Shallow creep on the Haiyuan Fault (Gansu, China) revealed by SAR Interferometry

Abstract

Interferometric synthetic aperture radar data are used to map the interseismic velocity field along the Haiyuan fault system (HFS), at the north-eastern boundary of the Tibetan plateau. Two M ∼ 8 earthquakes ruptured the HFS in 1920 and 1927, but its 260 km-long central section, known as the Tianzhu seismic gap, remains unbroken since ∼1000 years. The Envisat SAR data, spanning the 2003–2009 period, cover about 200 × 300 km2 along three descending and two ascending tracks. Interferograms are processed using an adapted version of ROI_PAC. The signal due to stratified atmospheric phase delay is empirically corrected together with orbital residuals. Mean line-of-sight velocity maps are computed using a constrained time series analysis after selection of interferograms with low atmospheric noise. These maps show a dominant left-lateral motion across the HFS, and reveal a narrow, 35 km-long zone of high velocity gradient across the fault in between the Tianzhu gap and the 1920 rupture. We model the observed velocity field using a discretized fault creeping at shallow depth and a least squares inversion. The inferred shallow slip rate distribution reveals aseismic slip in between two fully locked segments. The average creep rate is ∼5 mm yr−1, comparable in magnitude with the estimated loading rate at depth, suggesting no strain accumulation on this segment. The modeled creep rate locally exceeds the long term rate, reaching 8 mm yr−1, suggesting transient creep episodes. The present study emphasizes the need for continuous monitoring of the surface velocity in the vicinity of major seismic gaps in terms of seismic hazard assessment.

Full text

Shallow creep on the Haiyuan Fault (Gansu, China) revealed
by SAR Interferometry
R. Jolivet,
1,2
C. Lasserre,
1
M.-P. Doin,
3
S. Guillaso,
3
G. Peltzer,
4,5
R. Dailu,
6
J. Sun,
7
Z.-K. Shen,
4
and X. Xu
7
Received 30 July 2011; revised 6 April 2012; accepted 20 April 2012; published 5 June 2012.
[1]Interferometric synthetic aperture radar data are used to map the interseismic
velocity field along the Haiyuan fault system (HFS), at the north-eastern boundary
of the Tibetan plateau. Two M 8 earthquakes ruptured the HFS in 1920 and 1927, but its
260 km-long central section, known as the Tianzhu seismic gap, remains unbroken since
1000 years. The Envisat SAR data, spanning the 2003–2009 period, cover about 200 
300 km
2
along three descending and two ascending tracks. Interferograms are processed
using an adapted version of ROI_PAC. The signal due to stratified atmospheric phase
delay is empirically corrected together with orbital residuals. Mean line-of-sight velocity
maps are computed using a constrained time series analysis after selection of
interferograms with low atmospheric noise. These maps show a dominant left-lateral
motion across the HFS, and reveal a narrow, 35 km-long zone of high velocity
gradient across the fault in between the Tianzhu gap and the 1920 rupture. We model the
observed velocity field using a discretized fault creeping at shallow depth and a least
squares inversion. The inferred shallow slip rate distribution reveals aseismic slip in
between two fully locked segments. The average creep rate is 5mmyr
1
, comparable
in magnitude with the estimated loading rate at depth, suggesting no strain accumulation
on this segment. The modeled creep rate locally exceeds the long term rate, reaching 8 mm
yr
1
, suggesting transient creep episodes. The present study emphasizes the need for
continuous monitoring of the surface velocity in the vicinity of major seismic gaps in terms
of seismic hazard assessment.
Citation: Jolivet, R., C. Lasserre, M.-P. Doin, S. Guillaso, G. Peltzer, R. Dailu, J. Sun, Z.-K. Shen, and X. Xu (2012),
Shallow creep on the Haiyuan Fault (Gansu, China) revealed by SAR Interferometry, J. Geophys. Res.,117, B06401,
doi:10.1029/2011JB008732.
1. Introduction
[2] The recent improvements in space-based geodesy,
with the increasing number and accuracy of surface
deformation measurements, allow us to better investigate the
stress and strain distribution and evolution within the litho-
sphere throughout the seismic cycle. Coseismic and post-
seismic deformations are now well described and modeled,
based on InSAR and GPS data, in particular. In contrast,
measuring and modeling spatiotemporal variations of the
interseismic deformation along a fault remain difficult and
are now at the forefront of the research in seismotectonics
and seismic hazard assessment.
[3] Recent advances on this topic have come from the
study of subduction zones, with the discovery of slow slip
events (e.g., Japan [Ozawa et al., 2002], Cascadia [Dragert
et al., 2001], Mexico [Kostoglodov et al., 2003; Radiguet
et al., 2011]) and lateral variations of interseismic coupling
[e.g., Mazzottietal., 2000; Chlieh et al., 2008]. Transient or
permanent aseismic slip during the interseismic period have
also been observed along sections of intracontinental strike-slip
faults (e.g., North Anatolian Fault [Ambraseys,1970;Çakir
et al., 2005], San Andreas fault [Lienkaemper et al.,1991;
Schmidt et al., 2005; Ryder and Bürgmann, 2008]) or nor-
mal faults [e.g., Doubre and Peltzer, 2007]. Aseismic slip may
reduce seismic hazard by releasing stress in the seismogenic
1
Institut des Sciences de la Terre, UMR 5275, Université Joseph
Fourier, CNRS, Grenoble, France.
2
Now at Tectonic Observatory, California Institute of Technology,
Pasadena, California, USA.
3
Laboratoire de Géologie, UMR 8538, Ecole Normale Supérieure,
CNRS, Paris, France.
4
Department of Earth and Space Science, University of California, Los
Angeles, California, USA.
5
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, California, USA.
6
Lanzhou Seismological Institute, Chinese Earthquake Administration,
Lanzhou, China.
7
Institute of Geology, Chinese Earthquake Administration, Beijing,
China.
Corresponding author: R. Jolivet, Tectonic Observatory, California
Institute of Technology, Pasadena, CA 90125, USA.
(romain.jolivet@ujf-grenoble.fr)
Copyright 2012 by the American Geophysical Union.
0148-0227/12/2011JB008732
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, B06401, doi:10.1029/2011JB008732, 2012
B06401 1of18

part of the crust. It may also play an important role in the
triggering of large ruptures, as shown by numerical simulation
[Lapusta and Liu, 2009] and recent seismic observations
[Bouchon et al., 2011]. Recent studies thus point to the
importance of studying the spatial patterns and rates of strain
accumulation during the interseismic phase, the mechanisms
involved and their relationship with the physical properties of
faults and their surrounding graphic [Hetland and Hager,
2006; Lundgren et al., 2009; Jolivet et al., 2009]. Such obser-
vations remain challenging and only a few faults are currently
well documented.
[4] We focus here on the Haiyuan fault system at the north-
eastern margin of the Tibetan plateau. Two M
w
8 earthquakes
ruptured long sections of this fault system in the past century
(1920, 1927, Figure 1). Another section has been identified
as a seismic gap with a high hazard [Gaudemer et al., 1995],
and possible shallow creep at its eastern end, as suggested
by a previous InSAR study based on sparse ERS data
[Cavalié et al., 2008]. This makes the Haiyuan fault an
interesting target to investigate along-strike variations of
the interseismic strain rate in relation with the fault seismic
history and geometry.
[5] We use Envisat SAR interferometry data to map the
mean surface displacement rate in the fault area between
2003 and 2009. Given the low expected strain rate, method-
ological refinements are required [Cavalié et al., 2008]. We
first present the seismotectonic setting of the Haiyuan fault
system, the radar data set, and the overall processing strategy.
We then detail specific InSAR processing steps developed to
increase the signal-to-noise ratio, including atmospheric
phase delay correction, interferogram selection, and time
series analysis. We model the 2-D interseismic strain rate
Figure 1. Seismotectonic map showing the Haiyuan Fault system and its location in the India-Asia collision
zone. Fault traces are superimposed on Shuttle Radar Topography Mission (SRTM) Digital Elevation Model
(DEM). White lines and stars represent surface ruptures and epicenters, respectively, of the M 81920
and 1927 earthquakes. Bold grey line follows the Tianzhu seismic gap [Gaudemer et al., 1995]. Black
rectangles shows the coverage of analyzed Envisat SAR data, with track numbers indicated. Seismicity
from Seismological Institute of Lanzhou, Chinese Earthquake Administration regional network is shown
for the 2003–2009 period.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
2of18

based on mean LOS velocity maps, taking into account spa-
tially correlated residual noise. We finally discuss the
implications of the observed spatial strain variations.
2. The Haiyuan Fault System
[6] The 1000 km-long Haiyuan fault system extends
from the central Qilian Shan to the west, to the Liupan
Shan, to the east (Figure 1). It contributes to the accom-
odation of the deformation related to the India/Asia colli-
sion. Strain is partitioned between predominantly left-lateral
east-striking faulting, along the Haiyuan and Gulang faults,
and North-North-East shortening across thrusts systems
[Gaudemer et al., 1995]. Left-lateral and thrusts faults connect
at depth to an oblique shear zone, through a south-dipping
decollement [Gaudemer et al., 1995; Meyer et al., 1998].
[7] Two M
w
8 earthquakes occurred on the Haiyuan fault
system in the past century (Figure 1). The 12-16-1920, M
w
8–8.3, Haiyuan earthquake broke the eastern 240 km-
long section of the Haiyuan fault [e.g., Deng et al., 1986;
Zhang et al., 1987]. The 05-23-1927, M
w
8, Gulang
earthquake ruptured south-dipping thrusts located at the
south-eastern end of the Qilian Shan [Gaudemer et al.,
1995; Xu et al., 2010]. In contrast, the 260 km-long
unruptured section of the Haiyuan fault, west of the 1920
rupture, has been identified as a seismic gap, the Tianzhu gap,
with a high seismic hazard (Figure 1) [Gaudemer et al.,
1995]. From a paleoseismological study, Liu-Zeng et al.
[2007] estimated the recurrence time of earthquakes along
the Tianzhu gap to be about 1000 years, with the last two
large earthquakes occurring at 1092 AD and at 143 or
374 AD (estimated M
w
8). A few M
w
5–6 events have
occurred near the extremities of the gap, in the past 20 years
[Lasserre et al., 2001]. Intense microseismic activity is also
concentrated in between the 1920 rupture and the seismic gap
(Figure 1).
[8] The Holocene slip rate of the Haiyuan fault was esti-
mated from offset measurements and dating of morphologi-
cal markers (alluvial terrasses and moraines). It decreases
from west to east, from 19 5mmyr
1
along the Leng Long
Ling segment [Lasserre et al., 2002], to 12 4mmyr
1
along the Mao Mao Shan segment [Lasserre et al., 1999],
down to 3.5–10 mm yr
1
along the 1920 rupture (8 2mm
yr
1
[Zhang et al., 1988], 4.5 1mmyr
1
[Li et al., 2009]).
As for most strike-slip faults in Tibet, the short term (i.e.
geodetic) slip rate of the Haiyuan fault, that may vary during
its seismic cycle, differs from the average long term (i.e.
Quaternary) slip rate. The cause for such discrepancy remains
controversial [e.g., Mériaux et al., 2004; Cowgill, 2007;
Loveless and Meade, 2011]. Large scale, rigid block models
based on GPS data over China [e.g., Gan et al., 2007]
indicate a 8.6 mm yr
1
slip rate on the Haiyuan fault.
Cavalié et al. [2008] derive a 4.2–8mmyr
1
slip rate from
the modeling of a fault-parallel velocity profile across the
fault, near the junction between the 1920 rupture and the
Tianzhu gap, using sparse ERS InSAR data between 1993
and 1998. Additionally, they highlight a strong strain con-
centration in the fault zone and suggest the presence of creep
at shallow depth. In the following sections, we further char-
acterize the interseismic slip rate on the fault, and its along-
strike variations at shallow depth based on denser time series
of InSAR data, covering an extended study area and period.
3. Envisat Data Set and Interferogram Processing
[9] We process all available radar data acquired by the
ENVISAT satellite from two ascending and three descend-
ing orbital tracks, to measure the interseismic deformation
along the Haiyuan fault system (Figure 1). This data set
covers the eastern part of the Tianzhu gap as well as the
western end of the 1920 rupture, over a 60000 km
2
area.
Data span the 2003–2009 period, with almost monthly
acquisitions since 2007. 21 to 32 images are combined into
83 to 167 interferograms, depending on the track number
(Table 1).
[10] We use an interferometric chain that includes routines
from the ROI_PAC software [Rosen et al., 2004] and addi-
tional modules to process raw data into interferograms [Doin
et al., 2011]. Precise DORIS orbits are provided by the
European Space Agency. We use the Shuttle Radar Topogra-
phy Mission Digital Elevation Model [Farr and Kobrick,
2000] oversampled by a factor of 2 after referencing to the
WGS84 ellipsoid. The main processing steps are the following:
[11] 1. Single Look Complex (SLC) images are computed
with a common doppler, chosen so that their doppler band-
widths overlap with each other at 90% minimum.
[12] 2. We select a single master image that maximizes the
total coherence, following Zebker and Villasenor [1992] and
Hooper et al. [2007].
[13] 3. All SLCs are coregistered in the master image
geometry using a Digital Elevation Model (DEM) assisted
procedure [Nitti et al., 2011]. This procedure is described in
the Appendix A.
[14] 4. We select image pairs with a perpendicular base-
line smaller than 400 meters and a temporal baseline greater
than six months, following a Small BAseline Subset
approach (SBAS) [Berardino et al., 2002]. This provides a
good compromise in terms of interferograms signal-to-noise
ratio and coherence.
Table 1. Envisat Data Set, Interferogram Selection for Time Series Analysis and Residual Turbulent Noise Model on LOS Velocity
Maps
a
Track
Number of Images and Images Used Interferograms Used Covariance Function Autocovariance
Interferograms in Time Series in Time Series (rad
2
yr
2
) (rad
2
yr
2
)
T061 31 (167) 75% (23) 54% (90) 0.0073e
0.1378x
0.0087
T240 25 (130) 60% (15) 31% (40) 0.01473e
0.1487x
cos(0.01124x) 0.01527
T290 25 (83) 64% (16) 35% (29) 0.01934e
0.086x
cos(0.05649x) 0.02225
T333 32 (163) 56% (18) 30% (47) 0.01631e
0.1734x
0.01902
T469 21 (88) 66% (14) 45% (40) 0.02408e
0.08934x
0.02628
a
xis the distance that separates two pixels.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
3of18

[15] 5. Full resolution differential interferograms are
obtained after range spectral filtering adapted to the local
elevation slope [Guillaso et al., 2006, 2008]. Steps 3 and 5
improve the coherence at long perpendicular baselines in
areas of rough topography. Interferograms are corrected from
orbital effects and topography, using precise DORIS orbits
and the SRTM DEM.
[16] 6. Interferograms are looked by a factor of 4 in range
and of 20 in azimuth, filtered using a power spectrum filter
[Goldstein and Werner, 1998], and unwrapped using a branch-
cut algorithm [Goldstein et al.,1988].
[17] 7. Finally, we perform an atmospheric mitigation pro-
cess and a time series analysis, as described in the following
sections. These procedures allow us to recover the phase
increments between two acquisition dates.
4. Correction of Atmospheric Phase Delay
and Orbital Errors
[18] The unwrapped differential interferometric phase F
i,j
between two dates i and j can be written as the sum of four
terms:
Fi;j¼fdef þforbit þfatmo þfnoise;ð1Þ
where f
def
is the expected deformation signal related to the
fault, f
orbit
is the residual orbital phase, f
atmo
the atmospheric
phase delay and f
noise
the residual noise from instrument,
decorrelation, coregistration, unwrapping or DEM errors.
4.1. Tropospheric Phase Delays
[19] As phase delays related to dispersive effects in the
ionosphere can be neglected in C-band radar, we consider
here only the tropospheric propagation delays, related to the
spatial and temporal variability of the air refractivity index.
Tropospheric delays are separated into a stratified compo-
nent, corresponding to the vertical stratification averaged
over the scene, and a turbulent term, that is considered random
in space and time (Figure 2a). The turbulent portion of the
troposphere delay can be efficiently removed by interferogram
stacking [e.g., Zebker et al.,1997;Ryder and Bürgmann,
2008] or time series analysis [e.g., Ferretti et al., 2001;
Schmidt and Bürgmann,2003].The“stratified”component of
the tropospheric phase delay (Figure 2b) only depends on the
temporal variations of the troposphere vertical stratification in
between the minimum and maximum elevations of a scene
[Doin et al., 2009, and references therein]. It is related to ele-
vation and mimicks topography on interferograms (Figures 2b
and 2c). It is highly problematic in cases where topography
correlates with the expected tectonic signal (e.g., Kunlun fault
[Jolivet et al., 2011]). Estimates of displacement rates may be
biased by stratified delays, because of the uneven sampling
throughout the season cycle [Doin et al., 2009].Therefore,
such delays must be corrected prior to or during the time series
analysis [Elliott et al., 2008].
4.2. Correction Strategy
[20] To account for trade-offs between f
def
,f
orbit
and
f
atmo
, we estimate these contributions through a joint inver-
sion [Cavalié et al., 2008]. We use the following simplified
expression of equation (1):
Fi;j¼si;jDþai;jRþbi;jAþgi;jRA þdi;jþki;jzþfrand þfnoise:
ð2Þ
[21] The first term corresponds to f
def
. It is scaled to D,an
interseismic elastic half-space model, projected in the satel-
lite Line-Of-Sight (hereafter referred to as LOS) [Savage
and Burford, 1973; Cavalié et al., 2008]. The second,
third, fourth and fifth terms correspond to f
orbit
, a function
of the range Rand the azimuth A. We use a bilinear function
to describe f
orbit
because of a lack of additional independent
constraints on the orbital contribution (equation (2)).The
Figure 2. Examples of interferograms showing (a) turbulent atmospheric patterns and (b) stratified atmo-
spheric phase delay correlated with elevation. (c) Digital Elevation Model from SRTM, one color cycle
represents 350 meter elevation change. (d) Same as Figure 2b after correction from stratified atmospheric
delay and orbital errors. One color cycle represents 2 rad along LOS.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
4of18

sixth term corresponds to f
atmo
, which is divided into a
stratified tropospheric phase delay, approximated by a linear
function of the elevation z, and a turbulent contribution
f
rand
.
[22] For each interferogram, produced with acquisitions iand
j, we jointly solve for parameters s
i,j
,a
i,j
,b
i,j
,g
i,j
and k
i,j
using
a least squares minimization. To ensure that stratified delay
corrections are consistent within the interferogram network, we
reestimate the delay/elevation ratios k
i,j
through a time series
analysis [Cavalié et al., 2007; Elliottetal., 2008]. Orbital
parameters are reestimated in a similar way [Biggs et al., 2007].
[23] All interferograms are corrected using the derived
orbital and stratified delay contributions. Figures 2d and 3a
show an example of such correction, which illustrates the
validity of the delay/elevation linear trend approximation, as
observed on most of the interferograms because of the
moderate elevation range across the scene. The delay/ele-
vation relationship reestimated with a network approach
shows a better agreement with the InSAR data, from low to
high elevation.
4.3. Correction Validation
[24] Global atmospheric models allow the computation of
the stratified tropospheric phase delay as a function of ele-
vation as well as the corresponding delay/elevation ratio
[Doin et al., 2009]. We compare the InSAR derived delay/
elevation ratios with prediction from the ERA40 analysis
provided by the European Centre for Medium-Range
Weather Forecast (Figure 3) [Uppala et al., 2005].
[25] For each acquisition date, we extract the specific
humidity, the temperature and the geopotential height at each
of the 21 pressure levels at one grid point per track chosen at
the lowest elevation within the studied area. Parameters are
taken at 6 AM and 6 PM, for descending and ascending tracks
respectively. We then generate ERA-derived delay/elevation
ratios for all interferograms.
[26] Figure 3b shows the good agreement between InSAR
derived and ERA40 derived delay/elevation ratios, with only
minor differences from one track to another. Discrepancies
for some interferograms may be due to the prevailance of
turbulences, to a complex vertical stratification of the tro-
posphere with a non-linear delay/elevation relationship, or to
poorly constrained model parameters at some dates.
5. Time Series Analysis
[27] After interferogram corrections from residual orbital
errors, phase/elevation correlation and referencing, we pro-
duce mean LOS velocity maps for each track, to investigate
for spatial variations of interseismic strain across the
Haiyuan fault. Interferogram stacking is one way to compute
mean LOS velocity maps [e.g., Peltzer et al., 2001; Wright
et al., 2001]. It reduces random noise by a factor of ffiffiffiffi
N
p,if
N independent interferograms are used [Zebker et al., 1997].
However, it requires proper selection and weighting of
interferograms, based on their signal-to-noise ratios [Cavalié
et al., 2008] and corrections of tropospheric phase delays
beforehand to obtain reliable, unbiased velocity estimates
[Cavalié et al., 2008; Doin et al., 2009]. Time series analysis
is preferably used for large sets of interferograms [e.g., Usai,
1999; Ferretti et al., 2001; Berardino et al., 2002; Schmidt
and Bürgmann, 2003; Hooper et al., 2007; Cavalié et al.,
2007]. It preserves data ordering through time and takes
advantages of redundancy in the spatial and temporal base-
line spaces, so that time-dependent ground deformation can
be distinguished from random atmospheric noise, unwrap-
ping and DEM errors.
[28] We follow a variant of the SBAS approach [Berardino
et al., 2002] described by Lopez-Quiroz et al. [2009] to build
Figure 3. (a) Phase/Elevation correlation plot from Figure 2b (gray dots). Blue line is the linear fit to
data. Red curve is the prediction from global atmospheric model ERA40. Black dashed line is the linear
trend, inverted through a time series analysis, used to correct interferograms, as shown in Figure 2d.
(b) Comparison between delay/elevation ratios derived from InSAR data and ERA40 model. Black/red/
green/blue/purple dots represents respectively tracks 061/333/290/469/240. Black dashed line represents
the unit correlation. The regression coefficient is 0.71. The RMS between delay/elevation ratios derived
from InSAR data and ERA40 are 0.0017 rad.km
1
, 0.0013 rad.km
1
, 0.003 rad.km
1
, 0.0019 rad.km
1
and0.002 rad.km
1
for tracks 061, 240, 290, 333, and 469, respectively.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
5of18

the displacement time series and derive best fit interseismic
velocity maps.
5.1. Constrained Time Series
[29] For each pixel of each track, independently, we con-
sider the following linear equation system:
Fi;j¼X
j1
k¼i
djkand j1¼0;ð3Þ
where j
1
is the phase value at the first acquisition date among
N in the data set, and F
i,j
is the pixel interferometric phase
between dates iand j,anddj
k
are phase increments between
successive dates kand k+ 1. For some pixels, the interfero-
gram network may be separated into independent groups of
interferograms, with no geometrical and temporal overlap.
Equation (3) is usually inverted using a Singular Value
Decomposition method to overcome this limitation [Berardino
et al., 2002]. We favor the inversion approach of Lopez-Quiroz
et al. [2009]: a linear phase model is used to connect inde-
pendent groups of images, as an additional constraint to
equation (3). We also take into account thephase errors that are
a result of DEM errors, correlated with the perpendicular
baseline. The constraining equation is then:
∀l∈2;N½
X
l1
k¼1
djk¼VDtlþeBl
?þc;ð4Þ
where V is the mean LOS velocity for the considered pixel,
Dt
l
=t
l
t
1
is the time interval between acquisition 1 and l,eis
proportional to the DEM error, B
?
l
is the perpendicular base-
line of acquisition l, with respect to the first acquisition and cis
a constant. We combine equations (3) and (4) and invert the
corresponding linear system using a least squares minimization
scheme [Anderson et al., 1999] (Appendix B).
[30] We perform this inversion for each pixel that is
unwrapped in at least 50% of the interferograms. In the end,
we obtain for each track independently the phase increments
at all pixels, the mean LOS velocity and a DEM correction
map in the radar geometry.
5.2. Data Selection
[31] To improve the signal-to-noise ratio of the mean LOS
velocity maps, we select images with the lowest residual
atmospheric noise prior to time series analysis (Table 1). We
first compute the 1-D energy function, Sp, of the residual
noise, for each interferogram with at least 30% of unwrapped
pixels [Puysségur et al., 2007; Cavalié et al., 2008]:
Sp xðÞ¼ 1
NxðÞ X
m;n=dist m;nðÞ¼xjFmFnj;ð5Þ
where N(x) is the number of pairs of pixels mand nseparated by
a distance x,F
m
and F
n
are the interferometric phases of pixels
mand n, respectively. The energy function (or noise spec-
trum) provides an estimate of the noise correlation distance
(30 km) and of the amplitude of the residual turbulent
atmospheric noise for each interferogram (sill value over
30 km, Figure 4). Sp(x) increases from short distances to
30 km, over which it flattens for most interferograms.
[32] We solve for the noise spectrum at each acquisition
date using a least squares inversion of interferogram spectra
(Appendix C). Figures 4c and 4d show the inverted sill values
of the spectra and their associated resolution, for each acqui-
sition date on track 061. The highest (respectively lowest)
spectra values mostly correspond to the summer (respectively
winter) acquisitions, as previously found by from Doin et al.
[2009].
[33] We select radar scenes with low and well resolved
spectra sill values. Our tests show that the selection of scenes
with a sill value below 1 rad and a resolution above 0.75 is a
good compromise in terms of data set decimation and noise
reduction on the LOS velocity maps. Only interferograms
computed from these scenes are used in the time series anal-
ysis. About 25 to 45% of the images are eliminated, depend-
ing on the track number (Table 1). The resulting interferogram
network for track 061 is shown on Figure 4a. The auxiliary
material illustrates the selection process for tracks 240, 290,
333 and 469 together with the temporal coherence map
[Gourmelen et al., 2010].
1
5.3. Analysis of Mean LOS Velocity Maps
[34] Figure 5 shows the LOS velocity maps obtained for two
ascending and three descending tracks after georeferencing to
the ground geometry. The residual noise level of the velocity
maps is greatly reduced compared to that of the corresponding
interferograms (Figure 4b and auxiliary material). It varies from
0.15 rad.yr
1
for track 061 to about 0.25 rad.yr
1
for track 469.
This corresponds to 30–50% of the expected velocity step
across the fault (0.5 rad.yr
1
corresponds to 6 mm yr
1
in fault
parallel velocity, assuming a pure strike-slip motion on a ver-
tical fault) [Cavalié et al., 2008]. The higher noise level on
track 469 (Figure 5) likely results from the lower quality of that
data set, which has fewer images than on other tracks and is
affected by a more significant turbulent tropospheric signal
(Table 1).
[35] The first remarkable feature on Figure 5 is the steep
LOS velocity gradient across the Haiyuan fault, on all tracks.
This step has opposite signs on descending and ascending
tracks, consistent with left-lateral motion, and reaches up to
0.5 rad.yr
1
along LOS (Figures 5 and 6). The shape of the
velocity profiles across the fault is in overall consistent with
a classic arctangent shape predicted by elastic models across
a strike-slip fault [Savage and Burford, 1973]. However,
along-strike variations of the strain distribution are observed,
in the near fault zone in particular, due to the combination of
horizontal and vertical motion or to various degrees of fault
locking in the seismogenic zone.
[36] The velocity gradient across the fault varies from west
to east. While rather smooth along the trace of the 1920 rup-
ture (0.04 rad.yr
1
.km
1
on track 061, or 0.2 LOS-mm yr
1
.
km
1
; Figures 5 and 6a), it becomes sharper and concentrated
in a 35 km-long narrow zone along the Lao Hu Shan (LHS)
segment at the eastern end of the Tianzhu gap (0.12 rad.yr
1
.
km
1
on track 061 or 0.5 LOS-mm yr
1
.km
1
; Figures 5 and
6b). Furthermore, the LOS velocity step observed in the
near fault zone (up to 1.4 rad.yr
1
, or 6 LOS-mm yr
1
)is
higher than that in the far field (0.5 rad.yr
1
or 2 LOS-mm
yr
1
; Figure 6b). These observations suggest that the upper
1
Auxiliary materials are available in the HTML. doi:10.1029/
2011JB008732.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
6of18

seismogenic part of the crust is fully locked along the 1920
rupture segment and is creeping along the LHS segment, at
a rate that may exceed the tectonic loading rate at depth.
[37] In between these 2 segments, the left-stepping releas-
ing jog (10 km-long and 5 km-wide) shows a LOS velocity
increase on both ascending and descending tracks (Figures 5
and 6c). This indicates a vertical motion, away from satellite,
consistent with subsidence in a pull-apart basin (“Jingtai
Basin”hereafter). Other subsiding areas are visible in the
central part of the 1920 rupture, most likely corresponding to
mining or other human-related activities.
[38] A velocity gradient of up to 0.25 rad.yr
1
along the
LOS is also visible about 40 km north of the Haiyuan fault
and may be associated with predominantly left-lateral motion
on the Gulang fault.
6. Fault Slip-Rate Modeling
[39] To investigate further the spatial variations of the
strain rate along the different segments of the fault, we invert
the LOS velocity maps to estimate a slip-rate on the deep
part of the fault and the slip-rate distribution along its shal-
low part.
6.1. Model Geometry and Parametrization
[40] Our fault model is based on the following simplifi-
cations: (1) slip on the deep section of the fault, below the
20 km seismogenic depth, estimated from the micro-seis-
micity distribution (Figure 7) and seismological studies
[Lasserre et al., 2001], is assumed to be purely horizontal
and uniform, (2) slip on the shallow section can vary along
strike in amplitude and rake. Following, Cavalié et al.
[2008], we assume that the fault is vertical. Its shallow part
is divided into two segments, corresponding to the eastern
end of the Tianzhu gap and to the 1920 rupture, following
the fault surface trace mapped from satellite images and
fieldwork. It is discretized into 512 2.5 2.5 km patches
(Figure 7). The deep section of the fault is considered as a
single dislocation following a smoothed trace with respect to
that at the surface.
[41] We solve for (1) both the strike-slip and dip-slip com-
ponents of the slip rate on the shallow patches, (2) a uniform
strike-slip rate at depth, (3) a bilinear ramp in longitude and
Figure 4. For Track 061: (a) Relative perpendicular baseline of all radar images as a function of their
acquisition dates. Dashed lines indicate processed images pairs. Black lines show interferograms selected
for Time series analysis. (b) Noise energy function Sp as a function of distance for each interferogram, nor-
malized by the temporal baseline. Black dashed line is the mean LOS velocity map noise energy function.
(c) Noise energy spectrum at 30 km for each interferogram, obtained by inversion. Error bars are 1 sigma.
Acquisition with Sp(30) greater than 1 rad are rejected. (d) Resolution of inverted Sp(30) values. Acquisi-
tions with resolution less than 0.75 are rejected.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
7of18

Figure 5. Mean Line-Of-Sight velocity maps from time series analysis for each track. One color cycle (yellow/pink/green)
is 9 mm/yr toward the satellite. Red line follows the 1920 rupture trace along the Haiyuan fault. Blue line follows the
Tianzhu seismic gap. Dark thin lines indicate secondary faults. Boxes show location of profiles on Figure 6. Background
shade is from SRTM DEM.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
8of18

Figure 6. Mean Line-Of-Sight velocity profiles (black lines) with 2-sigma deviation (grey lines). Dashed black lines show
preferred model of Figure 7, corresponding elevation profiles are shown at bottom. Profile location are shown on Figure 5:
(a) 1920 rupture, (b) Tianzhu gap and (c) Jingtai Basin.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
9of18

latitude and a constant to correct for residual orbital errors for
each LOS velocity map. We use the generalized least squares
solution [Tarantola,2005]:
mpost ¼mprior þðGtC1
DGþC1
MÞGtC1
Ddobs Gmprior

:ð6Þ
m
prior
and m
post
are the vectors of “apriori”and “aposteriori”
model parameters (m
prior
is the null vector). Vector d
obs
con-
tains the LOS velocity values for all pixels covered by at least
one ascending and one descending track after data decimation.
We subsample each LOS velocity map using a quadtree
algorithm based on the spatial phase gradient[Welstead, 1999;
Sudhaus and Jònsson, 2009]. The maximum quadtree box
size is 16 16 pixels (approx. 700 700 m
2
). Because of
uncertainties in the fault trace location, we eliminate pixels
adjacent to the fault surface trace (less than 0.5 km from the
fault; we choose 0.5 km so that slip on the shallowest patches
remains constrained). The Gmatrix contains the LOS velocity
kernels on each subsampled data point computed for unit slip-
rate values on each fault patch. We model the 3D surface
displacement using the analytical solution of Okada [1985] for
a rectangular dislocation embedded in a semi-infinite homo-
geneous elastic half-space. Projection of these displacements
into the LOS takes into account for the incidence angle at each
pixel location. Additional terms in Gare related to the mod-
eling of residual orbital ramps.
[42] The model covariance matrix C
m
is used to smooth
the slip-rate solution on the shallow section of the fault
[Radiguet et al., 2011]. It is defined as
Cmi;jðÞ¼sml0
l

2
edi;jðÞ
2l;ð7Þ
where s
m
is the “a priori”standard deviation of the slip-rate
model parameters, fixed at 10 mm yr
1
,lis the correlation
length (i.e. the characteristic smoothing distance), l
0
is a
scaling factor fixed at 2.5 km, which corresponds to the
mean distance between adjacent, shallow patches and d(i,j)
Figure 7. Vertical 2.5 2.5 km
2
gridded fault model in the upper 20 km, with inverted shallow slip rate
distribution and associated standard deviation: (a, b) strike-slip and (c, d) dip-slip components. Positive
values are for east ward and uplift motion south of the fault, respectively. Blue and red line outline the
Tianzhu gap and 1920 rupture fault traces. Grey shaded fault patches correspond to patches on which
model is poorly resolved (R < 0.63).
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
10 of 18

is the distance between patches iand j. The exponential form
in equation (7) allows more smoothing at long distances and
more variability at small distances than a Gaussian form
[Radiguet et al., 2011]. The strike-slip and the dip-slip com-
ponents on fault patches are independent, so thatC
m
is built as
two independent blocks, one for each component. “A priori”
covariances on the strike-slip rate at depth and on the bilinear
ramp terms are assumed independent from any other param-
eter and set high enough to ensure a sufficient degree of
freedom (i.e. practically, we progressively increase s
m
until
the best fit parameters are independent from s
m
; Other terms
in C
m
are zeros.).
[43]C
D
is the downsampled data covariance matrix that
takes into account the residual spatially correlated noise of
the mean LOS velocity maps, using the empirical covariance
function of each map [Sudhaus and Jònsson, 2009],
(Figure 8 and Appendix D).
6.2. Inversion Results
[44] We perform the inversion for different values of the
correlation lengths l
Strike
and l
Dip
for the shallow strike-slip
and dip-slip rates, respectively. Each solution is characterized
by a data-model RMS and a roughness:
r¼Pr2mpost



2Np
;ð8Þ
where r2mpost is the spatial Laplacian of the slip rate
distribution and N
p
is the number of fault patches [Jònsson
et al., 2002]. We use the L-curve criterion to determine the
optimal smoothing [Hansen, 1992]. Our preferred model is
chosen for l
Strike
= 12 km and l
Dip
= 16 km (Figure 7), as
a good compromise between model roughness and the
RMS (Figure 9). The “a priori”RMS, computed with
m
prior
,is1mmyr
1
. The “a posteriori”RMS, computed
from the preferred m
post
, is 0.6 mm yr
1
. The fit to data is
shown on profiles on Figure 6. Residuals are shown in the
auxiliary material as well as three maps showing the modeled
East directed, North directed and vertical displacement rate.
An oversmoothed model with a large correlation length (l≫
100 km) would give an “a posteriori”RMS around 0.7 mm
yr
1
, suggesting that most of the RMS decrease between
“apriori”and “aposteriori”models is due to the modeling of
Figure 8. Empirical covariogram (grey dots) and fitted covariance functions (continuous lines) for
each track.
Figure 9. Data-model root mean square as a function of dip-slip rate and strike-slip rate roughnesses.
Preferred model is shown by red dot.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
11 of 18

the slip rate on the deep section of the fault, fitting far field
observations. Uncertainties on the model parameters, as well
as trade-offs between parameters (Figure 10), are computed
from the “aposteriori”model covariance matrix (details are
given in Appendix E).
[45] The inverted, deep left-lateral slip rate is 5.3 1.0 mm
yr
1
, in overall agreement with previous InSAR or GPS
derived studies [Gan et al., 2007; Cavalié et al., 2008]. As
the spatial wavelength of the residual orbital ramps is com-
parable to that of the far field deformation, the deep slip rate
is correlated with residual orbital parameters (Figure 10). The
correlation is low with the longitude ramp (0.2, positive for
descending orbits, negative for ascending orbits), and high
with the latitude ramp (0.7), as expected from the fault
orientation. This correlation results in a deep slip rate varia-
tion of 0.6 mm yr
1
, which remains below the 1serror on
the deep slip rate (1mmyr
1
).
[46] The slip rate on the shallow part of the fault is highly
variable (Figure 7). Three distinct sections can be identified.
[47] 1. The western section of the Tianzhu gap, west of the
Lao Hu Shan, can be considered as locked. Shallow slip rate
values do not exceed 2 mm yr
1
, on the order of uncer-
tainties for both slip components.
[48] 2. The fault section that ruptured during the 1920
Haiyuan earthquake appears locked as well. Fault patches at
the eastern edge of this section show dip-slip motion. How-
ever, they are associated with poor resolution (<0.6) at the
model edges. This likely results from the poor data quality in
this area, as tracks 290 and 469 have the highest noise level
(Figure 8).
[49] 3. In between the two locked sections, a creeping
zone is observed along the eastern end of the Tianzhu gap, as
inferred from the mean LOS velocity maps (Figure 5). It
extends for about 35 km along strike, down to the imposed
20 km depth, with most of the creep concentrated between 5
and 15 km depth. Slip is mostly strike-slip, with a maximum
rate of 8 2mmyr
1
, and a mean rate of 5 1.5 mm yr
1
.
The strong subsidence observed in the Jingtai basin
(Figures 5 and 7) is partly explained by a very localized dip-
slip motion on the southern bound of the basin, with a rate of
30.5 mm yr
1
(Figure 6).
[50] The correlation between the estimated strike-slip rates
on shallow fault patches and the deep slip rate increases with
depth but remains low, below 0.18. The correlation between
the shallow dip-slip rate and the deep slip rate is even lower,
0.03 at maximum. This suggests that the inversion of shallow
slip rate values is poorly sensitive to the determination of the
deep slip rate. Finally, we note that the values of the resolution
for the dip-slip values are overall larger than that of the strike-
slip values, likely due to the InSAR acquisition geometry.
7. Discussion
7.1. Model Limitations
[51] All previous InSAR studies of interseismic deforma-
tion in China rely on the inversion of average LOS velocity
profiles across faults, using 2-dimensional fault models in an
elastic half-space [Wright et al., 2004; Taylor and Peltzer,
2006; Cavalié et al., 2008; Elliott et al., 2008; Wang et al.,
2009] or thin plate modeling that takes into account varia-
tions in the properties of the medium on both sides of the
fault [Jolivet et al., 2008]. Along-strike strain variations are
generally neglected. This study shows that such variations
can be detected along faults in Tibet, thanks to the large
radar data archive acquired by the ERS and Envisat satellites
and an appropriate processing scheme, as for the well-
documented San Andreas fault in California [e.g., Ryder and
Bürgmann, 2008].
[52] However, some limitations remain in our modeling
related in particular to the simplified geometry and dis-
cretization of the modeled fault, and to kinematic assump-
tions. The deep slip-rate likely varies along-strike, as the fault
veers to the South-East and splays into several branches, east
of the Yellow river. Indeed, estimates of the long-term
Holocene slip rate decrease from west to east [Lasserre,
2000; Li et al., 2009]. Furthermore, the deep slip is proba-
bly not purely horizontal, given the complex 3-D geometry of
the fault system [Gaudemer et al., 1995]. Given the loss of
resolution with depth and the difficulty of modeling long
spatial wavelengths of the signal, we cannot account for such
complexities at depth.
[53] Figure 6 also shows that our model does not correctly
reproduce the observed velocity near the fault. The fit could
probably be improved by assuming smaller fault patches at
shallow depth (i.e. less than 2.5 km depth), a variable cor-
relation length (i.e. smoothing) and introducing slight var-
iations of the fault dip angle along strike at shallow depth.
[54] Finally, we ignore the influence of the Gulang strike-
slip fault on the surface velocity field. We quantify below
the consequences of this assumption on the estimate of the
Haiyuan fault slip-rate.
7.2. Tectonic Loading Rate
[55] The Gulang fault splays eastward from the Haiyuan
fault in between the Leng Long Ling and the Jing Qiang He
segments and merges with the Tianjing Shan thrust, east of
the Yellow river (Figure 1). It acts as a south-dipping lateral
thrust ramp branching off the Haiyuan fault [Gaudemer et al.,
1995]. Both the Haiyuan fault and the Gulang fault should be
taken into account to estimate the present-day tectonic load-
ing between North Eastern Tibet to the South and the Gobi-
Ala Shan platform to the North.
[56] A subtle velocity gradient can be seen along the Gulang
fault trace in the LOS velocity maps of descending tracks
61, 333 and of the ascending track 240 (Figure 5) and along
associated profiles (Figure 6). The velocity gradient is con-
spicuous near 104E, about 40 km north of the Haiyuan
fault, with several strands forming a wide pushup structure,
and becomes sharper near 104.5E, after stepping to the
north (Figures 5, 6a and 6b). However, the Gulang fault
related velocity gradient is hardly detectable on the ascend-
ing track 469, most likely because of the higher noise level
(Figure 8). This velocity change is consistent with left-lateral
slip on the Gulang fault, probably combined with North-
directed thrust motion, and is about one third of that observed
across the Haiyuan fault. This seems consistent with the
loading rate difference between the Gulang and Haiyuan
faults, estimated from other geodetic (1.3 mm yr
1
and
8.6 mm yr
1
,respectively[Gan et al., 2007]) or tectonic
studies (4 mm yr
1
and 10 mm yr
1
,respectively[Gaudemer
et al.,1995]).
[57] Neglecting the Gulang fault in the modeling partly
biases the estimate of the Haiyuan fault deep slip-rate. We
therefore test the robustness of our result by masking out
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
12 of 18

Figure 10. Model parameter tradeoffs: probability density function from the “a posteriori”model covari-
ance matrix between deep slip rate and each of the orbital ramp terms (along longitude, latitude and con-
stant). Contour for PDF values of 0.5, 0.68 (i.e. s) and 0.95 are shown in black.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
13 of 18

the Gulang fault area in the velocity maps and inverting
a subset of the surface data around the Haiyuan fault.
The “a priori”model RMS for this data subset is 1 mm yr
1
,
while its “a posteriori”RMS is 0.6 mm yr
1
. The inverted
parameters slightly differ from those of the previous model:
no significant changes are observed on the shallow slip esti-
mates, the residual orbital parameters vary by less than 5%
and the deep slip rates are comparable for both models within
the error bars (6.1 1mmyr
1
instead of 5.3 1mmyr
1
).
We emphasize that further quantification and model refine-
ment are out of the scope of this paper, as an extended study
of the 3D geometrical complexity of the fault system at depth
would probably require a wider spatial coverage of the area.
[58] Finally, we conclude on a low present-day loading rate
of the Haiyuan fault along the studied segments, 5mm
yr
1
. This is in keeping with recent GPS-derived and ERS
InSAR studies [Cavalié et al., 2008; Loveless and Meade,
2011], but contrasts with the significantly higher estimates
of the long-term, Holocene rate on the same fault segment
[Lasserre et al., 1999]. Uncertainties on Holocene slip rate
estimations as well as complex time varying fault behavior
during a single or several seismic cycles could explain such
discrepancy [He and Chery, 2008].
7.3. Shallow Creep
[59] The existence of a shallow, 35 km-long, slipping zone
at the junction between two locked fault segments, one that
ruptured during the M
w
8, 1920 earthquake, and one con-
sidered as a seismic gap in the late stage of its seismic cycle,
is intriguing and raises many questions. The characteristics
of this shallow slip, the relationship with the current seismic
activity and past earthquakes, the potential mechanisms, as
well as the implications on the seismic hazard in the Tianzhu
gap area, are discussed below.
[60] The shallow slipping zone along the Lao Hu Shan
segment (LHS) is coincident with a cluster of micro-earth-
quakes, in contrast with the two locked segments on both sides
(Figures 1 and 7). Over the observation time period (2003–
2009), the geodetic moment released by the shallow slip is
7.9 10
17
N.m (equation M
w
5.9), more than 30 times
larger than the cumulative seismic moment of the recorded
earthquakes of 2.5 10
16
N.m, indicating that most of the
observed shallow slip is aseismic. The corresponding average
creep rate, 5mmyr
1
, is similar to the loading rate at depth,
suggesting that there is, in overall, no stress increase along the
LHS segment between 2003 and 2009. However, locally, the
aseismic slip rate reaches values slightly higher than the loading
rate (Figure 7). Temporal fluctuations of the creep rate thus
may have occurred during the studied period, with episodic
bursts that may be related to the microseismic activity.
[61] Similar observations of shallow creep have been made
along different sections of the Pacific - North America plate
boundary, in California. Reported creep rates along the San
Andreas Fault are lower than, or equal to the plate loading
rate, and vary along strike [Lyons and Sandwell, 2003;
Schmidt et al., 2005; Funning et al., 2007; Ryder and
Bürgmann, 2008] and through time [e.g., de Michele et al.,
2011]. Interactions between aseismic slip, microearthquakes
and larger earthquakes have been investigated. Lohman
and McGuire [2007] explain a seismic swarm near the
Salton Trough as being driven by aseismic processes, using
the rate and state friction formalism [e.g., Dieterich, 1994].
Waldhauser et al. [2004] suggest that such fault behavior,
including creeping and locked segments, and seismicity
features at shallow depth, such as streaks of microearthquakes
along creeping fault zones [Rubin et al., 1999] or gaps in the
seismicity, may relate to long-lived geometrical, frictional
or rheological variations along the fault. Johanson and
Burgmann [2005] suggest that segments devoid of micro-
seismicity are likely locked and prone to generating large
ruptures, while segments that exhibit strong micro- to moder-
ate seismic activity mostly release strain by aseismic slip.
[62] We have very few elements to discuss whether the
shallow creep process along the Haiyuan fault can be
considered as transient or in a steady state at the scale of
the seismic cycle. The stress perturbations induced by the
1920 and 1927 earthquakes (both shear stress increase and
normal stress decrease along the LHS segment) may have
triggered the transient creep and the cluster of microseis-
micity along this segment. Such a triggering process can be
described by rate and state friction laws along velocity
strengthening fault segments [Dieterich, 1994]. High pres-
sure fluids may circulate in the fault zone and are com-
monly invoked as contributing to the normal stress decrease
on the fault plane, thus favoring transient or steady state
creep [Morrow et al., 2000]. Left-lateral slip and reverse slip
on a south-dipping Gulang fault should also decrease the
normal stress along the Haiyuan fault. However, there is no
clear reason why it should occur exclusively on the LHS
segment. The fault zone composition and structure may play
an important role as well. The presence of weak minerals,
like talc, serpentinite or saponite, has been proposed as a
mechanism for decreasing the friction coefficient along
faults, favoring stable sliding or transient creep at depth
as shallow as 0–4km[Moore and Rymer, 2007; Lockner
et al., 2011]. In gouge zones in the upper crust, pressure
solution mechanism is another possible creep mechanism
that may dominate over other processes below a few kilo-
meters from the surface. The nature of grains in the fault
gouge, their size and spatial organization strongly influence
whether creep is transient or permanent and control the fault
seismic behavior [Gratier et al., 2011]. A 500 m-wide shear
zone with serpentine boudins has been observed in the LHS,
south of the active Haiyuan fault trace. This trace is also
marked by a 60 m-wide gouge zone, containing gypsum
crystals [Lasserre, 2000].However, shear zones and gouge
are observed elsewhere along the fault and not always lead to
shallow creep. Further geological and geodetic observations
would be necessary to conclude on the mechanisms of the
observed creep and its temporal characteristics.
[63] The occurrence of a future large earthquake on the
Tianzhu gap remains a plausible threat, with an expected
moment magnitude of 8 to break 260 km-long, 20 km-deep
fault section bearing a 5 m slip deficit accumulated over
1000 years. Large ruptures often nucleate near major fault
bends and jogs [King and Nabelek, 1985; Wesnousky, 2006],
and may result from stable sliding acceleration, as modeled
by Lapusta and Liu [2009] and recently observed by
Bouchon et al. [2011]. Because the observed shallow creep
on the Haiyuan fault lies at the eastern end of the Tianzhu
gap, near a major step over of the fault system, and shows
evidence of episodic slip rate increase in the recent years,
one may speculate that the LHS fault segment is currently
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
14 of 18

the locus of a process that will trigger the next large event on
the Haiyuan Fault.
8. Conclusion
[64] The time series analysis of interferograms from three
descending and two ascending tracks of the Envisat satellite
allows us to investigate the spatial variations of interseismic
strain along the Haiyuan fault system between 2003 and
2009. The maps of the LOS surface velocity are consistent
with a left-lateral motion across the fault and reveal a narrow,
35 km-long zone of high strain, near the junction between
the Tianzhu seismic gap and the fault section, which broke in
the 1920, M
w
8 Haiyuan earthquake. The slip rate distribu-
tion on the fault plane derived from data inversion shows
along-strike variations. At shallow depths (0–20 km), the
western part of the Tianzhu gap and the fault section that
ruptured in 1920 appear to be locked and are devoid of
seismic activity. The 35 km-long Lao Hu Shan segment
between the locked sections experiences shallow slip and is
coincident with a cluster of micro-earthquakes. This shallow
slip is interpreted as due to a creeping process, producing
mostly left-lateral movement and a zone of subsidence within
the Jingtai pull-apart basin. The average shallow slip-rate
(5mmyr
1
) is comparable in magnitude with the estimated
loading rate at depth, assumed to be constant along the fault.
This suggests the stress is not increasing on the LHS segment
at the present time. However, the data may suggest that some
episodic accelerations of the creep have occurred during the
study period, a transient process that could be the manifes-
tation of the nucleation mechanism of a future event that may
eventually rupture the Tianzhu seismic gap. A better char-
acterization of the inferred transient would require further
analysis of the temporal fluctuations of the surface velocity
field at the decadal scale, combined with seismological and
geological studies. The present study, in agreement with a
recent one along the San Andreas fault [de Michele et al.,
2011], emphasizes the need for continuous monitoring of
creeping segments in the vicinity of major seismic gaps as
they should provide hints on seismic hazard assessments.
Appendix A: Image Coregistration
[65] Empirical distortions between master and slave images
are first estimated using an amplitude correlation technique. In
addition, the slave image distortion in range, with respect to
the master image, is predicted using the orbital parameters and
the DEM projected in radar geometry at full resolution. The
predicted distortion map is then matched to the empirical off-
set map by adding an optimized translation, before being used
as a pixel by pixel grid for resampling the slave image. Dis-
torsions in azimuth are adjusted by a 2D quadratic polynomial:
fR;AðÞ¼aA2þbRA þcA2þdR þeA þf;ðA1Þ
where Rand Aare the pixel’s range and azimuth, respectively,
and ato fare polynomial coefficients to be estimated. This
procedure ensures accurate sub-pixel coregistration for large
perpendicular baselines and steep topography.
Appendix B: Time Series Analysis
[66] For each pixel of each track, independently, we solve
the following system, based on equations (3) and (4):
Dis the design matrix. b
i
are weighting parameters for each
image depending on their noise level, as defined in
Appendix C. The gparameter is set low enough (0.01 in our
case) so that: (1) the phase increments dj
k
are fully con-
strained by equation (3), and equation (4) provides the best
fit velocity Vand DEM error e; (2) when the inversion is
undetermined, equation (4) constrains the phase offset
between independent groups of images [Lopez-Quiroz et al.,
2009].
Appendix C: Interferogram Selection
[67] We assume that the noise energy function (or spec-
trum) of an interferogram Sp(x)
i,j
(equation (5)) is the sum
of the noise energy functions of the two corresponding
acquisitions at dates iand j, Sp(x)
i
and Sp(x)
j
:
Sp xðÞ
i;j≃Sp xðÞ
iþSp xðÞ
j:ðC1Þ
We invert equation (C1) using the least squares solution to
estimate each scene noise energy function, their associated
errors and resolution [Tarantola, 2005]:
m¼GtCDG

1Gd;ðC2Þ
where dcontains the spectrum values of all interferograms,
mcontains the spectrum values of all acquisitions and Gis
the design matrix. Diagonal terms of the data covariance
Fi;j
.
.
.
Fk;l
0
.
.
.
0
0
B
B
B
B
B
B
B
@
1
C
C
C
C
C
C
C
A
¼
D




















000
.
.
..
.
.
000




















g:
b1
b2
b3
b4
.
.
.
bN
00 …0
10 0
110 0
.
.
..
.
.
111…100
111 1 …10
0B1
perp 1
Dt2B2
perp 1
Dt3B3
perp 1
.
.
..
.
..
.
.
DtN1




























0
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
@
1
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
A
dj1
.
.
.
djN1
V
e
c
0
B
B
B
B
B
B
B
@
1
C
C
C
C
C
C
C
A
:ðB1Þ
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
15 of 18

matrix C
d
are the variances of the corrected interferograms,
while off-diagonal terms are zero.
[68] Each weighting parameter b
i
in (equation B1) is the
inverse of the spectrum maximum for acquisition i, normal-
ized by the sum of all acquisition spectrum maximum. This
way, the relative weight of the constrained part of the matrix
(equation (4)) is equal to g, with respect to the upper part.
Appendix D: Data Covariance Matrix
[69] We compute the empirical semi-variogram of each
full-resolution LOS velocity map using the following equa-
tion [Chilès and Delfiner, 1999]:
gxðÞ¼ 1
2NxðÞ X
m;n=dist m;nðÞ¼x
VmVn
ðÞ
2;ðD1Þ
where g(x) is the semi-variogram value at distance x,N(x)is
the number of pixel pairs separated by the distance xand V
m
and V
n
are the LOS velocities of pixels mand n. As for the
energy function in equation (5), there exists a sill value g
S
over a distance of ≃30 km beyond which noise is uncorre-
lated. Considering the residual noise as second-order sta-
tionary (i.e. not dependent on the position) and isotropic, it is
possible to build the two dimensional covariance function
Cov(x) for each LOS velocity map from the semi-variogram
using the following equation:
Cov xðÞ¼gSgxðÞ:ðD2Þ
Following Sudhaus and Jònsson [2009], we fit each
covariance function either with an exponential decay a. e
bx
or an exponential decay combined with a cosine term
a. e
bx
cos(wx) (Table 1 and Figure 8). To ensure that the
covariance functions remain positive-definite, we impose
w<b[Chilès and Delfiner, 1999]. We use these covariance
functions to build the full data covariance matrix C
Df
.
The downsampled data covariance matrix C
D
used in
equation (6) is related to C
Df
through the linear quadtree
operator Q[Sudhaus and Jònsson, 2009]:
CD¼QCDf :ðD3Þ
This ensures a high (respectively low) weight to data points
representing a large (respectively small) area.
Appendix E: Inversion Quality Tools From
Tarantola [2005]
[70] A posteriori errors on the model parameters are
given by
Cmpost ¼GtC1
DGþC1
m

1;ðE1Þ
where Gis the theory matrix, C
D
is the data covariance
matrix (D) and C
m
is the model covariance matrix. The
diagonal terms of C
mpost
are the variance of each parameter,
while the non-diagonal terms give the covariances between
parameters.
[71] The correlation r
ij
between two parameters iand j
(1, 0 and 1 for anti-correlated, not correlated and corre-
lated, respectively) is defined as
rij ¼Cmpost i;jðÞ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Cmpost i;iðÞCmpost j;jðÞ
p:ðE2Þ
[72] The resolution operator Ris given by
R¼CmGtGCmGtþCD

1G:ðE3Þ
Because we impose a spatial smoothing on fault patches
through the model covariance matrix C
m
, slip value on a
patch depends on the slip of the neighboring patches. We
thus express the resolution of each slip parameter as the sum
of all terms in the corresponding line of the resolution
operator R. A fully resolved (respectively unresolved)
parameter has a resolution of 1 (respectively 0).
[73]Acknowledgments. The SAR data set was provided by the
European Space Agency (ESA) in the framework of the Dragon 2 program
(ID 2509 and 5305). This program also supported R. Jolivet’s work,
through the Young Scientist fellowship. Funding was provided by the
French “Extraction et Fusion d’Information et de Données d’Interférométrie
Radar”program (EFIDIR, ANR, France) and Programme National de Télé-
détection Spatiale (CNES). Part of G. Peltzer’s contribution was done at the
Jet Propulsion Laboratory, California Institute of Technology, under con-
tract with NASA. Figures and map were prepared using Generic Mapping
Tools software [Wessel and Smith, 1995]. The authors thank Gareth Fun-
ning, an anonymous reviewer, and the Associate Editor for their construc-
tive comments and suggestions.
References
Ambraseys, N. N. (1970), Some characteristic features of anatolian fault
zone, Tectonophysics,9(2–3), 143–165.
Anderson, E., et al. (1999), LAPACK Users’Guide, 3rd ed., Soc. for Indust.
and Appl. Math., Philadelphia, Penn.
Berardino, P., G. Fornaro, R. Lanari, and E. Santosti (2002), A new algo-
rithm for surface deformation monitoring based on small baseline differ-
ential SAR interferograms, IEEE Trans. Geosci. Remote Sens.,40(11),
2375–2383, doi:10.1109/TGRS.2002.803792.
Biggs, J., T. Wright, Z. Lu, and B. Parsons (2007), Multi-interferogram
method for measuring interseismic deformation: Denali fault, Alaska,
Geophys. J. Int.,170(3), 1165–1179, doi:10.1111/j.1365-246X.2007.
03415.x.
Bouchon, M., H. Karabulut, M. Aktar, S. Ozalaybey, J. Schmittbuhl, and
M.-P. Bouin (2011), Extended nucleation of the 1999 M-w 7.6 Izmit
earthquake, Science,331(6019), 877–880, doi:10.1126/science.1197341.
Cavalié, O., M. P. Doin, C. Lasserre, and P. Briole (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, B03403, doi:10.1029/
2006JB004344.
Cavalié, O., C. Lasserre, M. P. Doin, G. Peltzer, J. Sun, X. Xu, and Z. K.
Shen (2008), Measurement of interseismic strain across the Haiyuan fault
(Gansu, China), by InSAR, Earth Planet. Sci. Lett.,275(3–4), 246–257,
doi:10.1016/j.epsl.2008.07.057.
Çakir, Z., A. Akoglu, S. Belabbes, S. Ergintav, and M. Meghraoui (2005),
Creeping along the ismetpasa section of the North Anatolian fault (western
Turkey): Rate and extent from InSAR, Earth Planet. Sci. Lett.,238(1–2),
225–234, doi:10.1016/j.epsl.2005.06.044.
Chilès, J.-P., and P. Delfiner (1999), Geostatistics - Modeling Spatial
Uncertainty, John Wiley, New York.
Chlieh, M., J. P. Avouac, K. Sieh, D. H. Natawidjaja, and J. Galetzka
(2008), Heterogeneous coupling of the Sumatran megathrust constrained
by geodetic and paleogeodetic measurements, J. Geophys. Res.,113,
B05305, doi:10.1029/2007JB004981.
Cowgill, E. (2007), Impact of riser reconstructions on estimation of secular
variation in rates of strike-slip faulting: Revisiting the Cherchen River site
along the Altyn Tagh Fault, NW China, Earth Planet. Sci. Lett.,254(3–4),
239–255, doi:10.1016/j.epsl.2006.09.015.
de Michele, M., D. Raucoules, F. Rolandone, P. Briole, J. Salichon,
A. Lemoine, and H. Aochi (2011), Spatiotemporal evolution of surface
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
16 of 18

creep in the Parkfield region of the San Andreas fault (1993–2004) from
synthetic aperture radar, Earth Planet. Sci. Lett.,308(1–2), 141–150,
doi:10.1016/j.epsl.2011.05.049.
Deng, Q., et al. (1986), Variations in the geometry and amount of slip on the
Haiyuan (Nanxihaushan) fault zone, China, and the surface rupture of the
1920 Haiyuan earthquake, in Earthquake Source Mechanism,Geophys.
Monogr. Ser., vol. 37, pp. 169–182, edited by S. Das, J. Boatwright,
and C. Scholtz, AGU, Washington, D. C.
Dieterich, J. H. (1994), A constitutive law for rate of earthquake production
an its application to earthquake clustering, J. Geophys. Res.,99,2601–2618,
doi:10.1029/93JB02581.
Doin, M. P., C. Lasserre, G. Peltzer, O. Cavalie, and C. Doubre (2009),
Corrections of stratified tropospheric delays in SAR interferometry: Val-
idation with global atmospheric models, J. Appl. Geophys.,69,35–50,
doi:10.1016/j.jappgeo.2009.03.010.
Doin, M. P., S. Guillaso, R. Jolivet, C. Lasserre, F. Lodge, G. Ducret, and
R. Grandin (2011), Presentation of the small baseline NSBAS processing
chain on a case example: The etna deformation monitoring from 2003 to
2010 using ENVISAT data, Eur. Space Agency Spec. Publ.,ESA SP-697,
1–7.
Doubre, C., and G. Peltzer (2007), Fluid-controlled faulting process in
the Asal Rift, Djibouti, from 8 yr of radar interferometry observations,
Geology,35(1), 69–72, doi:10.1130/G23022A.1.
Dragert, H., K. Wang, and T. S. James (2001), A silent slip event on the
deeper Cascadia subduction interface, Science,292(5521), 1525–1528,
doi:10.1126/science.1060152.
Elliott, J. R., J. Biggs, B. Parsons, and T. J. Wright (2008), InSAR slip rate
determination on the Altyn Tagh Fault, northern Tibet, in the presence of
topographically correlated atmospheric delays, Geophys. Res. Lett.,35,
L12309, doi:10.1029/2008GL033659.
Farr, T. G., and M. Kobrick (2000), Shuttle radar topography mission
produces a wealth of data, Eos Trans. AGU,81(48), 583–585.
Ferretti, A., C. Prati, and F. Rocca (2001), Permanent scatterers in SAR
interferometry, IEEE Trans. Geosci. Remote Sens.,39(1), 8–20.
Funning, G. J., R. Bürgmann, A. Ferretti, F. Novali, and A. Fumagalli
(2007), Creep on the Rodgers Creek Fault, northern San Francisco bay
area from 10 year ps-insar data set, Geophys. Res. Lett.,34, L19306,
doi:10.1029/2007GL030836.
Gan, W., P. Z. Zhang, Z.-K. Shen, Z. Niu, M. Wang, Y. Wan, D. Zhou, and
J. Cheng (2007), Present-day crustal motion within the Tibetan Plateau
inferred from GPS measurements, J. Geophys. Res.,112, B08416,
doi:10.1029/2005JB004120.
Gaudemer, Y., P. Tapponnier, B. Meyer, G. Peltzer, S. M. Guo, Z. T. Chen,
H. G. Dai, and I. Cifuentes (1995), Partitioning of crustal slip between
linked, active faults in the eastern Qilian Shan, and evidence for a major
seismic gap, the Tianzhu Gap, on the western Haiyuan Fault, Gansu
(China), Geophys. J. Int.,120(3), 599–645.
Goldstein, M. R., and L. C. Werner (1998), Radar interferogram filtering for
geophysical applications, Geophys. Res. Lett.,25(21), 4035–4038,
doi:10.1029/1998GL900033.
Goldstein, M. R., A. H. Zebker, and C. L. Werner (1988), Satellite radar
interferometry: Two-dimensional phase unwrapping, Radio Sci.,23(4),
713–720, doi:10.1029/RS023i004p00713.
Gourmelen, N., F. Amelung, and R. Lanari (2010), Interferometric syn-
thetic aperture radar-GPS integration: Interseismic strain accumulation
across the hunter mountain fault in the eastern California shear zone,
J. Geophys. Res.,115, B09408, doi:10.1029/2009JB007064.
Gratier, J.-P., J. Richard, J. Renard, F. Mittempergher, M.-L. Doan, G. Di Toro,
J. Hadizadeh, and A.-M. Boullier (2011), Aseismic sliding of active faults
by pressure solution creep: Evidence from the san andreas fault observa-
tory at depth, Geology,39, 1131–1134.
Guillaso, S., A. Reigber, L. Ferro-Famil, and E. Pottier (2006), Range
resolution improvement of airborne SAR images, IEEE Geosci. Remote
Sens. Lett.,3(1), 135–139, doi:10.1109/LGRS.2005.859943.
Guillaso, S., C. Lasserre, M.-P. Doin, O. Cavalié, J. Sun, and G. Peltzer
(2008), InSAR measurement of interseismic strain in areas of low coher-
ence: Example across the Haiyuan Fault (Gansu, China) using a local
InSAR adaptive range filter, paper presented at General Assembly, Eur.
Geosci. Union, Vienna.
Hansen, P. C. (1992), Analysis of discrete ill-posed problems by means of
the L-curve, SIAM Rev.,34(4), 561–580.
He, J., and J. Chery (2008), Slip rates of the Altyn Tagh, Kunlun and
Karakorum faults (Tibet) from 3D mechanical modeling, Earth Planet.
Sci. Lett.,274(1–2), 50–58, doi:10.1016/j.epsl.2008.06.049.
Hetland, E., and B. Hager (2006), Interseismic strain accumulation: Spin-up,
cycle invariance, and irregular rupture sequences, Geochem. Geophys.
Geosyst.,7, Q05004, doi:10.1029/2005GC001087.
Hooper, A., P. Segall, and H. Zebker (2007), Persistent scatterer interfero-
metric synthetic aperture radar for crustal deformation analysis, with
application to Volcan Alcedo, Galapagos, J. Geophys. Res.,112,
B07407, doi:10.1029/2006JB004763.
Johanson, I., and R. Burgmann (2005), Creep and quakes on the northern
transition zone of the San Andreas fault from GPS and InSAR data,
Geophys. Res. Lett.,32, L14306, doi:10.1029/2005GL023150.
Jolivet, R., R. Cattin, N. Chamot-Rooke, C. Lasserre, and G. Peltzer (2008),
Thin-plate modelling of interseismic deformation and asymmetry across
the Altyn Tagh fault zone, Geophys. Res. Lett.,35, L02309, doi:10.1029/
2007GL031511.
Jolivet, R., R. Bürgmann, and N. Houlié (2009), Geodetic exploration of the
elastic properties across and within the northern San Andreas fault zone,
Earth Planet. Sci. Lett.,288(1-2), 126–131, doi:10.1016/j.epsl.2009.09.014.
Jolivet, R., R. Grandin, C. Lasserre, M.-P. Doin, and G. Peltzer (2011), Sys-
tematic InSAR tropospheric phase delay corrections from globa meteoro-
logical reanalysis data, Geophys. Res. Lett.,38, L17311, doi:10.1029/
2011GL048757.
Jònsson, S., H. A. Zebker, P. Segall, and F. Amelung (2002), Fault slip
distribution of the 1999 M
w
7.1 Hector Mine, California, earthquake,
estimated from satellite radar and GPS measurements, Bull. Seismol.
Soc. Am.,92(4), 1377–1389, doi:10.1785/0120000922.
King, G., and J. Nabelek (1985), Role of fault bends in the initiation and
termination of earthquake rupture, Science,228(4702), 984–987,
doi:10.1126/science.228.4702.984.
Kostoglodov, V., S. Singh, J. Santiago, S. Franco, K. Larson, A. Lowry,
and R. Bilham (2003), A large silent earthquake in the Guerrero seismic
gap, Mexico, Geophys. Res. Lett.,30(15), 1807, doi:10.1029/
2003GL017219.
Lapusta, N., and Y. Liu (2009), Three-dimensional boundary integral
modeling of spontaneous earthquake sequences and aseismic slip,
J. Geophys. Res.,114, B09303, doi:10.1029/2008JB005934.
Lasserre, C. (2000), Fonctionnement sismique, cinématique et histoire
géologique de la faille de haiyuan, PhD thesis, Univ. Paris 7, Paris.
Lasserre, C. P., et al. (1999), Postglacial left slip rate and past occurrence of
M 8 earthquakes on the western Haiyuan fault, Gansu, China, J. Geophys.
Res.,104(B8), 17,633–17,651, doi:10.1029/1998JB900082.
Lasserre, C., B. Bukchin, P. Bernard, P. Tapponnier, Y. Gaudemer,
A. Mostinsky, and R. Dailu (2001), Source parameters and tectonic origin
of the 1996 June 1 Tianzhu (M-w = 5.2) and 1995 July 21 Yongden
(M-w = 5.6) earthquakes near the Haiyuan fault (Gansu, China), Geophys.
J. Int.,144(1), 206–220, doi:10.1046/j.1365-246x.2001.00313.x.
Lasserre, C., Y. Gaudemer, P. Tapponnier, A. S. Mériaux, J. Van der
Woerd, Y. Daoyang, F. J. Ryerson, R. C. Finkel, and M. W. Caffee
(2002), Fast late pleistocene slip rate on the Leng Long Ling segment
of the Haiyuan fault, Qinghai, China, J. Geophys. Res.,107(B11),
2276, doi:10.1029/2000JB000060.
Li, C., P. Z. Zhang, J. Yin, and W. Min (2009), Late quaternary left-lateral
slip rate of the Haiyuan fault, northeastern margin of the Tibetan Plateau,
Tectonics,28, TC5010, doi:10.1029/2008TC002302.
Lienkaemper, J. J., G. Borchardt, and M. Lisowski (1991), Historic creep
rate and potential for seismic slip along the Hayward fault, California,
J. Geophys. Res.,96(B11), 18,261–18,283, doi:10.1029/91JB01589.
Liu-Zeng, J., Y. Klinger, X. Xu, C. Lasserre, G. Chen, W. Chen, P. Tapponnier,
and B. Zhang (2007), Millennial recurrence of large earthquakes on the
Haiyuan fault near Songshan, Gansu Province, China, Bull. Seismol. Soc.
Am.,97,14–34, doi:10.1785/0120050118.
Lockner, D. A., C. Morrow, D. Moore, and S. Hickman (2011), Low
strength of deep San Andreas fault gouge from SAFOD core, Nature,
472,82–85, doi:10.1038/nature09927.
Lohman, R. B., and J. J. McGuire (2007), Earthquake swarms driven by
aseismic creep in the Salton Trough, California, J. Geophys. Res.,112,
B04405, doi:10.1029/2006JB004596.
Lopez-Quiroz, P., M.-P. Doin, F. Tupin, P. Briole, and J.-M. Nicolas
(2009), Time series analysis of Mexico City subsidence constrained by
radar interferometry, J. Appl. Geophys.,69,1–15, doi:10.1016/j.jappgeo.
2009.02.006.
Loveless, J., and B. Meade (2011), Partitioning of localized and diffuse
deformation in the Tibetan Plateau from joint inversions of geologic
and geodetic observations, Earth Planet. Sci. Lett.,303(1-2), 11–24,
doi:10.1016/j.epsl.2010.12.014.
Lundgren, P., E. A. Hetland, Z. Liu, and E. J. Fielding (2009), Southern San
Andreas-San Jacinto fault system slip rates estimated from earthquake
cycle models constrained by GPS and interferometric synthetic aperture
radar observations, J. Geophys. Res.,114, B02403, doi:10.1029/
2008JB005996.
Lyons, S., and D. Sandwell (2003), Fault creep along the southern San
Andreas from interferometric synthetic aperture radar, permanent scat-
terers, and stacking, J. Geophys. Res.,108(B1), 2047, doi:10.1029/
2002JB001831.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
17 of 18

Mazzotti, S., X. Le Pichon, P. Henry, and S. Miyazaki (2000), Full inter-
seismic locking of the Nankai and Japan-west Kurile subduction zones:
An analysis of uniform elastic strain accumulation in Japan constrained
by permanent GPS, J. Geophys. Res.,105(B6), 13,159–13,177,
doi:10.1029/2000JB900060.
Mériaux, A. S., F. J. Ryerson, P. Tapponnier, J. Van der Woerd, R. C. Finkel,
X. Xu, Z. Xu, and M. W. Caffe (2004), Rapid slip along the central Altyn
Tagh Fault: Morphochronologic evidence from Cherchen He and Sulamu
Tagh, J. Geophys. Res.,109, B06401, doi:10.1029/2003JB002558.
Meyer, B., P. Tapponnier, L. Bourjot, F. Metivier, Y. Gaudemer, G. Peltzer,
G. Shunmin, and C. Zhitai (1998), Crustal thickening in Gansu-Qinghai,
lithospheric mantle subduction, and oblique, strike-slip controlled growth
of the Tibet Plateau, Geophys. J. Int.,135(1), 1–47.
Moore, D. E., and M. J. Rymer (2007), Talc-bearing serpentinite and the
creeping section of the San Andreas fault, Nature,448, 795–797,
doi:10.1038/nature06064.
Morrow, C. A., D. E. Moore, and D. A. Lockner (2000), The effect of min-
eral bond strength and adsorbed water on fault gouge frictional strength,
Geophys. Res. Lett.,27, 815–818, doi:10.1029/1999GL008401.
Nitti, D. O., R. F. Hanssen, A. Refice, F. Bovenga, and R. Nutricato (2011),
Impact of DEM-assisted coregistration on high-resolution SAR interfer-
ometry, Geosci. Remote Sens. Lett.,49(3), 1127–1143, doi:10.1109/
TGRS.2010.2074204.
Okada, Y. (1985), Surface deformation due to shear and tensile faults in a
half-space, Bull. Seismol. Soc. Am.,75(4), 1135–1154.
Ozawa, S., M. Murakami, M. Kaidzu, T. Tada, T. Sagiya, Y. Hatanaka,
H. Yarai, and T. Nishimura (2002), Detection and monitoring of ongoing
aseismic slip in the Tokai region, central Japan, Science,298(5595),
1009–1012, doi:10.1126/science.1076780.
Peltzer, G., F. Crampe, S. Hensley, and P. Rosen (2001), Transient strain
accumulation and fault interaction in the eastern California shear zone,
Geology,29(11), 975–978, doi:10.1130/0091-7613(2001)029.
Puysségur, B., R. Michel, and J.-P. Avouac (2007), Tropospheric phase
delay in interferometric synthetic aperture radar estimated from meteoro-
logical model and multispectral imagery, J. Geophys. Res.,112, B05419,
doi:10.1029/2006JB004352.
Radiguet, M., F. Cotton, M. Vergnolle, M. Campillo, B. Valette, V. Kostoglodov,
and N. Cotte (2011), Spatial and temporal evolution of a long term slow
slip event: The 2006 Guerrero slow slip event, Geophys. J. Int.,184(2),
816–828, doi:10.1111/j.1365-246X.2010.04866.x.
Rosen, P., S. Hensley, G. Peltzer, and M. Simons (2004), Updated repeat
orbit interferometry package released, Eos Trans. AGU,85(5), 47.
Rubin, A., D. Gillard, and J. Got (1999), Streaks of microearthquakes along
creeping faults, Nature,400(6745), 635–641, doi:10.1038/23196.
Ryder, I., and R. Bürgmann (2008), Spatial variations in slip deficit on the
central San Andreas fault from InSAR, Geophys. J. Int.,175(3), 837–852,
doi:10.1111/j.1365-246X.2008.03938.x.
Savage, J. C., and R. O. Burford (1973), Geodetic determination of relative
plate motion in central California, J. Geophys. Res.,78, 832–845,
doi:10.1029/JB078i005p00832.
Schmidt, D. A., and R. Bürgmann (2003), Time-dependent land uplift and
subsidence in the Santa Clara valley, California, from a large interfero-
metric synthetic aperture radar data set, J. Geophys. Res.,108(B9),
2416, doi:10.1029/2002JB002267.
Schmidt, D. A., R. Burgmann, R. M. Nadeau, and M. d’Alessio (2005),
Distribution of aseismic slip rate on the Hayward fault inferred from seis-
mic and geodetic data, J. Geophys. Res.,110, B08406, doi:10.1029/
2004JB003397.
Sudhaus, H., and S. Jònsson (2009), Improved source modelling through
combined use of InSAR and GPS under consideration of correlated data
errors: Application to the June 2000 Kleifarvatn earthquake, Iceland,
Geophys. J. Int.,176(2), 389–404, doi:10.1111/j.1365-246X.2008.03989.x.
Tarantola, A. (2005), Inverse Problem Theory and Methods for Model
Parameter Estimation, Soc. for Indust. and Appl. Math., Philadelphia,
Penn.
Taylor, M., and G. Peltzer (2006), Current slip rates on conjugate strike-
slip faults in central Tibet using synthetic aperture radar interferometry,
J. Geophys. Res.,111, B12402, doi:10.1029/2005JB004014.
Uppala, S. M., et al. (2005), The ERA-40 re-analysis, Q. J. R. Meteorol.
Soc.,131, 2961–3012, doi:10.1256/qj.04.176.
Usai, S. (1999), SAR interferometry on a very long time scale: A study of
the interferometric characteristics of man-made features, IEEE Trans.
Geosci. Remote Sens.,37(4), 2118–2123.
Waldhauser, F., W. L. Ellsworth, D. P. Schaff, and A. Cole (2004), Streaks,
multiplets, and holes: High-resolution spatio-temporal behavior of Parkfield
seismicity, Geophys. Res. Lett.,31, L18608, doi:10.1029/2004GL020649.
Wang, H., T. J. Wright, and J. Biggs (2009), Interseismic slip rate of the
northwestern Xianshuihe fault from InSAR data, Geophys. Res. Lett.,
36, L03302, doi:10.1029/2008GL036560.
Welstead, S. T. (1999), Fractal and Wavelet Image Compression Technique,
Int. Soc. for Opt. and Photon. Opt. Eng. Press, Bellingham, Wash.
Wesnousky, S. G. (2006), Predicting the endpoints of earthquake ruptures,
Nature,444(7117), 358–360, doi:10.1038/nature05275.
Wessel, P., and W. Smith (1995), New version of the Generic Mapping
Tools released, Eos Trans. AGU,76(33), 329.
Wright, T., B. Parsons, and E. Fielding (2001), Measurement of interseis-
mic strain accumulation across the North Anatolian Fault by satellite
radar interferometry, Geophys. Res. Lett.,28(10), 2117–2120,
doi:10.1029/2000GL012850.
Wright, T. J., B. Parsons, P. C. England, and E. Fielding (2004), InSAR
observations of low slip rates on the major faults of western Tibet, Science,
305, 236–239, doi:10.1126/science.1096388.
Xu, X., R. S. Yeats, and G. Yu (2010), Five short historical earthquake sur-
face ruptures near the Silk Road, Gansu Province, China, Bull. Seismol.
Soc. Am.,100(2), 541–561, doi:10.1785/0120080282.
Zebker, H. A., and J. Villasenor (1992), Decorrelation in interferometric
radar echoes, IEEE Trans. Geosci. Remote Sens.,30(5), 950–959.
Zebker, H. A., P. A. Rosen, and S. Hensley (1997), Atmospheric effects in
interferometric synthetic aperture radar surface deformation and topo-
graphic maps, J. Geophys. Res.,102(B4), 7547–7563, doi:10.1029/
96JB03804.
Zhang, W. Q., D. C. Jiao, P. Z. Zhang, P. Molnar, B. C. Burchfield, Q. D.
Deng, Y. P. Wang, and F. M. Song (1987), Displacement along the
Haiyuan fault associated with the great 1920 Haiyuan, China, earthquake,
Bull. Seismol. Soc. Am.,77(1), 117–131.
Zhang, P. Z., P. Molnar, B. C. Burchfield, L. Royden, Y. P. Wang, Q. D.
Deng, F. M. Song, W. Q. Zhang, and D. C. Jiao (1988), Bounds on the
Holocene slip rate of the Haiyuan Fault, north-central China, Quat.
Res.,30(2), 151–164.
JOLIVET ET AL.: CREEP ON THE HAIYUAN FAULT B06401B06401
18 of 18