in particular the depth resolution of events located at

distances greater than about one network aperture

from the nearest station. The apparent lineation at

4.3 km depth on Figure 3c corresponds to poorly

constrained locations for which the depth was held

fixed at an assumed 3 km below sea level in order to

obtain an epicentral solution. Finally, it is not known

at present how well the preliminary velocity model

represents the actual shallow velocity structure at

Brady‟s (S. Jarpe, LBNL, personal communication),

which also contributes to the uncertainty in the

locations.

Microearthquake activity and locations

The largest concentration of MEQs is located to

the north of the network between the injection and

production wells (Figure 1). The majority of the

events within this concentration occurred in two

bursts of activity, the first on February 3 and 4, 2011,

and a second smaller cluster of events on April 2,

2012 (Figure 5). More recently, in January 2013,

there as been increased earthquake activity, but these

events are more widely distributed, with the largest

events to the SW of the geothermal field.

7 of 16

Figure 3. Profiles and cross sections showing (from top to bottom): range change between satellite and ground

between two SAR scenes, misfit between modeled and observed subsidence, and seismicity aligned S-N

(left) and W-E (right). Note that range change exceeds subsidence since the satellite scenes are inclined

~44° to vertical. The velocity model used in locating MEQs is inset in the lower-left, S-N cross-section.

Modeled elastic deformation sources are plotted in the cross-sections as magenta lines and the locations

of wells and seismometers are indicated (symbols consistent with Figure 1).

TRACER EVIDENCE OF FLOW PATHS

The overall pattern of flow within Brady‟s has

been previously investigated using tracers injected in

the NE of the field at the 18D-31 well and monitored

at the producing wells 46-1 and 46A-1, which reside

on the same well pad (Figure 4) (Geothermex, 1992).

Multiple peak tracer concentrations at single wells

indicate transport through distinct paths. Stark

contrast in peak concentrations and arrival times

between wells on the same well pads indicate

distinct, highly channeled paths rather than diffusive

flow along the fault trend. Well 46A-1 is

immediately south of 46-1, yet it shows very little

direct breakthrough, suggesting complex flow paths

and diffusion approximately where the fault trace

bends and the surface hydrothermal activity is

observed.

The circulation path is further elucidated by the

suggestion that cooler production brines injected in

the NE rapidly progress SW along the trend of the

fault, then diffuse into the hanging wall (or along

more tortuous fracture pathways at an angle to the

primary structural trend). In general, the pattern of

temperature indicates that as fluids move to the SW

they might be forced to circulate west then back to

the north indicating a complex flow network that is

isolated from the 15-12ST1 well farther south.

8 of 16

Figure 4: (a) Results of pre-production tracer test

conducted April-May 1992 in which 200 lb

of fluoroscein tracer was injected into well

18D-31 and monitored in wells 46-1 and

46A-1 (GeothermEx, 1992). (b) Interpreted

circulation pattern inferred from tracer

data and supported by temperature

monitoring in injection and production

wells. Note that 46A-1 is not shown but is

on the same wellpad as 46A-1.

COMPARISON OF SUBSIDENCE AND MEQs

Spatial Comparison

The position of maximum subsidence

corresponding to fluid extraction is localized east and

south of the production wells in the hanging wall

adjacent to a bend in the major basin-bounding fault

trace as well as in surface hydrothermal features

(Figure 1). However, minimal earthquake activity

and seismic energy release occurs within this area

(Figure 1 and Figure 5). MEQS, including some of

the largest events, are localized within the adjacent

region between the production wells from the bend in

the fault to the injection wells along the fault trend.

This interval corresponds to a high gradient in

subsidence.

The relative position of MEQs with regard to

injectors could be consistent with reduction in

effective normal stresses by injection, and the

gradient in subsidence could imply stressing that

contributes to earthquakes. We note, however, that a

similar magnitude of subsidence gradient to the south

is not associated with spatially clustered seismicity.

Although intense subsidence is strongly

localized SW of this fault bend and adjacent to the

production wells, the region of subsidence extends to

the NNE ~1.5 km to the injection wells and ~3 km to

the SSW along the fault trend. Many MEQs are

distributed along this broader region of subsidence.

The best fitting deformation source used to fit the

surface deformation field similarly extends roughly

from the injection wells 1.5 km to the NNE to 3km to

the SSW along this fault, implying the potential for a

more extensive reservoir volume interacting with the

production wells. The horizontal planar Mode I

source in the deformation model („Okada1‟ in

Appendix A) roughly coincides with the reservoir

depth (pers. comm., E. Zemach, 2012; Faulds et al.,

2010; 2012). The majority of earthquakes appear to

occur below both the reservoir and the modeled

deformation sources (Figures 1 and 3).

Planned EGS well 15-12ST1 is located SW of

the region of intense subsidence, but within the

footprint of the more gently subsiding region (Figures

1, 2, and subsidence profiles in Figure 3). This

location is also associated with much higher static

temperatures than the currently producing reservoir

(Faulds et al., 2012). These characteristics imply a

spatially confined reservoir isolated from 15-12ST1.

Temporal Comparison

Correlation between the impulse provided by

pumping activities and the response evident from

seismicity and surface deformations requires a clear,

robust correlation in time in which pumping activity

consistently precedes deformation responses. At

Brady‟s we test for simple correlations among the

time series of this forcing and response over the

period of recorded microseismicity (Figure 5). In

general, injection and production rates are fairly

constant with small variation on the order of less than

15-20%.

A strong subsidence signal is evident in the

single interferogram analyzed to date. Initial

evaluation of SAR pairs indicate that subsidence is

often much smaller than this example, suggesting that

subsidence rate is highly variable in time. Seismicity

shows similar high variability in timing, generally

occurring in clusters interspersed with periods of

quiescence.

The period of the interferogram includes an

increase in injection rate with the production held

fairly constant. Thus it coincides with an overall

reduction in the rate of fluid extraction. Also within

this period there are several earthquakes, but these

9 of 16

earthquakes occur both before as well as after the

increase in the injection rate.

The earliest cluster of seismicity in November

2010 roughly follows a large reduction in injection

and production, but is preceded by an increased rate

of MEQ during a period of nearly constant pumping.

A later cluster of MEQ activity in April 2012 also

occurs during a period of nominally constant

pumping.

Deformation-Magnitude Comparison

Deformation within the Brady‟s geothermal field

is assessed by comparing the measured volume

change resulting from pumping with the modeled

sources of surface deformation from InSAR and from

earthquake slip. Volume change, dV, or static

moment, M0, associated with each source is

independently calculated.

Static moment from the InSAR deformation

sources corresponds to constant displacement normal

to the rectangular Okada dislocation and is defined as

, where the displacement takes place over

the dislocation area A and E is Young‟s modulus

(Table 1). In this case the volume change is directly

related to the static moment, distinguishing this

deformation source from a seismic deformation

source in which shearing dominates and opening is

negligible.

Table 1: Comparison of Deformations by Source

Whole

Period:

766 days

InSAR

Period:

88 days

Impulse: Pumping Records

dV [m3] -8.49e+06 -8.62e+05

Response: MEQ

Total Number1 (all) 186 ---

N in subsiding

region3

123 (66%) 6

N (strong subsidence

region2)

26 (14%) 0

Total M01 [N.m] (all) 1.83e+13 --

Total M0 [N.m]

(subsiding region3 )

7.25e+12 1.10e+11

M0 [N.m] (intense

subsidence region2)

1.38e+11

0

dV [m3] --- ---

Response: InSAR

M0 [N.m] (Prod.)4 --- 1.94e+15

M0 [N.m] (Inject.) --- 1.13e+14

dV [m3] (all sources) --- ‑6.7±8e+04

1 Within boxed region of Figure 1.

2 Orange contour of Figure 1

3 Green contour of Figure 1

4 Okada deformation sources

Static seismic moment corresponds to shear

displacement on faults and fractures and is defined as

, where the average shear displacement d

takes place over fault area A and is the shear

modulus. Note that volume change does not

contribute to the seismic moment estimate. The total

cumulative seismic moment release between

November, 2010 and July, 2012 was ~1013

N.m.

Almost all of this occurred during the Mw2.4 event on

December 17, 2010, with a minor contribution from

the burst of activity in February 2011 (Figure 5).

There was negligible seismic moment release during

88-day the period covered by the interferogram

(Figure 5, Table 1). Even if all of the seismic

moment release had been concentrated beneath the

area of subsidence, the microearthquakes would have

made insignificant, if any, contribution to measured

surface deformation.

DISCUSSION

Correspondence between the impulse provided

by pumping and response monitored by measurement

of surface deformations and seismicity has been

assessed in three ways: (1) relative position; (2)

relative timing; (3) relative strength. It is clear from

the comparison of deformations independently

derived from pumping records, modeling of surface

deformations, and seismicity that three distinct

signals are present. The surface deformation evident

from the InSAR scenes is consistent with a

deformation source at the depth of the reservoir

currently tapped by production wells, but

significantly under-predicts the dV measured from

the comparison of production and injection volumes

(Table 1). The difference cannot be explained by the

use of elastic versus poroelastic models for the source

volumes or by fluid volume changes between surface

and reservoir conditions. The two effects essentially

cancel as the former represents an approximately

20% increase in the modeled dV whereas the latter

represents a 20% decrease in the required water

volume when compared with surface conditions due

to thermal expansion. The largest of these, the

horizontal-planar „Okada1‟, which accounts for

production, is horizontally extensive along the fault

trace, whereas the majority of subsidence is localized

within a bend in the fault (Figure 1). This suggests

that a more extensive fault-hosted reservoir at depth

is supplying fluid for production through a smaller

vertical conduit providing the connection to the

production wells, and which experiences the greatest

subsidence due to the localized fluid extraction. Such

a geometry is consistent with isothermal gradients in

static temperature profiles from wells in the field

(Shevenell et al., 2012). This deformation is largely

aseismic and represents the majority of the energy

supplied by volume loss due to pumping.

10 of 16

Figure 5: Time series during the period of observation include: (a) average daily produced (red) and

injected (blue) volumes and their difference (gray);(b) cumulative fluid volume change in the

reservoir as well as a subset (c) during the period of InSAR observation of surface deformation

represented as the accumulation of subsidence over the period of the paired SAR scenes; (d)

Seismicity history derived from earthquakes within boxed region aligned with the fault trend in

the middle panel of Figure 1.

11 of 16

DISCUSSION

Correspondence between the impulse provided

by pumping and response monitored by measurement

of surface deformations and seismicity has been

assessed in three ways: (1) relative position; (2)

relative timing; (3) relative strength. It is clear from

the comparison of deformations independently

derived from pumping records, modeling of surface

deformations, and seismicity that three distinct

signals are present. The surface deformation evident

from the InSAR scenes is consistent with a

deformation source at the depth of the reservoir

currently tapped by production wells, but

significantly under-predicts the dV measured from

the comparison of production and injection volumes

(Table 1). The difference cannot be explained by the

use of elastic versus poroelastic models for the source

volumes or by fluid volume changes between surface

and reservoir conditions. The two effects essentially

cancel as the former represents an approximately

20% increase in the modeled dV whereas the latter

represents a 20% decrease in the required water

volume when compared with surface conditions due

to thermal expansion. The largest of these, the

horizontal-planar „Okada1‟, which accounts for

production, is horizontally extensive along the fault

trace, whereas the majority of subsidence is localized

within a bend in the fault (Figure 1). The suggestion

is that a more extensive fault-hosted reservoir at

depth is supplying fluid for production through a

smaller vertical conduit providing the connection to

the production wells, and which experiences the

greatest subsidence due to the localized fluid

extraction. Such a geometry is consistent with

isothermal gradients in static temperature profiles

from wells in the field (Shevenell et al., 2012). This

deformation is largely aseismic and represents the

majority of the energy supplied by volume loss due to

pumping.

MEQs primarily occur below the modeled

deformation source, but do not coincide aerially with

the region of maximum subsidence. Those MEQs

within the region of subsidence largely occur

between injection and production wells (Figure 1)

where tracer data, temperature data, and the fault

geometry suggest there is focused fluid flux (Figure

4) and where there is a high gradient in subsidence

(Figure 3). However, the relative size of the moment

release from the InSAR derived deformation sources

is many orders of magnitude greater than moment

release from earthquake slip, and as noted previously,

there are no detected earthquakes beneath the

footprint of the greatest subsidence. Consequently,

the subsidence cannot be explained as a response to

slip on normal faults.

Relative Spatial Position of MEQs and

Deformation

Assuming subsidence reflects fluid extraction at

depth, then for this production dominated system,

MEQ activity does not clearly correspond to the

fractures comprising the most permeable reservoir.

Production of fluids will lower the effective normal

stress and stabilize frictional contacts locally but the

overall reservoir contraction could decrease the solid

stress at the boundaries of the reservoir. Thus, under

production dominated forcing, MEQs might be

expected to reveal the margins of the reservoir

volume. Large changes in solid stresses are likely to

correspond to regions with high gradients in

subsidence. Examination of the MEQ distribution in

comparison to the subsidence field (Figure 1) at best

indicates a minor role of this mechanism as most

MEQs are sporadically distributed around and in the

approximate shape of the subsidence field.

The densest cluster of MEQs is localized within

the region of subsidence gradient NE of the reservoir

along the path from injectors to producers. Tracer

testing indicates multiple confined pathways that

deliver discrete peaks in tracer concentration

consistent with flow along multiple separate

fractures/faults, as well as longer term arrivals of

nearly constant concentration more consistent with

diffusive flow (Figure 4). This implies that fluid

pressure changes are likely localized and largest

within the flow-path and highly anisotropic. Such

localization implies that a reduction in effective

normal stress that would reduce frictional resistance

to slip might be the strongest factor in influencing

MEQ activity.

In addition the potential role of thermal

contraction in contributing to MEQ activity is likely

different along confined flow paths might where

differential temperature between fluid and rock will

likely remain higher than for diffusive pathways.

This flow geometry could mean that thermal stressing

could also have a strong impact on the relative role

on induced seismicity consistent with findings by

Kelkar et al. (2012) in a model of stimulation at

Desert Peak.

Relative Timing of MEQs and Deformation

It is clear that there is strong spatial correlation

between pumping and deformation response, but the

lack of clear correlation in the detailed time series

implies that establishing the relationship among

forcing due to pumping and response manifested by

surface deformations and seismicity requires

accounting for prior reservoir activity in the model.

In other words, the impact of short-period

fluctuations in pumping must be appropriately

superposed on past activities. In part, it is necessary

because of the inherent time lag associated with pore

12 of 16

pressure, and hence effective stress redistribution

associated with fluid flow. Furthermore, the Kaiser

effect, whereby seismicity occurs only after

exceeding the previous failure threshold, has been

observed to modulate MEQ responses during

injection (Baisch and Harjes, 2003; Baisch, and

Vörös, 2010). Thus, while MEQs are triggered in the

initial fluid pressure rise in the volume adjacent to the

well, repeated injection into the same volume at a

later date only triggers seismicity if the fluid pressure

rise in the near-borehole volume exceeds the fluid

pressure achieved by the first injection.

In the Brady‟s case, initial fluid withdrawal,

which has been on-going since ~1992, will produce a

reduction in effective normal stress within the

permeable network of the reservoir accessed by

production wells, which stabilizes frictional contacts,

while simultaneous deflation of the finite reservoir

volume will induce tensional solid stresses farther

away. Daily fluctuations in fluid pressure are

superposed upon this pre-existing deformation

history, which is likely also complicated by the

characteristics of the permeable fracture network.

On-going episodes of seismicity, especially where

localized in regions of expected effective normal

stress reduction due to injection, support the potential

for continuing evolution of the permeable pathways

in the reservoir. This correlation also suggests that

the volumes adjacent to the reservoir, such as that

occupied by 15-12ST1, are likely to be amenable to

stimulation having sustained both minimal increase

in effect stress and stressing by contraction in the

adjacent production area. In the case of 15-12ST1,

the azimuth of the maximum horizontal stress

projects in the direction of the reservoir and coincides

with the predicted growth direction of shear-based

stimulations in a normal faulting stress environment.

In addition, the coincidence of production at a

bend in the fault trace implies that the pre-existing

geometry of the natural fracture network, and perhaps

a corresponding heterogeneity in stress (e.g., Swyer,

2012; Swyer and Davatzes, 2012; 2013) also strongly

influence fluid. The fact that 5-12ST1 is one of the

farthest wells from the bend suggests that its lack of

injectivity (pers. comm. E. Zemach, 2012) could be

because it resides outside this postulated zone of

enhanced fracture density.

Next Steps

For earthquakes, the timing of the deformation is

well constrained, but the current catalog of seismicity

has relatively poor spatial constraints on locations as

most earthquakes occur greater than one network

aperture away from the array, and the poorly

constrained velocity model also stems from the same

limitations in network geometry. We note that this

network was designed to monitor the upcoming

stimulation of 15-12 rather than on-going seismicity

associated with the reservoir and anticipate that

during the stimulation a population of well-located

earthquakes will become available consistent with the

response at nearby Desert Peak (Chabora et al.,

2012). Additional events should allow refinement of

the velocity model as well as advanced relocation

techniques to improve location accuracy.

For subsidence, the spatial constraint on

deformation is tight (at the surface), but the temporal

resolution is coarse due to the repeat time of the

satellite observations. The most-precise timing and

duration of subsidence is not resolvable below the 11

day repeat time of the TerraSAR-X satellite SAR

scene if the subsidence signal is on the scale of a

millimeter in that time interval or coarser for more

gradual accumulations of subsidence.

Overall, both surface deformations and MEQ

activity show variation in their rates that are not

easily correlated to the impulse provided by the fluid

mass balance or rates of injection and production

suggesting the need to address two key issues: (1) the

accumulated history of forcing needed to assess the

threshold of stress change or pressure that should

culminate in deformation events and (2) the delivery

of fluid pressure change in the reservoir to pumping

via a fully coupled poroelastic model of fluid

transport. Given differences in timing, position and

magnitude, our working hypothesis is that subsidence

and seismicity provide complementary responses to

the forcing by pumping to constrain these issues. In

future work, a single reservoir model consistent with

each of these measurements, will be derived and

tested using a poroelastic rheology.

CONCLUSIONS

We are in the first stages of exploring the

relationship between a comprehensive data set of

surface deformation, seismicity, geological structure,

and pumping. Differences in the timing, position and

magnitude, of subsidence indicate that they provide

complimentary responses to the forcing provided by

pumping. The InSAR data possesses excellent spatial

resolution but poor temporal resolution while the

seismicity data is excellent temporally but not well

constrained in depth. As expected, the correlation

between pumping and the measured deformation

responses are complex yet rich, as each signal

provides complementary information on the

subsurface conditions although with varying

resolutions.

Both the subsidence and seismicity follow the

general structural trend (SSW-NNE) although the

seismicity is scattered over a much wider region.

Initial modeling of elastic deformation sources

independently indicates that the pattern of surface

deformation requires volume/pressure changes

13 of 16

extended over a range of depths from the reservoir

(as identified from well data) upwards. The best-

fitting Okada Mode I deformation source

representing the reservoir is laterally extensive along

the fault trend and implies a far larger drainage area

than would be inferred from the position of

successful production wells. Concentrated subsidence

at the production wells accounted for by shallow

Mogi deformation sources is consistent with a

structurally controlled high permeability vertical

conduit that is laterally bounded by low permeability

rock that extends to the fault-hosted reservoir at

depth. Earthquakes generally occur on the margins of

the region of subsidence but outside the region of

intense subsidence and production, although the

region between injectors and producers contains

concentrated seismicity. In general, seismicity occurs

below the reservoir depth as resolved from both the

elastic model of subsidence and well observations.

Along with the differences in the positions of

subsidence and seismicity, we note that the expected

strain from the cumulative seismic slip (assuming

double-couple) of the seismicity is inadequate to

cause the observed surface deformation. Therefore,

much of the deformation associated with the resource

is aseismic, especially within the region obviously

tapped by production wells. Thus surface

deformations provide additional constraints on the

subsurface conditions different and complimentary to

induced seismicity.

Temporally, we see variations in both subsidence

rate and seismicity although we note, as mentioned

above, that the temporal resolution of the InSAR is

relatively poor. Comparisons with variations

pumping rate are underway, but an obvious, first-

order correlation is not apparent. This is not

unexpected, as the relative variations in rate are not

extreme and the expected effect is modulated by the

complex flow path and diffusion. We anticipate that

the strong, impulsive signal associated with the

upcoming stimulation of 15-12ST1 will induce a

stronger response.

ACKNOWLEDGMENTS

This project is supported by the Department of

Energy support, through the Geothermal

Technologies Program (FOA DE-FOA-0000522;

award number DE –EE0005510) and by ORMAT.

We would like to thank Dr. James Faulds for access

to fault and hydrothermal map data. Synthetic

Aperture Radar data from the TerraSAR-X (Pitz and

Miller, 2010) and the TanDEM-X (Krieger et al.,

2007) satellite missions operated by the German

Space Agency (DLR) were used under the terms and

conditions of Research Project RES1236. PALSAR

data from the ALOS satellite mission (Rosenqvist et

al., 2007) operated by the Japanese Space Agency

(JAXA) were used under the terms and conditions of

the WINSAR consortium.

REFERENCES

Ali, S.T. & Feigl, K.L., (2012). A new strategy for

estimating geophysical parameters from InSAR

data: application to the Krafla central volcano,

Iceland, Geochemistry, Geophysics, Geosystems,

13.

Amelung, F. & Bell, J.W., (2003). Interferometric

synthetic aperture radar observations of the 1994

Double Spring Flat, Nevada earthquake (M 5.9):

Mainshock accompanied by triggered slip on a

conjugate fault, J. Geophys. Res., 108, 2433,

doi:2410.1029/2002JB001953.

Baisch, S., and H.-P.Harjes: A model for fluid

injection induced seismicity at the KTB.

Geophys. Jour. Int., 152, 160-170, (2003).

Baisch, S., and R. Vörös (2010). Reservoir Induced

Seismicity : Where, When , Why and How

Strong?, World Geothermal Conference, (April),

25-29, 5 p.

Blake, K. and Davatzes, N.C. (2011). Stress

Heterogeneity in the Vicinity of the Coso

Geothermal Field. Proceedings Thirty-Fifth

Workshop on Geothermal Reservoir

Engineering, Stanford University, Stanford,

California. 11 p.

Cavalié, O., Doin, M.P., Lasserre, C. & Briole, P.,

(2007). Ground motion measurement in the Lake

Mead area, Nevada, by differential synthetic

aperture radar interferometry time series

analysis: Probing the lithosphere rheological

structure, J. Geophys. Res, 112.

Chabora, E., Zemach, E., Spielman, P., Drakos, P.,

Hickman, S., Lutz, S., Boyle, K., Falconer, A.,

Robertson-Tait, A., Davatzes, N.C., Rose, P.,

and Majer, E., and Jarpe, S. (2012). Hydraulic

Stimulation of Well 27-15, Desert Peak

Geothermal Field, Nevada, USA, Proceedings,

Thirty-Seventh Workshop on Geothermal

Reservoir Engineering, Stanford, California,

January 30-February 1, SGP-TR-194, 12 p.

CNES, (2006). DIAPASON Automated

Interferometric Processing SoftwareCentre

National d'Etudes Spatiales, Toulouse, France.

Coolbaugh, M.F., Sladek, C., and Kratt, C., (2004).

Digital mapping of structurally controlled

geothermal features with GPS units and pocket

computers, Geothermal Resources Council

Transactions, 28, 321-325.

14 of 16

Falorni, G., Teles, V., Vivoni, E.R., Bras, R.L. &

Amaratunga, K.S., (2005). Analysis and

characterization of the vertical accuracy of

digital elevation models from the Shuttle Radar

Topography Mission, Journal of Geophysical

Research, 110.

Faulds, J. E., Garside, L., & Opplinger, G. L., (2003).

Structural analysis of the Desert Peak-Brady

Geothermal Fields, northwestern Nevada:

Implications for understanding linkages between

northeast-trending structures and geothermal

reservoirs in the Humboldt structural zone. GRC

Transactions. 27, pp. 859-864. Geothermal

Resources Council Transactions.

Faulds, J., I. Moeck, P. Drakos, E. Zemach, et al.,

(2010). Structural assessment and 3D geological

modeling of the Brady‟s Geothermal Area,

Churchill County (Nevada, USA): A preliminary

report. , Thirty-fifth workshop on Geothermal

Reservoir Engineering, Feb. 1-3, SGP-TR-188, 5

p.

Faulds, J.E., Coolbaugh, M.F., Benoit, D., Oppliger,

G., Perkins, M., Moeck, I., and Drakos, P.,

(2010). Structural controls of geothermal activity

in the northern Hot Springs Mountains, western

Nevada: The tale of three geo- thermal systems

(Brady‟s, Desert Perk, and Desert Queen).”

Geothermal Resources Council Transactions, v.

34, p. 675-683.

Faulds, J. L. Garside, A. Ramelli, and M. Coolbaugh,

2012 (in prep). “Pre- liminary geologic map of

the Desert Peak-Bradys geothermal fields,

Churchill County, Nevada.” Nevada Bureau of

Mines and Geology Map.

Feigl, K.L. & Sobol, P.E., (2013). PHA2QLS.C: a

computer program to compress images of

wrapped phase by simultaneously estimating

gradients and quadtree resampling, in prep.

Feigl, K.L. & Thurber, C.H., (2009). A method for

modelling radar interferograms without phase

unwrapping: application to the M 5 Fawnskin,

California earthquake of 1992 December 4,

Geophys. J. Int., 176, 491-504.

Gesch, D., Oimoen, M., Greenlee, S., Nelson, C.,

Steuck, M. & Tyler, D., (2002). The National

Elevation Dataset, Photogrammetric

Engineering & Remote Sensing, Journal of the

American Society for Photogrammetry and

Remote Sensing, 68.

Geothermex, (1992). Evaluation of the Geothermal

Resource at Bradys Hot Springs, Nevada.,

Geothermex, Inc., Richmond, California, 140 p.

Goldstein, R.M. & Werner, C.L., (1998). Radar

interferogram filtering for geophysical

applications, Geophys. Res. Lett., 25, 4035-4038.

Hanssen, R.F., (2001). Radar Interferometry: Data

Interpretation and Error Analysis, edn, Vol., pp.

Pages, Kluwer Academic Publishers, Dordrecht.

Hoffmann, J., Zebker, H.A., Galloway, D.L. &

Amelung, F., (2001). Seasonal subsidence and

rebound in Las Vegas Valley, Nevada, observed

by synthetic aperture radar interferometry, Water

Resour. Res., 37.

Kellndorfer, J., Walker, W., Pierce, L., Dobson, C.,

Fites, J.A., Hunsaker, C., Vona, J. & Clutter, M.,

(2004). Vegetation height estimation from

Shuttle Radar Topography Mission and National

Elevation Datasets, Remote Sensing of

Environment, 93, 339-358.

Kelkar, S., Lewis, K., Hickman, S., Davatzes, N.C.,

Moos, D., and Zyvoloski, G., (2012). Modeling

Coupled Thermal-Hydrological-Mechanical

Processes During Shear Stimulation of an EGS

Well, Proceedings, Thirty-Seventh Workshop on

Geothermal Reservoir Engineering, Stanford,

California, January 30-February 1, SGP-TR-194,

8 p.

Kohlhase, A.O., Feigl, K.L. & Massonnet, D.,

(2003). Applying differential InSAR to orbital

dynamics: a new approach for estimating ERS

trajectories, J. Geodesy, 77, 493-502.

Krieger, G., Moreira, A., Fiedler, H., Hajnsek, I.,

Werner, M., Younis, M. & Zink, M., (2007).

TanDEM-X: A Satellite Formation for High-

Resolution SAR Interferometry, Geoscience and

Remote Sensing, IEEE Transactions on, 45,

3317-3341.

Massonnet, D. & Feigl, K.L., (1995). Discriminating

geophysical phenomena in satellite radar

interferograms, Geophys. Res. Lett., 22, 1537-

1540.

Massonnet, D. & Feigl, K.L., (1998). Radar

interferometry and its application to changes in

the Earth's surface, Rev. Geophys., 36, 441-500.

Massonnet, D. & Rabaute, T., (1993). Radar

interferometry: limits and potential, IEEE Trans.

Geoscience & Rem. Sensing, 31, 455–464.

Massonnet, D., Rossi, M., Carmona, C., Adragna, F.,

Peltzer, G., Feigl, K. & Rabaute, T., (1993). The

displacement field of the Landers earthquake

mapped by radar interferometry, Nature, 364,

138-142.

McLeod, I.H., Cumming, I.G. & Seymour, M.S.,

1998. ENVISAT ASAR data reduction - Impact

15 of 16

on SAR interferometry, IEEE Transactions on

Geoscience and Remote Sensing, 36, 589-602.

Mogi, K., (1958). Relations between the eruption of

various volcanoes and the deformations of the

ground surfaces around them, Bull. Earthquake

Research Institute, 36, 99-134.

Nathwani, J., Majer, E., Boyle, K., Rock, D.,

Peterson, J. & Jarpe, S., (2011). DOE real-time

seismic monitoring at enhanced geothermal

system sites, Thirty-sixth workshop on

Geothermal Reservoir Engineering, SGP-TR-

191.

Okada, Y., (1985). Surface deformation due to shear

and tensile faults in a half-space, Bull. Seism.

Soc. Am., 75, 1135-1154.

Oppliger, G., Coolbaugh, M. & Foxall, W., (2004).

Imaging structure with fluid fluxes at the Bradys

geothermal field with satellite interferometric

radar (InSAR): New insights into reservoir

extent and structural controls, Geothermal

Resources Council Transactions, 28, 37-40.

Oppliger, G., Coolbaugh, M. & Shevenell, L., (2006).

Improved Visualization of Satellite Radar InSAR

Observed Structural Controls at Producing

Geothermal Fields Using Modeled Horizontal

Surface Displacements Horizontal Strains from

the Displacement Field, GRC Trans., 30, 927-

930.

Pitz, W. & Miller, D., (2010). The TerraSAR-X

Satellite, Geoscience and Remote Sensing, IEEE

Transactions on, 48, 615-622.

Rosenqvist, A., Shimada, M., Ito, N. & Watanabe,

M., (2007). ALOS PALSAR: A Pathfinder

Mission for Global-Scale Monitoring of the

Environment, Geoscience and Remote Sensing,

IEEE Transactions on, 45, 3307-3316.

Segall, P., (2010). Earthquake and volcano

deformation, edn, Vol., pp. Pages, Princeton

University Press, Princeton, N.J.

Shevenell, L., Oppliger, G., Coolbaugh, M. & Faulds,

J., (2012). Bradys (Nevada) InSAR Anomaly

Evaluated With Historical Well Temperature and

Pressure Data, Geothermal Resources Council

Transactions, 36, 1383-1390.

Swyer, M.W, (2012). Evaluating the role of the

rhyolite ridge fault system in the desert peak

geothermal field, NV: Boundary element

modeling of fracture potential in proximity to

fault slip; Masters Thesis, Temple University,

Philadelphia, PA. 247 p.

Swyer, M.W. and Davatzes, N.C. (2012) Using

Boundary Element Modeling of Fault Slip to

Predict Patterns of stress Perturbation and

Related Fractures in Geothermal Reservoirs and

Explore Parameter Uncertainty, Proceedings,

Thirty-Seventh Workshop on Geothermal

Reservoir Engineering, Stanford, California,

January 30-February 1, SGP-TR-194, 14 p.

Swyer, M.W. and Davatzes, N.C. (2013) Evaluating

the role of the Rhyolite Ridge fault system in the

Desert Peak Geothermal Field with robust

sensitivity testing through boundary element

modeling and likelihood analysis, Proceedings,

Thirty-Eight Workshop on Geothermal Reservoir

Engineering, Stanford, California, February 11-

February 13, SGP-TR-194, 16 p.

Vincent, P., Larsen, S., Galloway, D., Laczniak, R.J.,

Walter, W.R., Foxall, W. & Zucca, J.J., (2003).

New signatures of underground nuclear tests

revealed by satellite radar interferometry,

Geophysical Research Letters, 30.

16 of 16

APPENDIX 1: Final estimates and 1-sigma uncertainties of model parameters from GIPhT.

Parameter Estimate Uncertainty

E grad @ epoch 002 [] -6.39E-07 +/- 2.71E-07 N grad @ epoch 002 [] 2.65E-07 +/- 1.39E-07 U grad @ epoch 002 [] -9.91E-06 +/- 2.86E-06 Offset @ epoch 002 [cycles] -0.4015 +/- NaN Mogi1 Easting [m] 327689.9 +/- 600.6 Mogi1 Northing [m] 4407093.9 +/- 521.5 Mogi1 Depth [m] 294.1777 +/- 54.0039 Mogi1 Volume Increase [m3/yr] -1.06E+04 +/- 3.69E+03 Mogi2 Easting [m] 327513.6 +/- 705.6 Mogi2 Northing [m] 4406378.4 +/- 714.4 Mogi2 Depth [m] 297.7805 +/- 65.918 Mogi2 Volume Increase [m3/yr] -1.34E+04 +/- 4.95E+03 Okada1 Length [m] 5263.5023 +/- 189.7974 Okada1 Width [m] 319.3482 +/- 29.9559 Okada1 Depth [m] 1080.9686 +/- 173.4524 Okada1 Strike [azimuth] 17.7975 +/- 1.1958 Okada1 Easting [m] 327982.4 +/- 129.3 Okada1 Northing [m] 4406452.3 +/- 317.9 Okada1 Tensile Opening [m/yr] -0.1593 +/- 0.0131 Okada2 Length [m] 680.5913 +/- 399.9023 Okada2 Width [m] 181.376 +/- 99.9023 Okada2 Depth [m] 37.6492 +/- 38.75 Okada2 Strike [azimuth] 33.5086 +/- 9.4287 Okada2 Easting [m] 328848.6 +/- 189.5 Okada2 Northing [m] 4408778.7 +/- 177.3 Okada2 Tensile Opening [m/yr] 0.126 +/- 0.0422 Poisson Ratio [] 0.2364 +/- 0.0957 Shear Modulus [Pa] 2.76E+10 +/- 4.58E+09 Total Net Volume Increase [m3/yr] -2.76E+05 +/- 3.47E+04 Okada1 Centroid Easting [m] 327830.4 +/- --- Okada1 Centroid Northing [m] 4406501.1 +/- --- Okada1 Centroid latitude [deg] 39.7911 +/- --- Okada1 Centroid longitude [deg] 240.9892 +/- --- Okada1 Convt strike [azimuth] 197.7975 +/- 1.1958 Derived Okada1 potency in m3/yr 2.68E+05 +/- 5.53E+03 Derived Okada1 moment [N.m/yr] 8.03E+15 +/- --- Derived Okada1 Mw 4.5732 +/- --- Okada2 Centroid Easting [m] 328773 +/- --- Okada2 Centroid Northing [m] 4408828.8 +/- --- Okada2 Centroid latitude [deg] 39.8122 +/- --- Okada2 Centroid longitude [deg] 240.9996 +/- --- Okada2 Convt strike [azimuth] 213.5086 +/- 9.4287 Derived Okada2 potency [m3/yr] 1.56E+04 +/- --- Derived Okada2 moment [N.m/yr] 4.67E+14 +/- --- Derived Okada2 [Mw] 3.7494 +/- --- Mogi1 Centroid latitude [deg] 39.7964 +/- --- Mogi1 Centroid longitude [deg] 240.9874 +/- --- Mogi2 Centroid latitude [deg] 39.7899 +/- --- Mogi2 Centroid longitude [deg] 240.9855 +/- --- Youngs Modulus [Pa] 6.90E+10 +/- --- Derived Okada1 potency [m3/yr] 2.68e+05 +/- 5.53e+03 Derived Okada1 M0 [N.m/yr] 8.03e+15 +/- --- Derived Okada2 potency [m3/yr] 1.56e+04 +/- --- Derived Okada2 M0 [N.m/yr] 4.67e+14 +/- --- Mogi1 dV in [m3/yr] -1.06e+04 +/- 3.69e+03 Mogi2 dV in [m3/yr] -1.34e+04 +/- 4.95e+03