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Wanpeng Feng, Zhenhong Li, John R. Elliott, Yo Fukushima, Trevor Hoey, Andrew Singleton, Robert Cook, Zhonghuai Xu, The 2011 MW 6.8 Burma earthquake: fault constraints provided by multiple SAR techniques, Geophysical Journal International, Volume 195, Issue 1, October 2013, Pages 650–660, https://doi.org/10.1093/gji/ggt254
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Abstract
We used two tracks of ALOS PALSAR images to investigate the focal mechanism and slip distribution of the 2011 March 24, MW 6.8 Burma strike-slip earthquake. Three different SAR techniques, namely conventional interferometry, SAR pixel offsets (SPO) and multiple-aperture InSAR (MAI), were employed to obtain the coseismic surface deformation fields along the ∼30 km length of the fault rupture. Along-track measurements from SPO and MAI techniques show a high correlation, and were subsequently used to precisely determine the location and extent of the surface fault trace. The best-fitting fault model geometry derived from an iterative inversion technique suggests that the rupture occurred on a near-vertical sinistral strike-slip fault west of the Nam Ma fault with a strike of 70°. A maximum slip of 4.2 m occurs at a depth of 2.5 km, with significant slip constrained only to the upper 10 km of the crust.
1 INTRODUCTION
On 2011 March 24th (UTC Time 13:55:12), a MW 6.8 earthquake struck Shan state in Eastern Burma (Myanmar) (Trisirisatayawong et al.2011), close to the border with Thailand and Laos (Fig. 1). The earthquake caused at least 70 fatalities, hundreds of injuries and several hundred building collapses, followed by landslides and other secondary hazards (Daneill et al.2011). Moment tensor solutions from the USGS indicate a pure strike-slip rupture on a nearly vertical dipping fault, with an epicentre at (20.687°N, 99.822°E). In the past 30 yr, more than 40 M > 5 earthquakes have occurred in the vicinity of the Burma shear zone, all having similar strike-slip mechanisms (Fig. 1a). The 2011 Burma event was the largest shallow earthquake in this region for the past 50 yr. This region is affected by the north–south converging India-Asia collision and the eastward East Asia continental extrusion (Molnar & Tapponnier 1975; Yin 2000; Chung et al.2005; Taylor & Yin 2009; Styron et al.2010). The Sagaing fault is formed as a result of the oblique convergence between India and the Sunda plate, bisecting Burma from South to North, and accommodating the major dextral slip at a rate of 10–23 mm yr−1 (Maung 1987; Vigny et al.2003; Socquet et al.2006; Maurin et al.2010; Wang et al.2011a). All geological surveys, GPS observations and numerical simulations suggest that the slip rate of the Sagaing fault is about four times greater than that of the right-lateral Red River Fault (RRF) in Yunnan, China (the latter being approximately 2.5–5 mm yr−1) (Scharer et al.1990; Replumaz et al.2001; Shen et al.2005; Wang et al.2011a). These two major strike-slip faults control the internal deformation within the Burma region. A set of nearly parallel ENE left-lateral faults (red lines in Fig. 1a) distributed in this area appear to be related to the clockwise rotation due to the dextral movements on the boundary (Fig. 1a) (Tapponnier & Molnar 1976; Taylor & Yin 2009). Among these strike-slip faults, the active Nam Ma fault (NMF) (which is probably a eastward extension of the fault associated with the 2011 Burma earthquake), extends in a NNE direction from the City of Mong Hpayak.
To improve our understanding of the nature of faulting in this region, we used a variety of SAR techniques, namely conventional differential interferometry, SAR pixel offsets (SPO), and multiple-aperture interferometry (MAI) to process two tracks of ALOS PALSAR images. From these we determined the fault trace for the Burma earthquake, and modelled its slip distribution using interferometric radar measurements of surface displacements.
2 COSEISMIC OBSERVATIONS FROM SAR DATA
To obtain precise coseismic surface displacements of the 2011 Burma earthquake, both ascending and descending tracks of L-band ALOS PALSAR images (Table 1) were analysed using three different techniques: conventional differential interferometry, MAI and SPO.
Mode . | Track . | Master . | Slave . | Baselinea (m) . | LOS Vectorb . | Days between the earthquake . | stddevc . | Stddevd . |
---|---|---|---|---|---|---|---|---|
. | . | (YYYY-MM-DD) . | (YYYY-MM-DD) . | . | East, North, Up . | and postseismic image . | (mm) . | (mm) . |
Descending | 126 | 2011-02-14 | 2011-04-01 | 460 | [0.6412, −0.1404,0.7543] | 8 | 6.5 | 13.1 |
Ascending | 486 | 2011-02-16 | 2011-04-03 | 30 | [−0.5866, −0.1290,0.7995] | 10 | 4.3 | 12.5 |
Mode . | Track . | Master . | Slave . | Baselinea (m) . | LOS Vectorb . | Days between the earthquake . | stddevc . | Stddevd . |
---|---|---|---|---|---|---|---|---|
. | . | (YYYY-MM-DD) . | (YYYY-MM-DD) . | . | East, North, Up . | and postseismic image . | (mm) . | (mm) . |
Descending | 126 | 2011-02-14 | 2011-04-01 | 460 | [0.6412, −0.1404,0.7543] | 8 | 6.5 | 13.1 |
Ascending | 486 | 2011-02-16 | 2011-04-03 | 30 | [−0.5866, −0.1290,0.7995] | 10 | 4.3 | 12.5 |
aPerpendicular baseline in the centre of the image.
bThe unit vector [de, dn, du] of the radar line of sight.
cStandard deviations calculated using all valid points in the non-deforming area (Figs S4b and e).
dStandard deviations calculated using all the valid points in residual interferograms (Figs 6c and f).
Mode . | Track . | Master . | Slave . | Baselinea (m) . | LOS Vectorb . | Days between the earthquake . | stddevc . | Stddevd . |
---|---|---|---|---|---|---|---|---|
. | . | (YYYY-MM-DD) . | (YYYY-MM-DD) . | . | East, North, Up . | and postseismic image . | (mm) . | (mm) . |
Descending | 126 | 2011-02-14 | 2011-04-01 | 460 | [0.6412, −0.1404,0.7543] | 8 | 6.5 | 13.1 |
Ascending | 486 | 2011-02-16 | 2011-04-03 | 30 | [−0.5866, −0.1290,0.7995] | 10 | 4.3 | 12.5 |
Mode . | Track . | Master . | Slave . | Baselinea (m) . | LOS Vectorb . | Days between the earthquake . | stddevc . | Stddevd . |
---|---|---|---|---|---|---|---|---|
. | . | (YYYY-MM-DD) . | (YYYY-MM-DD) . | . | East, North, Up . | and postseismic image . | (mm) . | (mm) . |
Descending | 126 | 2011-02-14 | 2011-04-01 | 460 | [0.6412, −0.1404,0.7543] | 8 | 6.5 | 13.1 |
Ascending | 486 | 2011-02-16 | 2011-04-03 | 30 | [−0.5866, −0.1290,0.7995] | 10 | 4.3 | 12.5 |
aPerpendicular baseline in the centre of the image.
bThe unit vector [de, dn, du] of the radar line of sight.
cStandard deviations calculated using all valid points in the non-deforming area (Figs S4b and e).
dStandard deviations calculated using all the valid points in residual interferograms (Figs 6c and f).
2.1 Conventional InSAR
Two coseismic interferograms were formed from ALOS PALSAR images using the JPL/Caltech ROI_PAC software (version 3.1 beta) (Rosen et al.2004). The topographic phase contribution was removed using version 4.1 of the Shuttle Radar Topography Mission (SRTM) 3-arcsecond (∼90 m) spacing digital elevation model (DEM) that has the voids filled from other data sources (Jarvis et al.2008). The interferograms were first multilooked using factors of 2 and 8 in the range and azimuth directions, respectively, and then filtered by a Goldstein filter (Goldstein & Werner 1998) with a fast Fourier transformation (FFT) window of 128 × 128 pixels. Finally, the interferograms were unwrapped using the branch-cut algorithm (Goldstein et al.1988) to obtain line-of-sight (LOS) displacements.
Fig. 2 shows different fringe patterns in the two ALOS interferograms because of their different radar LOS vectors (Table 1). The displacements along the range direction derived from descending track 126 (Fig. 2b) display an opposite sign compared with those from the ascending track 486 (Fig. 2f). In both ascending and descending interferograms, the number of fringes on both sides of the faults are similar (Figs 2a and e), suggesting that this event is associated with a steeply-dipping WSW-ENE strike-slipping fault with a limited amount of dip-slip displacements.
2.2 MAI
A major limitation of conventional InSAR is that it only provides one-dimensional displacements along the radar light-of-sight (LOS), which is the projection of the three components of actual surface displacements associated with geophysical events. Considering the difference between results from the backward part and forward part of signals, a second dimension (along-track) of deformation from one interferometric pair can be measured by a split-beam technique (Bechor & Zebker 2006). In this study, we employed the open-source codes developed by Barbot et al. (2008) to generate along-track interferograms. Note that the detectable along-track displacement is in the range of [ − L/4, L/4] (where L is the antenna length and about 8.9 m for ALOS) and no phase unwrapping is required (Barbot et al.2008). It is also worth pointing out that Barbot et al. (2008) applied a bandpass filter to the already focused SLC image to separate it into forward- and backward-looking scenes, reporting a 10 cm precision on their MAI results. On the contrary, the radar beam is split into two parts by restricting the azimuth resolution and the forward-/backward-looking scenes are focused in the other two studies (Bechor & Zebker 2006; Jung et al.2009; Ben-Dov & Herring 2011), though the former with a deskewed geometry and the latter with a skewed geometry. Bechor & Zebker (2006) suggested the precision of their MAI results ranged from 5 to 8.8 cm depending on coherence, while Jung et al. (2009) found precision from 10.2 to 13.1 cm on their implementation.
Because the signal-noise ratio (SNR) of MAI interferograms is usually lower than that produced by conventional InSAR (Sun et al.2008), stronger Goldstein filtering has been employed in MAI processing than that in conventional InSAR. The along-track displacements are shown in Figs 2(d) and (h), from which the rupture traces can be clearly observed. Formal errors of the MAI displacement maps were calculated using a small window of 4 × 4 pixels, and are about 0.08 m for both tracks.
2.3 SPO
To verify the reliability of along-track measurements from MAI, we also implemented SPO analysis (Michel et al.1999), which has been widely used in previous studies (Fialko et al.2001; Jónsson et al.2002; Funning et al.2005; Li et al.2011). SPO analysis uses cross-correlation techniques and its accuracy depends on the characteristics of SAR images (e.g. pixel sizes and surface properties) (Michel et al.1999). In this study, we utilized the ampcor program in the ROI_PAC package (Rosen et al.2004) using a matching window of 128 × 128 pixels with the steps of 8 pixels in range and 24 pixels in azimuth to construct two range offset and two azimuth offset maps. We first chose a threshold of 2 m to remove points with a magnitude larger than the threshold (Pathier et al.2006), and then applied a Gaussian smoothing filter, which is an iterative estimation for each pixel with a Gaussian operator of 7 × 7 pixels in the resultant offset maps.
With respect to the conventional interferograms, SPO maps include greater noise limiting their precision to 12–15 cm (Fialko et al.2001; Jónsson et al.2002), while MAI maps have smaller uncertainties in the level of 8 cm, even 2–4 cm in areas with coherence greater than 0.8 (Bechor & Zebker 2006). Figs 3(g) and (h) give the correlations between MAI and SPO displacements along the three profiles for both tracks 126 and 486. High-correlation coefficients are observed between the two data sets: 0.90 for track 126 and 0.87 for track 486, and their RMS differences are both 11.0 cm. This precision is consistent with those reported by previous studies (Barbot et al.2008). Note that the fault trace can be clearly observed from both SPO and MAI maps (e.g. Figs 2b, c and f), but little can be seen on conventional interferograms due to the decorrelation caused by the surface ruptures (Figs 2a and e). The fault trace can be used to constrain earthquake models, but it was mainly used to validate our model in this study.
2.4 Data reduction and weighting
In order to minimize the computational task, the two interferograms and MAI maps are subsampled using the R-based method developed by Lohman & Simons (2005). Using the fault trace determined from both MAI and SPO maps (Fig. 2), we defined a simple near-vertical, strike-slip uniform fault plane to downsample of the two interferograms and MAI maps. The 620 and 608 points were obtained from tracks 126 and 486 interferograms (see Figs S6a and b), respectively, and 333 and 318 points from tracks 126 and 486 MAI displacements (Figs S6c and d), respectively.
The use of a combination of different data sets in modelling requires the determination of the weighting of each data set. We firstly weighted each point using a small window as suggested by Simons et al. (2002) with the requirement that the sum of the normalized weight for each data set should be equal to unity. Secondly, relative weights were determined according to their variances in the far field: 0.45 for conventional InSAR datapoints and 0.025 for MAI datapoints (i.e. InSAR datapoints 18 times higher).
3 EARTHQUAKE MODELLING
A two-step inversion strategy is often employed to constrain the fault parameters with InSAR observations as performed in previous studies (Wright et al.2003; Li et al.2008; Atzori et al.2009; Li et al.2011). This comprises a nonlinear inversion to determine the fault geometry by minimizing the square misfit under the assumption of a uniform slip on a rectangular fault, followed by a linear inversion for estimating the slip distribution on the determined fault plane. However, the fault geometry determined under the assumption of a uniform slip is not necessarily the optimal one (especially the dip angle) for a spatially variable slip distribution (Burgmann et al.2002; Fukahata & Wright 2008). In this paper, an iterative approach is presented to determine the optimal dip angle in the slip inversion.
In this study, multipeak particle swarm optimization (M-PSO) was employed for inverting fault geometry parameters including strike, dip, slip, length, top and bottom depth by minimizing the squared misfits between the observed and the predicted LOS displacements using a hybrid minimization algorithm (Feng & Li 2010). This algorithm has been successfully applied to several earthquakes (Li et al.2008, 2011; Feng et al.2009). Table 2 shows the best-fit uniform solution, of which the location, strike angle and the length of rupture are highly consistent with those derived directly from SPO and MAI maps as shown in Table 2 and the inverted dip has a difference of ∼7º compared with the GCMT solution.
Modela . | Location . | Focal . | Length . | Width . | Depthb . | MW . | |||
---|---|---|---|---|---|---|---|---|---|
. | Lon . | Lat . | Strike . | Dip . | Rake . | (km) . | (km) . | (km) . | . |
USGS-BW | 99.882 | 20.673 | 246 | 81 | −3 | – | – | 8 | 6.7 |
GCMT | 100.2 | 20.62 | 70 | 85 | 11 | – | – | 12.6 | 6.8 |
MAI/SPOc | 99.795 | 20.89 | 70 | – | – | – | – | – | |
Uniform Inv | 99.995 | 20.674 | 69.7 | 92.7 | 1.8 | 22.4 | 8.4 | 4.6 | 6.8 |
Iterative Inv | 99.995 | 20.674 | 69.7 | 88.3 ± 4 | 4d ± 0.5 | 60 | 20 | 10.0 | 6.8 |
Modela . | Location . | Focal . | Length . | Width . | Depthb . | MW . | |||
---|---|---|---|---|---|---|---|---|---|
. | Lon . | Lat . | Strike . | Dip . | Rake . | (km) . | (km) . | (km) . | . |
USGS-BW | 99.882 | 20.673 | 246 | 81 | −3 | – | – | 8 | 6.7 |
GCMT | 100.2 | 20.62 | 70 | 85 | 11 | – | – | 12.6 | 6.8 |
MAI/SPOc | 99.795 | 20.89 | 70 | – | – | – | – | – | |
Uniform Inv | 99.995 | 20.674 | 69.7 | 92.7 | 1.8 | 22.4 | 8.4 | 4.6 | 6.8 |
Iterative Inv | 99.995 | 20.674 | 69.7 | 88.3 ± 4 | 4d ± 0.5 | 60 | 20 | 10.0 | 6.8 |
aThe models listed in the first column are from different sources: USGS-BW is derived from body wave data by USGS, GCMT is the Global CMT solution, ‘Uniform Inv’ is the uniform slip model, and ‘Iterative Inv’ is the refined model using the iterative method demonstrated in Section 3.3.
bThe depth of GCMT solution is the centroid, while the depth of the Uniform Inversion (Uniform Inv) represents the centre of the fault plane.
cThe geometry parameters of the fault were determined directly from the MAI/SPO maps.
dThis is the average rake and its standard deviation calculated using all the rakes in the patches with a slip greater than 0.5 m.
Modela . | Location . | Focal . | Length . | Width . | Depthb . | MW . | |||
---|---|---|---|---|---|---|---|---|---|
. | Lon . | Lat . | Strike . | Dip . | Rake . | (km) . | (km) . | (km) . | . |
USGS-BW | 99.882 | 20.673 | 246 | 81 | −3 | – | – | 8 | 6.7 |
GCMT | 100.2 | 20.62 | 70 | 85 | 11 | – | – | 12.6 | 6.8 |
MAI/SPOc | 99.795 | 20.89 | 70 | – | – | – | – | – | |
Uniform Inv | 99.995 | 20.674 | 69.7 | 92.7 | 1.8 | 22.4 | 8.4 | 4.6 | 6.8 |
Iterative Inv | 99.995 | 20.674 | 69.7 | 88.3 ± 4 | 4d ± 0.5 | 60 | 20 | 10.0 | 6.8 |
Modela . | Location . | Focal . | Length . | Width . | Depthb . | MW . | |||
---|---|---|---|---|---|---|---|---|---|
. | Lon . | Lat . | Strike . | Dip . | Rake . | (km) . | (km) . | (km) . | . |
USGS-BW | 99.882 | 20.673 | 246 | 81 | −3 | – | – | 8 | 6.7 |
GCMT | 100.2 | 20.62 | 70 | 85 | 11 | – | – | 12.6 | 6.8 |
MAI/SPOc | 99.795 | 20.89 | 70 | – | – | – | – | – | |
Uniform Inv | 99.995 | 20.674 | 69.7 | 92.7 | 1.8 | 22.4 | 8.4 | 4.6 | 6.8 |
Iterative Inv | 99.995 | 20.674 | 69.7 | 88.3 ± 4 | 4d ± 0.5 | 60 | 20 | 10.0 | 6.8 |
aThe models listed in the first column are from different sources: USGS-BW is derived from body wave data by USGS, GCMT is the Global CMT solution, ‘Uniform Inv’ is the uniform slip model, and ‘Iterative Inv’ is the refined model using the iterative method demonstrated in Section 3.3.
bThe depth of GCMT solution is the centroid, while the depth of the Uniform Inversion (Uniform Inv) represents the centre of the fault plane.
cThe geometry parameters of the fault were determined directly from the MAI/SPO maps.
dThis is the average rake and its standard deviation calculated using all the rakes in the patches with a slip greater than 0.5 m.
estimate the solutions using BVLS with the variations of α2 for a given dip angle. The fitting residuals (ξ) in the unit of metre and the model roughness (ψ) in the unit of metre per kilometre can be retrieved.
Note that the amplitude of α2 is related to the definition of L in eq. (2). We set a series of α2 in the range of [0.1,10] with an interval of 0.5.
calculate ξ and ψ relating to a series of dips in the range of [80°, 100°] with an interval of 1° in an iterative way;
normalize ξ and ψ using the following simple expression |${\raise0.7ex{{(\{ .\} - {\rm min}\{ .\} )}} \!/\!\lower0.7ex{{({\rm max}\{ .\} - {\rm min}\{ .\} )}}}$|, where {.} denotes the series of ξ and ψ. Note both variables become dimensionless.
calculate f(δ, α) for any given (δ, α) and plot fon a diagram as shown in Fig. 4(b), from which the optimal dip angle and smoothing operator can be directly determined.
The optimal dip angle and smoothing factor determined using the Log-function were directly employed to further develop the distributed slip model (model A). The uncertainty of slip solutions was calculated from 100 perturbed data sets that were created by adding simulated noise to the observations as proposed by Parsons et al. (2006). The standard deviation of slip at each patch is shown in Fig. 4.
Fig. 5(a) shows the optimal slip distribution of the 2011 Burma earthquake from both conventional InSAR and MAI observations (i.e. model A). The slip distribution is characterized by a peak amplitude of ∼4.2 m at a depth of 2–4 km on a vertical, purely sinistral strike-slip rupture reaching to the surface. The total released moment is about 1.8 × 1019 N m (assuming a rigidity of 3.2 × 1010 Pa) and is equivalent to moment magnitude MW 6.8. The major zone of slip is confined between the depths of 2–10 km, with a rupture length of ∼26 km. The maximum slip uncertainty reaches 0.5 m (Figs 5c and d), less than 10 per cent of the maximum rupture slip.
Fig. 5(e) shows the slip distribution determined using InSAR observations only (i.e. model B), and Fig. 5(g) shows the difference between model A and model B. It appears that the depth at which the major seismic moment was released drops ∼1.5 km from 2 km in model A to 3.5 km in model B, which is most likely due to the fact that MAI provided additional constraints in the near field where there was an absence of interferometric data.
Fig. 6 shows the simulated interferograms, AZI displacements and residuals from model A. The modelled interferograms can sufficiently explain InSAR observations with standard deviations of 1.6 and 1.4 cm for tracks 486 and 126, respectively. The RMS differences between SPO and modelled observations are 26 and 25 cm for tracks 126 and 486, respectively (Figs S7 and S8), while the RMS differences are ∼15 cm for both MAI maps. However, residuals of up to 5–6 cm can be observed close to the fault in Figs 6(c) and (f). One probable cause for these residuals in the near field is the slightly curved and stepping geometry of the real fault trace so that the simplified fault plane model fails to reproduce high fringe gradients close to the fault. Several previous InSAR studies have also reported that a simple elastic dislocation generally lacks the capability to model near‐fault processes (e.g. Lohman & Simons 2005; Li et al.2011). The maximum residual is located at the east end of the fault trace, where a small rupture segment with the amount of ∼0.2 m slip has been found in the slip distribution of model B. However, such a slip patch is not shown in model A, although there is a relatively big uncertainty in the same area (Fig. 5c). Note that landslides have been reported to have followed the main shock in this area, killing at least 10 people during the rupture (Vervaeck & Daniell 2011), which could partly explain the residual artefact. However, further evidence is required to fully support this point.
4 DISCUSSION
4.1 Coseismic slip deficit for strike-slip earthquakes
Coseismic slip deficits have been reported in several previous studies on slip distributions constrained with geodetic data for large strike-slip earthquakes (Fialko et al.2002; Fialko 2004; Fialko et al.2005; Kaneko & Fialko 2011), in other words, the inverted coseismic slip decreases towards the Earth surface. These studies suggested that inelastic deformation might be a major factor for the observed shallow slip deficits, which could introduce an ‘artificial’ deficit of up to 10 per cent of the maximum slip inferred from geodetic data.
A similar feature can also be observed in Fig. 5(a) and most of the seismic moment of the 2011 Burma earthquake was released at a depth of 4–5 km (Fig. 5b) along a 30-km-long left-lateral strike-slip rupture with a significant slip of ∼2 m near the surface, which reaches 50 per cent of the maximum amplitude of the inverted slip distribution. The frictional strength is one of the major factors to control the main rupture, and this strength should increase with depth (Das & Scholz 1983), which has been widely supported by in situ stress measurements (Mcgarr et al.1982). In other words, the accumulated strain should also increase with depth before ruptures. Consequently, the increases in stress and/or strain with depth give two possible reasons why slip decreases towards the ground surface. Though our analysis cannot provide comprehensive understanding of the characteristics of slip in the uppermost crust, it is notable that the inverted slip distribution of the 2011 Burma earthquake shows similar features to several previous studies, summarized by Fialko et al. (2005).
4.2 Geomorphologic features
The NMF is one of the most active fault systems within the Burma region with an estimated slip rate of ∼3 mm yr−1 (Lacassin et al.1998) based mainly upon large scale, long-term offset river bends. The fault segment that ruptured in the March 2011 event is likely to be the westward extension of the NMF (Fig. 7). Therefore, assuming a similar slip rate range and taking into account the average coseismic slip of about 3 m for the 2011 Burma earthquake, an earthquake recurrence interval of 1000–5000 yr is estimated for this segment for M ∼ 7 events (assuming that this earthquake is characteristic in this region). This recurrence interval is much less than that along the plate boundaries, in particular the Sagaing fault where the earthquake recurrence interval of M > 7 is about 100–300 yr (Wang et al.2011b). In terms of GPS measurements, the internal deformation in the Sunda plate is generally very small compared to the boundary of the plates (Simons et al.2007), which is consistent with the basic knowledge to the NMF from local geomorphologic observations.
Hillshaded topography reveals a sinistrally offset ridge across the fault trace of the 2011 event (Fig. 7a). The offsets are of the order ∼3 km, recording the long-term fault displacement. Assuming a constant long-term slip rate equivalent to the NMF, this suggests that the fault activity started at least 1–5 Ma ago.
5 CONCLUSIONS
In this paper, we have demonstrated the key feature of SPO and MAI techniques: both can provide surface displacements in the along-track direction, and can be used to determine fault traces. We have also introduced a new statistic variable in a Log-function to simultaneously determine the optimal fault dip angle and smoothing factor when modelling slip distribution.
InSAR observations have been used to constrain the fault geometry of the 2011 March 24th Burma earthquake. Our optimal slip inversion for this large event indicates: (1) the rupture occurred on a vertical pure sinistral strike-slip fault with a strike of ∼70°; (2) the maximum slip is 4.2 m, occurring at a depth of 2.5 km; and (3) the total releasing moment is about 1.8 × 1019 N m, which is equal to a moment magnitude of 6.8. Our model suggests that the fault segment that ruptured in this event is likely to be the westward extension of the NMF (Fig. 7). However, this fault has not been identified in previous studies (Styron et al.2010).
WF is supported by a China Scholarship Council (CSC) scholarship. This work was supported by the Natural Environmental Research Council (NERC) through the GAS project (Ref: NE/H001085/1), and the National Centre of Earth Observation (NCEO), of which the Centre for the Observation and Modeling of Earthquakes, Volcanoes and Tectonics (COMET+, http://comet.nerc.ac.uk) is a part. Part of this work was supported by National Natural Science Foundation of China (Project IDs: 41104028 and 41074005). We are grateful to JPL/Caltech for use of the ROI_PAC software. Most figures were made using the public domain Generic Mapping Tools (Wessel & Smith 1998). The ALOS data used in this study were shared by PIXEL (PALSAR Interferometry Consortium to Study our Evolving Land surface) and were provided from the Japan Aerospace Exploration Agency (JAXA) through a joint research contract between JAXA and the Earthquake Research Institute, University of Tokyo. The ownership of PALSAR data belongs to JAXA and Ministry of Economy, Trade and Industry of Japan. We are very grateful to the anonymous reviewers, and Editor Bert Vermeersen for thoughtful and thorough reviews that significantly improved this manuscript.
REFERENCES
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online version of this article:
Figure S1. (a) A trade-off curve line associated with the model with a dip angle of 87.3°. The thick and thin dashed black lines show the trends of model roughness and the residuals of modelled simulations after normalizing ([ξ, ψ]), respectively, while the solid grey line represents log(ξ + ψ). (b) Contour map of log(ξ + ψ) with variations of dips and hyperparameters (α2). White star indicates the point of global minimum.
Figure S2. (a) The simulated slip model with a magnitude of 6.5. (b) The optimal slip model determined with subsampled displacements using the PSOKINV package.
Figure S3. 3-D surface displacements: (a) easting component; (b) northing component and (c) UP component.
Figure S4. (a) Track 126D: conventional interferogram; (b) far-field data of (a); (c) 2-D variogram calculated using all the valid pixels in the far-field as shown in (b). (d), (e) and (f) are similar to (a), (b) and (c) but for track 486A.
Figure S5. (a) and (c) azimuth offset map and along-track interferogram for track 126D, (b) and (d) are similar to (a) and (c), but for track 486A. (e), (f), (g) and (h) are corrected versions of (a), (b), (c) and (d) after removing a two-order best-fitting polynomial surface. Note: Only data in the non-deforming area as defined in Fig. S4 were used to estimate the best-fitting polynomial surface.
Figure S6. Resampled datapoints from: (a) track 126D interferogram, (b) track 486A interferogram, (c) track 126D MAI and (d) track 486A MAI. Note: the resolution-based (R-based) method proposed by Lohman & Simons (2005) was employed in this study.
Figure S7. (a) Range change from track 126D, (b) modelled range change, (c) residuals of (a minus b), while (d), (e) and (f) for azimuth offsets in the same track as (a), (b) and (c). (g), (h) and (i) are similar to (a), (b) and (c) but for range changes from track 486A, and (j), (k) and (l) are similar to (d), (e) and (f) but for azimuth offsets from track 486A. The red lines mark the surface projection of the top boundary of the uniform model and the black dashed line indicates the 60-km-long model for the slip distribution.
Figure S8. (a), (c) and (e) Comparisons of the NNE-SSW profiles of A−A′, B−B′ and C−C′ from track 126 as shown in Fig. 2(b), (d) and (f) are similar to (a), (c) and (e) but for track 486A. Green diamonds denote the SPO range offsets, blue triangles imply the conventional InSAR displacements and dashed red lines represent the modelled measurements. The grey-shaded region indicates topography along each profile, and the white lines down to the x-axis shows the location of the seismic fault.
Figure S9. Trade-off curve lines between model roughness and residuals (root mean square, RMS).
Figure S10. Uncertainties and trade-offs of single fault model parameters computed using Monte Carlo analysis. Scatterplots show degrees trade-off between pairs of model parameters, and the red triangle implies the best-fit solution for the uniform model as listed in Table 2. Histograms show the uncertainty in individual model parameter. μ and ξ are the mean and standard deviation of the distribution of each parameter with a 95 per cent confidential interval, respectively.
Table S1. Comparison of the geometry parameters between the Burma slip model and numerical experiment model (Supplementary Data).
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