Rupture processes of the 2012 September 5 Mw 7.6 Nicoya CONSTRAINED

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Rupture processes of the 2012 September 5 M w
7.6 Nicoya, Costa Rica earthquake constrained
by improved geodetic and...
Article in Geophysical Journal International · July 2015
DOI: 10.1093/gji/ggv295
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Geophysical Journal International
Geophys. J. Int. (2015) 203, 175–183 doi: 10.1093/gji/ggv295
GJI Seismology
Rupture processes of the 2012 September 5 Mw 7.6 Nicoya,
Costa Rica earthquake constrained by improved geodetic
and seismological observations
Chengli Liu,1
Yong Zheng,1
Xiong Xiong,1
Rongjiang Wang,2
Allan López3
and Jun Li1
1State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences,
Wuhan 430077, China. E-mail: zhengyong@whigg.ac.cn
2GFZ German Research Centre for Geosciences, D-14473 Potsdam, Germany
3Centro de Investigaciones en Ciencias Geológicas, Universidad de Costa Rica; Universidad Latina de Costa Rica, Costa Rica
Accepted 2015 July 13. Received 2015 June 19; in original form 2015 February 4
S U M M A R Y
On 2012 September 5, the anticipated interplate thrust earthquake ruptured beneath the Nicoya
peninsula in northwestern Costa Rica close to the Middle America trench, with a magnitude
Mw 7.6. Extensive co-seismic observations were provided by dense near-field strong ground
motion, Global Positioning Systems (GPS) networks and teleseismic recordings from global
seismic networks. The wealthy data sets available for the 2012 Mw 7.6 Nicoya earthquake
provide a unique opportunity to investigate the details of the rupture process of this earthquake.
By implementing a non-linear joint inversion with high-rate GPS waveform, more static GPS
offsets, strong-motion data and teleseismic body waveform, we obtained a robust and accurate
rupture model of the 2012 Mw 7.6 Nicoya earthquake. The earthquake is dominantly a pure
thrust component with a maximum slip of 3.5 m, and the main large slip patch is located below
the hypocentre, spanning ∼50 km along dip and ∼110 km along strike. The static stress drop
is about 3.4 MPa. The total seismic moment of our preferred model is 3.46 × 1020
N m, which
gives Mw = 7.6. Due to the fast rupture velocity, most of the seismic moment was released
within 70 s. The largest slip patch directly overlaps the interseismic locked region identified
by geodetic observations and extends downdip to the intersection with the upper plate Moho.
We also find that there is a complementary pattern between the distribution of aftershocks and
the co-seismic rupture; most aftershocks locate in the crust of the upper plate and are possibly
induced by the stress change caused by the large slip patch.
Key words: Seismic cycle; Earthquake ground motions; Earthquake source observations;
Seismicity and tectonics; Body waves; Subduction zone processes.
1 I N T RO D U C T I O N
The Cocos Plate subducts northeastwards beneath the Caribbean
Plate (along the Middle America Trench) at a rate of ∼8.5 cm yr−1
(DeMets et al. 2010). Due to this rapid convergence rate, the
Nicoya region of northwestern Costa Rica is frequently subjected
to strong earthquakes. Since 1853, three earthquakes of M > 7
occurred in this region: 1853 (M ≥ 7.5), 1900 (M ≥ 7.5), and
1950 (M = 7.7) (Protti et al. 2014). Furthermore, geodetic stud-
ies have suggested that the Nicoya Peninsula is a locked zone in
which significant seismic strain was accumulated over time (Nor-
abuena et al. 2004; Outerbridge et al. 2010; Walter et al. 2011;
Feng et al. 2012; Jiang et al. 2012), as a consequence, a strong
earthquake was anticipated in this region for many years (Nishenko
1991; Protti et al. 2001). On 2012 September 5, an interplate
thrust earthquake occurred beneath the Nicoya Peninsula with a
moment magnitude (Mw) of 7.6. This event was recorded by a
dense network of strong-motion seismometers in the near-field,
teleseismic recordings from global seismic networks and Global
Positioning System (GPS) networks, including static GPS offsets
and high-rate GPS (Dixon et al. 2013; Protti et al. 2014). These
wealthy data sets (Fig. 1) provide a unique opportunity to investi-
gate the details of the rupture processes of the 2012 Mw 7.6 Nicoya
earthquake.
Accurate slip models are critically important for estimating
Coulomb stress changes and, consequently, for assessing seismic
hazard and possible post-seismic mechanisms. A number of rup-
ture models have been derived for this event by using teleseismic
data (Ye et al. 2013), geodetic data (Protti et al. 2014) or joint in-
version of multiple data sets (Yue et al. 2013). In detail, Ye et al.
(2013) used teleseismic body waves to resolve the rupture model.
However, due to the well-known propagation effects, teleseismic
data can only constrain the ruptured area relative to the hypocen-
tre (rupture initiation point) and are insufficient for resolving rup-
ture processes in detail (Delouis et al. 2010; Zhang et al. 2014).
Protti et al. (2014) built a static rupture model for this earthquake
C The Authors 2015. Published by Oxford University Press on behalf of The Royal Astronomical Society. 175
byguestonAugust18,2015http://gji.oxfordjournals.org/Downloadedfrom

176 C. Liu et al.
Figure 1. Map view of the Mw 7.6 Nicoya earthquake and data sets used in this study. Red star and black beachball denote the epicentre and USGS Centroid
Moment Solution of the main shock. The blue triangles and red triangles represent the high-rate GPS stations and strong-motion stations, respectively. The
yellow dots indicate all the remaining GPS stations, including 18 continuous and 21 campaign GPS sites. The upper inset shows the location of the teleseismic
stations used in this study.
by using 18 continuous and 21 campaign GPS records. However,
serious spatial uncertainty can be observed in their checkerboard
tests, especially for the slip along the trench. Moreover, since all
of the inland GPS stations were concentrated at one side of the
epicentre, the resolution of these data decreases rapidly away from
the coast (Yokota et al. 2011; Wei et al. 2012). Yue et al. (2013)
used 11 GPS observations (including nine high-rate stations), near-
field strong-motion data, and teleseismic waveforms to obtain a
co-seismic slip distribution model by applying a joint inversion
method. Although these data sets can constrain the rupture model
much better than those determined by unique data set, the reso-
lution and reliability of the model are inhibited due to a number
of critical considerations: (1) the rupture speed was kept constant,
which is relatively far away from reality; (2) only 11 static GPS
sites were used. Since near-field observations, especially near-field
static displacements, are particularly useful for constraining the slip
distribution of large complex ruptures, as shown for the Mw 7.2 El
Mayor-Cucapah event (Wei et al. 2011), therefore, the introduction
of additional near-field geodetic data with better azimuthal cover-
age was needed to significantly improve the resolution of the slip
model.
In this study, we used more near-field GPS data compiled by
Protti et al. (2014) to build a new rupture model of the Nicoya
earthquake with higher resolution. Following the approach of Ji
et al. (2002), we performed a non-linear joint inversion using
new static GPS data (Protti et al. 2014); near-field, strong-motion
and high-rate GPS measurements; and teleseismic body waves.
The advantages of this approach is a reduction in the number
of free parameters and the ability to solve for the slip ampli-
tude, rise time, rake angle and rupture velocity for each subfault
simultaneously.
2 DATA A N D M E T H O D O L O G Y
2.1 Data sources
The data sets used in this study contain both improved geodetic
and seismological observations. Static displacement measurements
at 39 GPS stations (18 continuous and 21 campaign GPS sites;
Fig. 1) were taken from Protti et al. (2014). The Laboratorio de
Ingenieria S´ısmica at the Universidad de Costa Rica (LIS-UCR)
provided strong-motion data from six accelerometer stations. Three-
component ground motion solutions for eight high-rate GPS stations
were taken from Yin & Wdowinski (2014). Teleseismic data, includ-
ing 33 P and 17 SH waveforms, were obtained from the Incorporated
Research Institutions for Seismology (IRIS).
2.2 Joint inversion methodology
To constrain the kinematic slip properties of the earthquake, we
collected seismological and geodetic data to conduct a joint inver-
sion. For the near-field observations, we collected the data from
six strong-motion stations. The baseline shifts of the accelerograms
were corrected following the method of Wang et al. (2011). The
corrected acceleration records were then converted into displace-
ments (the comparison between raw data and corrected data are
shown in Fig. S1). In order to remove high-frequency noise and
the disturbances caused by small-scale structures, a low-pass filter
with a corner frequency of 1 Hz was applied to the displacement
records. Comparing with strong-motion data, high-rate GPS data
are free from baseline shifts and thus can provide reliable near-field
records. For this reason, three-component displacement records for
eight high-rate GPS stations (Yin & Wdowinski 2014) were also

Rupture process of the 2012 Mw 7.6 Nicoya earthquake 177
used in the inversion, and the GPS waveforms are also filtered with
a low-pass filter with a corner frequency of 1 Hz.
However, almost all of the near-field data sets are located to the
east side of the source region, this kind of unevenly azimuthal dis-
tribution may decrease the resolution in distal offshore areas (Diao
et al. 2011, 2012; Loveless & Meade 2011). This problem can be
partially compensated by incorporating teleseismic data set (e.g.
Ammon et al. 2011; Koketsu et al. 2011; Wei et al. 2012; Yue &
Lay 2013). So we chose high-quality teleseismic data with good
azimuthal coverage to improve the azimuthal data coverage. The
data selected, 33 P and 17 SH waveforms, have high signal-to-noise
ratio and good azimuthal distribution during the joint inversion
(Fig. 1). The instrument responses were deconvolved from the orig-
inal recordings to obtain ground displacements, and the broad-band
seismograms were bandpass filtered between 0.003 and 1 Hz.
Based on the United States Geological Survey (USGS) Centroid
Moment Solution, and the geological environment in the source
region, we chose a slip model that consists of a single rupture plane
with a length of 168 km along the strike and 112 km down dip,
and with strike and dip angles of 307◦
and 21◦
, respectively. We
discretized the fault plane into a total of 336 subfaults, each with
a dimension of 8 × 7 km along strike and dip, respectively. The
rupture was assumed to initiate at the epicentral location (85.56◦
W,
9.76◦
N, 13.1 km) as determined by Yue et al. (2013).
To obtain a kinematic slip model, we applied a finite fault inver-
sion method, which performs the waveform inversion in the wavelet
domain, along with a simulated annealing method to simultaneously
invert slip amplitude, rake angle, rise time and average rupture ve-
locity (Ji et al. 2002, 2003). An advantage of this method is its
ability to combine multiple data sets, including geodetic and seis-
mic data, to retrieve the rupture evolution on the fault. In order
to eliminate singular value between neighbouring subfaults effec-
tively and create the visual effect of more smoothing, we imposed
additional constraints by minimizing the slip differences between
neighbouring subfaults, and by assuming the seismic moment in
the Global Centroid Moment Tensor (GCMT) catalogue (3.42 ×
1020
N m) as the reference moment. The Green’s functions used
to invert the geodetic and seismic data were calculated using a
1-D layered crustal velocity model, modified from Crust2.0 (Bassin
et al. 2000), and the local seismic velocity model (Table S1) as
proposed by DeShon & Schwartz (2004), respectively. During the
inversion process, the slip value for each subfault was allowed to
vary from 0 to 6 m, and the search for the rake angle was performed
in the range from 48◦
to 138◦
, with a search step of 3◦
. The average
rupture velocity was allowed to vary from 1.5 to 3.5 km s−1
, with a
step of 0.1 km s−1
. Finally, the rise time in the inversion model was
allowed to change from 2.0 to 24.0 s, with a time step of 2.0 s.
3 C H E C K E R B OA R D T E S T S
Checkerboard tests are used to provide a direct visualization of the
relative resolutions of different data sets. Thus, before conducting
the inversions of the real data sets, we performed checkerboard
tests to investigate the resolutions of the unique data set and joint
inversions for the rupture model (Fig. 2a). Similar to the inversions
with the real data, we built a test model in which the fault plane was
striking 307◦
and dipping 21◦
. The synthetic data were generated for
the same stations that provided the real data (Fig. 1) and the same
Green’s functions were used in both the checkerboard tests and the
real data inversions. The slip patches contained 4 × 4 (32 × 28 km)
subfaults, with each subfault having a slip of 3 m, a rake angle of
90◦
, an average rupture velocity of 2.5 km s−1
and a rise time of
12 s. The theoretical displacement and velocity waveforms were
computed for each geodetic and seismic station. We then inverted
the synthetic data without adding noise both separately and jointly.
In the inversion, the slip amplitude was allowed to vary from 0
to 6 m, the rake angle ranged from 48◦
to 138◦
, the rise time was
allowed to vary from 2.0 to 24.0 s, with a time step of 2.0 s and the
rupture velocity was allowed to vary from 1.5 to 3.5 km s−1
.
The results of the checkerboard tests show that the joint inver-
sion provides higher resolution than any other models determined
by unique data set (Fig. 2). The teleseismic body wave data set
model shows a poor resolution (Fig. 2b), which is not sufficient
to resolve the rupture process in detail. Comparing with previous
data sets, the resolution of the high-rate GPS data is improved dra-
matically (Fig. 2c), especially to the northwest of the epicentre.
However, slip resolution in the southeastern part of the fault plane
remains relatively low due to the absence of stations to the south
and southeast of the hypocentre. For the strong-motion data, the
updip slip pattern is smeared along the strike direction. Similar to
the high-rate GPS data, resolution in the southeastern part is still
needed improvement (Fig. 2d). The GPS static offsets generally
have good resolution for downdip slip, but are poorly resolved for
the near-trench slip, and especially poorly resolved for the slip to the
southeast of the hypocentre (Fig. 2e), which is in good agreement
with the model of Protti et al. (2014). Comparing with all the unique
data set models, the resolution of the joint inversion model is much
better, especially for the slip near the trench, where both the slip
pattern and the slip amplitude are significantly improved (Fig. 2f).
Moreover, other parameters, including rupture velocity, rise time
and rake angle, are all almost perfectly resolved (Figs S2 and S3b),
even when a 5 per cent or 10 per cent level of Gaussian white noise
is added to all synthetics, the input model is still recovered very well
(Fig. S3).
In addition, we also conducted two tests with different grid spac-
ing to explore the model resolution. In one checkerboard test, the
grid size is 56 × 42 km and the other one is similar to the real
slip pattern. The test results show that the inverted rupture mod-
els also provide a good resolution (Fig. S4). From these resolution
tests, we proposed that the joint inversion is capable of providing
high-resolution and reliable rupture model for the Mw 7.6 Nicoya
earthquake.
4 R E S U LT S A N D D I S C U S S I O N
In this study, we used a joint inversion to define a finite fault model of
the Nicoya earthquake (Fig. 3). Our optimized model shows that the
total seismic moment was 3.46 × 1020
N m, equals to a magnitude
of Mw 7.6, and that the dominant mechanism was pure thrust with a
maximum slip of 3.5 m, which occurred at a depth of 25 km below
the epicentre (Fig. 3a). The largest slip patch was located below
the hypocentre, extending ∼50 km along dip and ∼110 km along
strike. This area directly overlaps a locked onshore interseismic
region, as indicated by geodetic observations (Feng et al. 2012),
and extends downdip to the intersection between the subducted slab
and the Moho (Fig. 3b). Taken as a circular fault, the large slip
patch has a radius (R) of 30 km and an average slip ( D) of 2.5 m.
With a rigidity of 3.0 × 104
MPa, the estimated static stress drop,
using the expression σ = 7π
16
μ
¯D
R
(Kanamori & Anderson 1975),
was 3.4 MPa. This is consistent with a median stress drop (around
3.3 MPa) of global interpolate earthquakes (Kanamori & Anderson
1975; Allmann & Shearer 2009). Secondary oblique strike slip

178 C. Liu et al.
Figure 2. Checkerboard tests for the model resolution. (a) The input slip model used to generate synthetic data. Both seismic waveforms and static data are
generated at stations recording the main event and used in the inversion. The inversion results using only the teleseismic data (b), high-rate GPS waveform data
(c), strong-motion data (d), static GPS offsets (e) and combined all above data sets (f). Green star indicates the epicentre of the main shock.
asperities were also found along both sides of the major slip asperity,
located above the hypocentre. The dominant slip direction of the left
asperity is 135◦
along the rake direction and the rake angle of the
right asperity is 55◦
, which are consistent with the direction of the
co-seismic displacements observed by GPS stations near the coast
(Fig. 4). However, their magnitudes are much smaller than that of
the major slip patch.
Our preferred slip model shows that the rupture initiated with
small amplitude near the hypocentre, and then it propagated mainly
along the dip direction with a relatively slow speed (1–2 km s−1
)

Figure 3. (a) Slip distribution calculated from our joint inversion. The red star indicates the hypocentre, the colour bar shows the scale of the slip amplitude,
the white arrows represent the slip directions and the contours outline the rupture propagation time in second. (b) The surface projection of the inverted slip
distribution. Grey dots indicate the aftershocks from the RSN catalogue. The black contour indicates the interseismic locked region (Feng et al. 2012). The
white dashed line shows the surface projection of Moho interface. The inset shows the moment-rate function of this earthquake.
Figure 4. Comparison between the observed horizontal (a) and vertical (b) displacements and those predicted from our preferred model. The black arrows are
the static GPS data and the red arrows are synthetics.
in the first 10 s. After the first 10 s, the rupture speed below the
hypocentre was accelerated to 3.0 km s−1
(Fig. 3a), and the moment
rate function reached a peak value at about 20 s (Fig. 3b). After
20 s, two additional significant slip patches were located on both
side of the epicentre (Fig 3a), with a magnitude of 1.5 m. In order
to make sure whether these two slip patches represent real slip or
just artificial effects of the inversion, we conducted several tests
with different conditions to verify them. Their appearance in each
of these tests showed that the two patches are stable features of our
finite rupture model instead of artefacts.
The co-seismic rupture pattern revealed in our preferred model is
consistent with the distribution of aftershocks reported by the Red
Sismológica Nacional (RSN). Most aftershocks were located in the
crust of the upper plate and were likely induced by the Coulomb
stress change caused by the large slip patch. Another notable phe-
nomenon is that a cluster of aftershocks was located in the weak rup-
ture zone between the epicentre and the largest slip patch (Fig. 3b).
This cluster might be caused by the unreleased strain during the
process of the co-seismic rupture of the main shock.
Besides the consistency between the slip pattern and the after-
shock distribution, the synthetic waveforms of our preferred model
also fit with other observations well. Our preferred model can well
explain the static displacements (Fig. 4), high-rate GPS waveforms
(Fig. 5), most strong-motion observations (Fig. 6) and teleseismic
observations (Fig. 7). Relatively larger mismatches can only be
found in the strong-motion data. For example, the waveforms in
the E–W component of the GNSR and GLIB stations show particu-
larly large misfits in the later portions of the records (Fig. 6). These
misfits are presumably due to path effects caused by complicated
structures, especially within the sediments. In addition, near-field
horizontal strong-motion records are usually contaminated by both
co-seismic ground tilts and analogue-to-digital conversion (Boore
2003), which can cause baseline shift during the integration pro-
cess from accelerogram to displacement. Although we corrected
the strong-motion records, using the method of Wang et al. (2011),
it remains difficult to retrieve ground displacements accurately, es-
pecially for the post-event static displacement seismograms (Wang
et al. 2011).

180 C. Liu et al.
Figure 5. Comparison of the high-rate GPS records (black line) and synthetic seismograms (red line) derived from our model. Both data and synthetics are
aligned by the first P arrivals. The number at the first of each seismogram indicates the station name; the number at the right top is the maximum displacement
of the records in cm.
Figure 6. Comparison of the strong-motion records (black line) and synthetic seismograms (red line) derived from our model. Both data and synthetics are
aligned by the first P arrivals. The number at the first of each seismogram indicates the station name; the number at the right top is the maximum displacement
of the records in cm.
To further verify the reliability of our rupture model, we com-
pared our results with the results of other studies. In the inversion of
Protti et al. (2014), a co-seismic large slip patch was located directly
beneath the Nicoya Peninsula, consistent with our preferred model.
However, in contrast to our model, significant continuous slip can be
observed near the trench. However, their checkerboard test demon-
strates that the resolution in their model boundaries is quite low,
especially for boundary near the trench. Furthermore, in our model,
although there is also an obvious slip updip of the hypocentre, the
rupture area is smaller. Yue et al. (2013) also showed a relatively

Figure 7. Comparison of teleseismic displacement records in black and synthetic seismograms in red predicted by the slip model; the seismograms are bandpass
ﬁltered with a frequency band of 0.003–1 Hz. Both data and synthetic seismograms are aligned to the P and SH arrivals. The number at the end of each trace
is the peak displacement of the data in micrometres. The azimuth and distance in degrees are shown at the beginning of each record with the azimuth on top.

182 C. Liu et al.
small slip area with no significant slip near the trench. This suggests
that the strong rupture trench-proximal slip patch in the model of
Protti et al. (2014) may be an artificial effect caused by the low
resolution of their data set.
Overall, the rupture model of Yue et al. (2013) is most consis-
tent with our model. However, due to the different data sets utilized
by the two models there are still some differences between them.
For example, Yue et al. (2013) used only 11 GPS static offsets to
constrain the slip model, while we used 39 GPS data sites, thus our
GPS data have much better azimuthal coverage. To investigate the
slip discrepancies between the models, we conducted two additional
checkerboard tests to explore the relative resolution powers of our
joint work and the inversion of Yue et al. (2013). First, we tested
the co-seismic displacement resolution. As we used more static data
than Yue et al. (2013), the resolution of our static model is much
better, especially for the slip patches at greater depths and near the
model boundaries (Fig. S1). Second, we compared the resolutions
between the data sets of Yue et al. (2013) and those of our preferred
model (Fig. S2). As expected, the spatial resolution of our joint
inversion is much better than that of Yue et al. (2013). These results
suggest that the slip discrepancies and the differences in co-seismic
displacement between our joint inversion and the inversion of Yue
et al. (2013) mainly reflect data set selection. The results also show
that our preferred model provides a higher resolution and more re-
liable rupture pattern for the Nicoya earthquake. Moreover, in order
to test the influence of the rupture velocity, we tested many inver-
sions by assuming different constant rupture velocities (the rupture
velocities were chosen between 1.5 and 3.5 km s−1
with an interval
of 0.5 km s−1
), and also assuming a variable rupture velocity, which
was allowed to vary in the range from 1.5 to 3.5 km s−1
. By com-
parison, the model derived from the variable rupture velocity yields
a better fit to all data sets (Table S2) than the other models.
5 C O N C LU S I O N S
By implementing a joint inversion of high-rate GPS waveforms,
more static GPS offsets than in previous studies, strong-motion
data and teleseismic body waveforms, we obtained a robust and
accurate rupture model of the 2012 Mw 7.6 subduction zone thrust
earthquake of the Nicoya Peninsula, northwestern Costa Rica. Our
model shows that the earthquake was dominated by pure thrust slip
with maximum amplitude of 3.5 m. The largest area of slip was
located below the hypocentre, extending ∼50 km along the dip and
∼110 km along the strike. The static stress drop was about 3.4 MPa
and the total seismic moment was 3.46 × 1020
N m, equivalent to
Mw 7.6. Due to the fast rupture velocity, most of the seismic moment
was released within 70 s.
The largest slip patch directly overlaps the interseismic locked
zone, as indicated by geodetic observations, and extends downdip to
the intersection with the Moho. We also found that the distribution
of aftershocks was complementary to the co-seismic rupture pattern.
Most aftershocks were located in the crust of the upper plate and
were likely induced by the Coulomb stress change caused by the
large slip patch identified in this study.
AC K N OW L E D G E M E N T S
This work was supported by the National Natural Science Foun-
dation of China (Grant Numbers 41422401 and 41321063), the
Chinese Earthquake Administration (Grant Number 201308013)
and by an Excellent Young Scientist grant from Hubei Province
(Grant Number 2012FFA026). We would like to thank Aarón Moya
(Earthquake Engineering Laboratory, UCR) for providing the
strong-motion data, Lepolt Linkimer and Wilfredo Rojas (RSN,
UCR) for preparing the aftershock catalogue, Haitao Yin (Earth-
quake Administration of Shandong Province) for the high-rate GPS
data and the IRIS data centre for providing the broad-band teleseis-
mic waveforms. All figures were generated using the open-source
Generic Mapping Tools software (Wessel & Smith 1991).
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S U P P O RT I N G I N F O R M AT I O N
Additional Supporting Information may be found in the online ver-
sion of this paper:
Table S1. 1-D velocity model used in this study.
Table S2. Misfits versus different rupture velocities.
Figure S1. Baseline correction of strong-motion records. Left:
three components of original strong-motion records at each station.
Middle: velocity seismograms integrated from the original strong-
motion records. Right: displacement seismograms integrated from
the velocity seismograms, and the red lines show the corrected
displacement waveform.
Figure S2. (a) Rise time of the input model. (b) Rise time obtained
by joint inverting the synthetic data.
Figure S3. (a) Input slip model. (b) Slip model obtained by joint in-
verting the synthetic data sets with no noise. (c) Slip model obtained
by joint inverting the synthetic data sets with 5 per cent Gaussian
white noise. (d) Same as (c), but with 10 per cent Gaussian white
noise. The black contours indicate the target and inverted rupture
initiation time. The white arrows denote the rake angles.
Figure S4. Checkerboard tests with different slip patches. Panels
(a) and (c) show the target model with different slip patches. Panels
(b) and (c) present the corresponding inverted results. The black
contours indicate the target and inverted rupture initiation time.
The white arrows denote the rake angles.
Figure S5. The checkerboard tests for static GPS data resolution.
(a) The inversion result of using our data and (b) using the data of
Yue et al. (2013). Light blue triangles indicate the static GPS station
locations.
Figure S6. Comparing the resolution of (a) using our data sets and
(b) the data sets of Yue et al. (2013) (http://gji.oxfordjournals.org/
lookup/suppl/doi:10.1093/gji/ggv295/-/DC1).
Please note: Oxford University Press is not responsible for the con-
tent or functionality of any supporting materials supplied by the
authors. Any queries (other than missing material) should be di-
rected to the corresponding author for the paper.
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