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Article

Focal Mechanism and Regional Fault Activity Analysis of 2022 Luding Strong Earthquake Constraint by InSAR and Its Inversion

by
Wenshu Peng
*,
Xuri Huang
and
Zegen Wang
School of Earth Science and Technology, Southwest Petroleum University, Chengdu 610500, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(15), 3753; https://doi.org/10.3390/rs15153753
Submission received: 23 May 2023 / Revised: 22 July 2023 / Accepted: 24 July 2023 / Published: 28 July 2023
(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)
Browse Figures
Figure 1
<p>Plate tectonic setting and seismogenic environment of the 2022 Luding Ms 6.8 earthquake.</p> "> Figure 2
<p>Tectonic map of the southeastern Tibetan Plateau.</p> "> Figure 3
<p>Coseismic deformation field of Luding 2022 earthquake: (<b>a</b>) the ascending orbit interference fringe; (<b>b</b>) the descending orbit interference fringe; (<b>c</b>) the ascending orbit LoS deformation; (<b>d</b>) the descending orbit LoS deformation. The yellow star is the location of the epicenter given by the CENC.</p> "> Figure 4
<p>Observed, modeled coseismic displacements and residuals of the 2022 Luding earthquake. (<b>a</b>) Observed Ascending displacement. (<b>b</b>) Modeled Ascending displacement. (<b>c</b>) Residuals of Ascending displacement. (<b>d</b>) Observed Descending displacement. (<b>e</b>) Modeled Descending displacement. (<b>f</b>) Residuals of Descending displacement. The black box is the seismic fault.</p> "> Figure 5
<p>Fault slip distribution from InSAR inversion. (<b>a</b>) 2D coseismic sliding distribution model (<b>b</b>) 3D coseismic sliding distribution model.</p> "> Figure 6
<p>LoS displacement acquired by InSAR ascending (<b>a</b>) and descending (<b>b</b>) orbits. Yellow star is the epicenter.</p> "> Figure 7
<p>Cross-sections along the SE-NW and SW-NE transects shown in <a href="#remotesensing-15-03753-f003" class="html-fig">Figure 3</a> panels for the cumulated ascending (<b>a</b>,<b>b</b>) and descending (<b>c</b>,<b>d</b>) LoS displacement maps. Red lines are profile lines by fitting the points on the deformation field the section line passes. The gray areas are formation sections.</p> "> Figure 8
<p>Aftershocks and Coulomb stress distribution of the 2022 event. (<b>a</b>) CFS and aftershocks displacement on XSHF. (<b>b</b>) CFS and aftershocks distribution on profile A–A’. (<b>c</b>) CFS and aftershocks distribution on profile B–B’. (<b>d</b>) CFS and aftershocks distribution on profile C–C’. (<b>e</b>) CFS and aftershocks distribution on profile D–D’.</p> "> Figure 9
<p>Spatial and temporal evolution of aftershocks. (<b>a</b>) Aftershocks distribution 0–80 min after main shock. (<b>b</b>) Aftershocks distribution 80 min to 24 h after main shock. (<b>c</b>) Aftershocks distribution 24 h to 2 days after main shock. The orange circles are the aftershocks distribution, and the yellow pentagram is the epicenter. Black lines are projections of faults on the ground.</p> "> Figure 10
<p>Coulomb stress change caused by Luding earthquake to the surrounding fault. (<b>a</b>) CFS change of 2022 Luding earthquake on Longmenshan fault. (<b>b</b>) CFS change of 2022 Luding earthquake on Anninghe fault. (<b>c</b>) CFS change of 2022 Luding earthquake on Daliangshan fault.</p> ">
Review Reports Versions Notes

Abstract

:
On 5 September 2022, an Ms6.8 magnitude earthquake occurred in Luding County, Sichuan Province, China. Based on Sentinel-1 SAR images, this paper uses the D-InSAR approach to obtain the displacement field of the earthquake, invert the coseismic sliding distribution, and then calculate the static coulomb stress changes of the coseismic deformation on the aftershock distribution and surrounding faults. Further, the seismic structure is analyzed and discussed. The InSAR coseismic deformation field demonstrates that the maximum LoS displacement of the surface deformation caused by the Luding earthquake is about 15 cm. The Luding Ms 6.8 earthquake is dominated by the Moxi fault, which is a left-lateral strike-slip fault that ruptures along the NNW-SSE trend at about 160.3°, and the dip is 81°. The fault depth is mainly 5~15 km, the maximum sliding amount is about 174.8 cm, and the corresponding depth is 8.5 km. The seismic moment tensor obtained by inversion is 1.06 × 1019 Nm, Mw = 6.65. The Coulomb stress generated by the Luding earthquake on the northern end of the Anninghe fault zone exceeded the trigger threshold. The risk of the Anninghe fault’s future earthquake was greater, and continuous monitoring and risk assessment were required.

1. Introduction

On 5 September 2022, an Ms6.8 earthquake occurred in Luding County, Sichuan Province, China, causing 93 deaths, 420 injury casualties, the destruction of at least 50,000 houses, and severe infrastructural damage. More than 110,000 people were affected. The epicenter was located at Hailuogou Glacier Forest Park, 39 km away from Luding County and 226 km away from Chengdu City. The population within 20 km of the epicenter is about 1.55 million. The average altitude within 5 km of the epicenter is about 2684 m [1]. The Luding earthquake is the fifth M ≥ 6 earthquake that has occurred in the central and eastern parts of the Qinghai–Tibet Plateau in the past two years, following the Yangbi M6.4, Maduo M7.4, Lushan M6.1, and Malkang M6.0 earthquakes. However, compared to the other four earthquakes, the Luding earthquake caused more serious casualties, building damage, and secondary disasters and was the most high-profile earthquake in China in recent years.
After the earthquake, relevant departments and researchers processed and inverted the relevant seismic data and analyzed the tectonic background of the earthquake to discuss the causes of the earthquake and the future earthquake development trend [2]. The focal mechanism given by different institutions showed that the earthquake was a rupture event dominated by a strike-slip fault; the seismic fault parameters were given by different institutions (Table 1). Based on these analysis results and combined with the active tectonic background of the earthquake area, it is preliminary concluded that the seismic structure of the Luding Ms6.8 earthquake is the southern and eastern sections of the Xianshuihe fault zone (XSHF).
The XSHF is the main component of the southern boundary of the Bayan Har block on the Qinghai–Tibet Plateau (Figure 1) [3,4]. It starts from the north-west of Ganzi County, goes south and east through Luhuo, Daofu, Kangding, and Moxi, and extends to the south of Xinmin, and then gradually weakens its activity and finally disappears near the Shimian [5]. The total length of the fault is about 400 km, the overall direction is 320°~330°, it has shown a strong left-handed strike-slip motion since the late Cenozoic, and the total scale of horizontal displacement is about 60 km [6]. Based on the plane geometry and discontinuity of the fault, the Xianshuihe fault zone can be divided into two parts, the north-west part and the south-east part, bounded by the Huiyuansi pull-apart basin, and both have obvious differences in geomorphological characteristics and activity. The north-west section generally shows a simple geometric structure, consisting of the Luhuo section, Daofu section, and Qianning section in a left-order oblique column to form a main fault, and the average sliding rate is 10~15 mm/a [7,8,9,10]. The structure of the south-east section is relatively complex; the Serah-Kangding section is formed by three secondary faults spreading out in parallel; the sliding rate of a single fault is less than 10 mm/a, but the sum of the sliding rates of the three faults is about 10 mm/a [11]. The fault south of Kangding extends as a single main fault, and the sliding rate value is also about 6~10 mm/a [12].
The history of earthquakes in the XSHF is relatively short, with more than 10 destructive earthquakes since 1747 AD [13]. Over the past 1700 years, there have been eight earthquakes of magnitude M7.0 or higher along the fault zone (Figure 2). The most recent two events were the 7.75-magnitude earthquake in Kangding-Luding on 1 June 1786, and the 7.9-magnitude earthquake in Luhuo, 1973 [14]. Relevant studies show that after the 8.0 magnitude Wenchuan earthquake in 2008 and the 7.0-magnitude Lushan earthquake in 2013, the Coulomb stress around the XSHF increased significantly [15]. The strike-slip rate of the fault zone increased from north-west to south-east, which could induce a major earthquake [16]. Papadimitriou et al. [17] considered the historical focal mechanism and interseismic loading effects of the XSHF in 2004 and found that the fault zone had a high stress accumulation. Xu et al. [18] added a discussion of post-earthquake viscosity relaxation based on the work of Papadimitriou et al. Their results showed that the Coulomb stress accumulation in the western grinding section was the most significant. Cheng [19] et al. analyzed the sliding rate of the XSHF in 2014 and 2021, respectively, and found that the XSHF has a high probability of inducing strong earthquakes in the next few decades. Pan et al. [20] used GPS data to show that there is a GPS deformation anomaly in the western part of the Qinghai–Tibet Plateau, and its seismic risk is the highest. According to Zhang et al. [21], the western section of the XSHF showed creep-slip characteristics in 2015~2017 and was in the post-earthquake recovery stage, and the creep-slip phenomenon disappeared in 2018~2019, starting a new round of inter-earthquake strain accumulation. The results of the above studies predicted the occurrence of the earthquake within a certain range.
Due to the large undulating terrain near the epicenter, the large area covered by glaciers, the harsh climatic conditions, and the sparse GNSS site, it is not possible to use GNSS geodesy to obtain a reliable coseismic deformation field, so it is important to use synthetic aperture radar (SAR) data. Interferometric synthetic aperture radar (InSAR) technology has become an important technology in seismogeodetics with its outstanding advantages of strong penetration ability and continuous spatial coverage. In recent years, with the continuous development of InSAR technology, it has made many achievements in the extraction of interseismic deformation in large areas of complex terrain. For example: 2015 Gorkha earthquake [22], 2015 Illapel earthquake [23], 2016 Kaikōura earthquake [24], 2016 Kumamoto earthquake [25], 2017 Jiuzhaigou earthquake [26], et al.
Because the aftershock distribution strip is located slightly west in the surface trace of the Moxi fault, some researchers believe that the seismic structure may not be the main fault of XSHF but a secondary fault in the NW direction. The 2022 Mw 6.8 Luding earthquake is the only strong earthquake that happened after the 1786 Mw 7.4 Luding earthquake on the XSHF. Based on the above, there are two issues still not proven: Whether the 2022 Luding earthquake is a repeat of the historical earthquake rupture section or occurred in the earthquake empty section, and which fault is the earthquake-generating structure of the 2022 Luding earthquake. This paper provides an important opportunity to determine the seismic structure of the Luding earthquake. Studying the fault system in the southern section of the XSHF is of great theoretical and practical significance for an in-depth understanding of the cause of this earthquake, the current activity of the southern and eastern sections of the XSHF, and the future seismic risk assessment in the eastern boundary of the Sichuan-Yunnan block.
In this paper, the Sentinel-1 ascending and descending orbit data were used to obtain coseismic deformation fields. In addition, we inverted the distribution of afterslip to evaluate the contribution and implication of the Luding earthquake on regional crustal deformation and tectonic activity. Finally, we investigate the Coulomb stress-related triggering relationship between this event and its peripheral fault zone based on the finite-fault rupture process model.

2. Materials and Methods

2.1. InSAR Data Processing

In this study, the ascending and descending SAR data from Sentinel-1A satellites were used to obtain the coseismic deformation maps (Table 2). Based on Sentinel-1 SAR images, the coseismic deformation field of the earthquake was obtained by D-InSAR technology, and the coseismic sliding distribution was obtained by inversion. Then, the static Coulomb stress changes of the aftershock distribution and the surrounding faults can be calculated. Finally, the seismic structure was analyzed and discussed.
The Sentinel-1 satellite has a short revisit period and high orbital accuracy and can still obtain high-quality interferograms in vegetation-covered areas [27]. Since being launched in 2014, the Sentinel-1 satellite has provided important surface deformation data constraints for inversion studies of the source parameters of several strong earthquakes around the world, such as the Napa earthquake in the United States [28], the Nepal earthquake [29], and the Meinong earthquake in Taiwan [30]. We downloaded the Sentinel-1 SAR image data (interferometric width mode (IW)) covering the whole earthquake area after the Luding earthquake. The shorter vertical baseline and time interval of the interferogram reduce the influence of external DEM (digital elevation model) errors on the isoseismic deformation results and ensure the coherence of the interferogram.
InSAR data processing is based on the two-track method [16] to generate the coseismic interference deformation field of the Luding earthquake. In the data processing procedure, precision orbital data provided by ESA and the digital elevation model (DEM) provided by the National Aeronautics and Space Administration (NASA) were used to remove the influence of geomorphological phase [31]. In order to improve the signal quality of the interferogram, the Steerable Pyramid filtering method is used to reduce the noise of the interferogram [32]. Steerable Pyramid filtering using iterative adaptive filtering for each layer of the Laplace pyramid: Iterative adaptive filtering first performs a local frequency estimation on the input images, keeping the main frequency part in the image. Then we perform Goldstein filtering of the image residues based on the coherence and adaptive determination of the relevant parameters. The iterative adaptive filtering result of each layer of the input image is obtained. The difference map acquired from the subsampled image and the subsampled image of the next layer are iteratively adaptively filtered and deposited in the Laplace pyramid. Finally, the Laplace pyramid was reconstructed to obtain the final image filtering results.
Phase unwrapping adopts the minimum-cost flow (MCF) algorithm based on the Delaunay triangulation network [33,34], and removes the residual orbital phase in the interferogram by quadratic polynomial fitting. The theme idea of Delaunay MCF is: First, find the phase with a high coherence coefficient, extract it into a high-quality phase set, and then use the Delaunay triangle network to identify the residuals in the triangle network so that the positive and negative residues of these high-quality phase data are almost connected and the branch cutting line is established. Finally, we can solve the integral using the method to cross the branch cutting line and get the final phase disentanglement result. The phase delay caused by vertical stratification of the atmosphere in the differential interferogram is corrected by downloading the corresponding generic atmospheric correction online service for InSAR (GACOS) data [35]. In order to estimate the orbital error phase more finely and reduce the interference of terrain error phase, tropospheric delay error phase, and noise phase caused by external DEM on orbital error phase estimation, a multi-resolution phase analysis of the differential interference phase in InSAR is carried out, the interference phase is separated into different frequency spatial scales, and then the quadratic polynomial model is used to estimate and remove the orbital error in the differential interferogram. For the residual atmospheric turbulent delay, according to the space-time characteristics of space-related time and uncorrelated space-time, time-domain high-pass filtering and space-domain low-pass filtering are used to estimate and weaken. The ionospheric delay in the C-band Sentinel-1 deformation has not been removed, as its impact can be negligible.

2.2. Coseismic Slip Modeling

The simulation of the coseismic deformation field is one of the most important means to improve our understanding of seismic structure and to evaluate regional earthquake disasters. In this paper, the geometric model of seismic faults is determined according to the results of aftershock repositioning. Then, the coseismic displacement distribution of seismic faults is inverted by using the InSAR coseismic deformation field as a constraint so as to determine the fine motion characteristics of seismic faults. Due to the large amount of InSAR coseismic deformation field data and the highly correlated deformation results in space, the interferometric map is downsampled to obtain an appropriately sized InSAR deformation dataset before the fault geometry parameters are inverted. We use the quadtree method [36] to downsample the ascending deformation field, which can maximize the preservation of the spatial characteristics of the original coseismic deformation according to the deformation gradient. However, for the descending deformation field, due to the serious noise of the interferogram, it is difficult to obtain the spatial characteristics of the coseismic deformation by the quadtree method. We use the uniform sampling method, which can effectively reduce the influence of the data from some observation areas with large errors on the overall deformation results. In the actual sampling process, the sampling points are relatively dense for the near-field region and relatively sparse for the far-field region, which can retain the spatial characteristics of the original deformation field to the greatest extent. Before downsampling, the original ascending image had 839 rows, 1012 columns, and a total of 801,852 points. The quadtree threshold is set to 15 cm; after downsampling, the image becomes 105 rows and 127 columns; the total number of points is 10,372; and the point compression ratio is 94.2%. For the descending orbit image, the uniform sampling interval is 500 m near the deformation field and 2000 m in the model inversion range, and the number of sampling points is 11,220.
Before inversion, it is necessary to determine the geometric model of the seismic fault. In general, if there is no other prior information, the fault geometry parameters of the slip distribution model can be inverted by the nonlinear inversion method and the OKADA dislocation model [37]. The Okada model is a function of the relationship between the underground fault parameters and the surface deformation data, mainly to simulate the observed interference deformation field and estimate the fault parameters. The main steps of the coseismic sliding distribution of an inversion fault include InSAR observation downsampling, nonlinear inversion fault geometry parameters (longitude, latitude, dip, strike, rake, depth, length, width, and slip of the fault), and the fine sliding distribution of the linear inversion fault surface.
In this paper, a two-step strategy [38] is used to perform seismic dislocation model inversion:
(1) The rectangular fault is assumed to slide uniformly under a uniform elastic semi-space; the geometric parameters of the faults were determined by the minimum square mismatch method. In the inversion, the shear modulus was set to 3.23 × 1010 Pa and the Poisson’s ratio to 0.25. According to the source mechanism solution given by different institutions (Table 1), combined with the deformation field characteristics, the fault strike is set between 120 and 190. The search range for the length and width of the faults is determined based on the deformation field. The depth of the fault is set within 0–20 km; the rake is set within −30°–30°; and the dip of the fault is set within 30°–90° according to the background of the regional structure. Under the above parameter settings, the multiple peak particle swarm optimization (MPSO) algorithm searched for the optimal uniform sliding fault parameters (Table 1), and then the error of the uniform sliding inversion fault geometry parameters was estimated using Monte Carlo correlation noise simulation [19,25,38,39]. During the inversion, the function between the InSAR observations and the fault parameters is first established:
D o b s = G m + ε D l o s = D U cos ⁡ θ + D N sin ⁡ α sin ⁡ θ ⁡ − D E cos ⁡ α sin ⁡ θ
In Formula (1), Dobs is the value of observation on surface deformation, including the displacement components (DU, DN, DE) in the vertical, north-south, and east-west directions.  G  is the Green function, while m represents the fault geometry parameters and sliding parameters.  ε  represents the error. Dlos represents the line-of-sight deformation acquired by InSAR. α represents the azimuth, and θ represents the angle of incidence.
(2) The sliding distribution of the fault is determined by uniform inversion. Prior to inversion, the length (along strike) and width (along dip) of the rectangular fault surface were extended to 30 km and 16 km, respectively, and discretized into 1 km × 1 km-sized patches, and the fault top depth was set to 0. In uniform elastic half-space, the direction of each patch was fixed by the linear least squares method to calculate the strike and dip components of each patch in the fault plane.
The linear relationship between sliding momentum and the observed value on the patches is:
G κ 2 L S = D l o s 0
where  κ 2  is the smooth factor; L is the Laplace second-order smoothing operator; and S is the sliding amount on each fault sheet. Since the inclination of the uniform sliding fault model obtained by previous inversion is not the optimal inclination [39] corresponding to the distributed sliding model, the method that comprehensively considers the roughness and model fit residual needs to search for the optimal inclination.
Based on the InSAR line-of-sight deformation results obtained by inversion, the Levenberg–Marquardt [40] least squares optimization algorithm is used to iterate until the convergence of the objective function at the global minimum. Nine geometric parameters (longitude, latitude, dip, strike, rake, depth, length, width, and sliding amount of fault) and six orbital parameters of the fault starting point can be obtained to determine the geometry of the fault. Then, the Monte Carlo-related noise simulation is used to estimate the error of the geometric parameters of the uniform slip inversion fault.

3. Results

3.1. Coseismic Deformation

The ascending and descending interferograms and LoS-oriented deformation of the Luding earthquake are shown in Figure 3. In Figure 3c, the maximum line-of-sight subsidence of ascending orbit T26 was 15 cm, and the maximum line-of-sight lift was 14 cm. It can be seen from Figure 3d that the maximum line-of-sight subsidence of the descending orbit T135 coseismic deformation field is 8 cm and the maximum line-of-sight lift is 13 cm. There is a difference in the subsidence and lifting amounts of the different orbits, mainly due to the different observation angles of the ascending and descending orbits. By analyzing the deformation distribution characteristics of the InSAR image, we can determine that the fault rupture extends along the direction of NNW-SSE, the interference fringes have good coherence, and the overall shape is elliptical. The surface deformation range caused by the earthquake reaches 35 km × 35 km. The opposite sign of the deformation variable observed in the images of the seismic ascending and descending orbits indicates that the fault is dominated by strike-slip fault motion [41].

3.2. Fault Slip Distribution Inversion

The fault slip distribution was inverted under the constraint of the coseismic deformation field of the ascending T26 and the descending T135 orbits, and the fault strike was set to NNW-SSE. The corresponding data simulation results are shown in Figure 4. The observed values are consistent with the modeled values, and the observed and modeled values are 90.6% and 90.2%, and the root of mean square of the corresponding residuals is 0.8 cm and 1.2 cm, and there is no obvious systematic error in the residual figure, indicating that the coseismic slip model can better simulate the surface InSAR observation data.
The 2D and 3D coseismic sliding distribution models are shown in Figure 5. The inversion results show that the fault sliding is mainly based on strike-slip motion with a small amount of overthrust component. According to the nonlinear inversion, the uniform sliding fault geometric parameters are obtained: The 2022 Luding earthquake had a fault length of 21.36 km, a width of 11.33 km, a strike of 160.3°, a dip of 81°, and a rake of 2.3°. The inversion of the fine sliding distribution of the fault is carried out based on the above results. The fault geometry results given based on nonlinear inversion will have errors. Therefore, it is necessary to re-estimate the linear inversion. After linear inversion, the fault sliding distribution and simulated deformation value are obtained. The coseismic sliding distribution is mainly concentrated at a depth of 5~15 km. The maximum sliding amount is located at 8.5 km, about 174.8 cm; the average sliding amount is 0.56 m; and the seismic scalar moment obtained by inversion is 1.06 × 1019 Nm, equivalent to magnitude Mw6.65. This result is roughly the same as that given by GCMT, USGS, and other institutions.
To better analyze the deformation patterns, we performed two perpendicular profiles, respectively, crossing the LoS ascending and descending displacement fields (Figure 6). The dashed lines in panels indicate the cross sections shown in Figure 7. Complex crustal movement and high vegetation density can lead to serious decoherence; several blank areas often exist in a coseismic deformation field. These lead to blank areas in the corresponding cross-section, so we divided the deformation field and kriging interpolated it separately according to the same density of InSAR downsampling [42] to obtain continuous simulated deformation fields. The profile line, obtained by fitting the points on the deformation field through which the section line passes, is obtained through the nonlinear least squares fitting method.
After the mainshock, the NW-SE (A–A’) transects initially show the larger ground displacement away from the sensor, then an uplift (motion towards the sensor) of the surface movement and a new downward displacement (motion away from the sensor). Such behavior is common to both the cumulative displacement patterns received from the ascending and descending orbits. In detail, along the ascending track, the maximum negative value is about −15 cm due to the larger magnitude of the main shock. The descending cumulative displacement value is slightly lower than the ascending case, up to −12 cm. The latter is probably due to the different acquisition geometries and small residuals related to the atmospheric contribution that will not make the results invalid.
Instead, concerning the SW-NE (B-B’) profile, the displacement patterns for both ascending and descending orbits show a decrease in the LoS distance occurring at a short distance of less than 10 km with values up to −5 cm followed by a 15 cm rise for a total length of 5 km. Then, a decrease occurs for a length of about 10 km.
These patterns suggest that the LoS displacements in two orbits are due to the presence of left-slip faults showing a roughly NW-SE strike and a common NE dipping with a length of more than 30 km.
After the earthquake, the stress distribution in the area will change. The Comlomb3 software can draw the profile along the fault direction and calculate the Coulomb fault stress (CFS) distribution on the profile after setting the fault strike, dip, and rake. The Moxi fault was selected as the source fault to further analyze the contribution of the 2022 Luding earthquake to regional stress changes. From the plane distribution of aftershocks, they are mainly distributed in parallel along the Moxi fault. Four sectional lines (A–A’, B–B’, C–C’, and D–D’) were placed along the parallel and vertical directions of the fault to analyze the relationship between Coulomb stress and aftershocks. Furthermore, profiles showed that most aftershocks occurred at a depth of 5 to 15 km. The A–A’ section line is parallel to the Moxi fault along its direction. Figure 8b shows that most of the aftershocks are located in the Coulomb stress release zone. The B–B’, C–C’, and D–D’ sectional lines are perpendicular to the Moxi fault. Figure 8c shows that the main shock and most of the aftershocks are located on the Moxi fault line. Most aftershocks are located in the Coulomb stress release areas. Figure 8d shows that the aftershocks are mostly distributed on the western side of the fault. Figure 8e shows that the Moxi fault is nearly vertical at the D–D’ profile, with aftershocks distributed on both sides of the fault, with more aftershocks on the west side than on the east side. The aftershock distribution characteristics show that the aftershocks distributed along the Moxi fault are mainly concentrated in the south-west wall of the fault, and the Coulomb stress in the aftershock’s distribution area is reduced. The seismic fault is mainly concentrated in the Coulomb stress release zone.

4. Discussion

4.1. The Rupture Process and Fracture Activity of 2022 Luding Earthquake

The location of aftershocks in the short term after the main shock is closely related to the rupture surface and rupture process of the main shock fault [43]. The early aftershocks are generally spread along the rupture surface of the main shock, and the length of the aftershock zone is equivalent to the rupture length. Therefore, the distribution of aftershocks can directly show the deep structure of strong earthquakes and the rupture range of faults. At the same time, the comparison with the main shock position can reflect the process and direction of rupture. Figure 9 shows how aftershocks evolve over time on a flat surface. It can be seen that within 80 min of the main shock, the aftershocks mainly occur in the rupture section of the main fault. After 80 min, aftershocks began to appear on the north-west side of the Xianshuihe fault. After 24 h, the range of aftershocks has expanded on each fault, which is generally considered to be the driver of aftermath [44,45]. The distribution of aftershocks along the Moxi fault is relatively obvious. At the same time, there is also a large heterogeneity, which is mainly manifested as three cluster regions with more aftershocks separated by sparse regions with fewer aftershocks (Figure 9c). The early aftershocks were mainly distributed south of the epicenter, consistent with the direction of rupture, and the aftershock clusters north of the epicenter were relatively delayed in time and small in number. Spatially, early aftershocks (0–80 min after the main shock) were distributed on the south-east side of the epicenter (Figure 9a). It can be assumed that the main shock rupture of this earthquake is a unilateral rupture of the position of the epicenter towards the SE. The inversion results of the earthquake rupture process also show that the earthquake is dominated by SE unilateral rupture, and the rupture duration is 16~18 s. It can be speculated that the rupture at this time corresponds to the main fault movement of this earthquake. The subsequent distribution of aftershocks (80 min–24 h after the main shock) shows that a band of aftershocks in the NEE direction appears between the conch gully and swallow gullies near the EW–E direction (Figure 9b). Some studies consider that this NE-oriented aftershock band may correspond to a fault that strikes NE [46,47,48,49,50]. However, aftershock activity mainly occurred in the middle, deep, and marginal expansion areas of the same seismic rupture zone, and in general, the aftershock distribution and the same seismic rupture had good spatial complementarity, indicating that the aftershock activity in the Luding area was caused by stress loading triggered by the same seismic rupture. A small part of the aftershock activity was distributed in the stress unloading area, indicating that the earthquake failed to release all the stress accumulated at the fault zone [2]. The focal mechanism shows that the seismic fault of the Luding earthquake is pure strike slip and nearly upright. Based on the results of the focal mechanism and aftershock distribution characteristics, this paper concluded that the seismic fault of the Luding earthquake is the west section of XSHF.

4.2. CFS Changes Associated with the 2022 Luding Earthquake

Previous studies have demonstrated that the static CFS change caused by an earthquake on the nearby faults plays an important role in promoting future strong earthquakes or delaying possible earthquakes [51,52]. Based on the elastic half-space model Okada [33], this paper calculates the CFS Changes of the 2022 Luding 6.8 earthquake on the southern section of the Daliangshan fault, the Anninghe fault, and the Longmenshan fault. The section surface of the NNW trend in the solution of the source mechanism of the Luding earthquake produced by GCMT and USGS was used as the source fault. For the calculation of the Seraha section of the Xianshuihe fault zone, the source mechanism of the 2014 Kangding Mw 5.8 earthquake announced by Yi et al. [53] was used as the receiving fault. For the southern section of the Longmenshan fault, the focal mechanism solution of the 2022 Lushan earthquake provided by the China Institute of Earthquake Prediction was used [54]. For the calculation of the Daliangshan fault and the Anninghe fault, since there has been no strong earthquake on these two faults since 2000, the manual review of the small and medium-sized source mechanism solution catalog provided by the CMT product of the center of the China Earthquake Network was adopted, and the Mw5.0 earthquake on 1 October 2014 and the MW4.7 earthquake on 31 October 2018 were used, respectively. After calculation, the Coulomb stress trigger effect caused by the Luding 6.8 magnitude earthquake in 2022 on the southern section of the Daliangshan fault (DF), Anninghe fault (AF), and Longmenshan fault (LMSF) was obtained. According to Figure 10, the CFS at the northern end of the Anninghe fault (Figure 10a) exceeded the trigger threshold and had a positive trigger effect on the southern section of the Longmenshan fault (Figure 10b) and the northern section of the Daliangshan fault (Figure 10c). However, the CFS on the main fault section did not reach the trigger threshold. It should be noted that the two main sections of the Anning River fault zone remained calm in 1480 and 1536, both after two 7.5-magnitude earthquakes, and are considered to be the main seismic vacancy areas [45]. In addition, the locked model based on InSAR and GPS data shows that the Anning main fault is now in a highly locked state [55,56]. Therefore, particular attention needs to be paid to the northern section of the Anninghe fault zone, which experienced three strong earthquakes in 1480, 1536, and 1952 and is a seismic gap area.

5. Conclusions

Based on InSAR inversion and the Coulomb stress model, this paper studies the source parameters, rupture process, seismogenic fault pattern, background seismic activity of the Luding earthquake, and the dangerous impact of the earthquake on the surrounding faults. The conclusions are as follows:
(1)
The epicenter of the Luding Ms6.8 earthquake is located at 102.08E, 29.59N. The maximum Sentinel-1 LoS uplift and subsidence values of the Luding earthquake are about 15 cm and 12 cm, respectively. Combined with the surface deformation fields of the ascending and descending orbits, the fault rupture extends along the NNW-SSE direction. The surface deformation range caused by the earthquake reaches 30 km × 30 km.
(2)
Using InSAR data as the constraint, the inversion shows that the Luding earthquake is a typical left-slip event. The strike of the main seismic source mechanism is 160.3°, the dip angle is 80.4°, and the rake is 2.3°. The coseismic sliding distribution is mainly concentrated at a depth of 5–15 km. The maximum sliding amount is located at 8.5 km. The seismic moment tensor obtained by inversion is 8.98 × 1018 N·m, equivalent to magnitude Mw6.6, which is basically consistent with the inversion results of other institutions.
(3)
Combined with the seismic inversion and the analysis of aftershocks, the Moxi fault in the south-east section of XSHF is the seismic structure of the 2022 Luding 6.8 earthquake. The left-handed strike-slip movement occurred during the occurrence of the main earthquake, which was triggered by the main earthquake, and the corresponding small movement occurred in the north of the epicenter.
(4)
The Coulomb stress results showed that after the Luding earthquake, the south and east sections of the XSHF and the northern section of the Anninghe fault zone were in a stress loading state, among which the Coulomb stress loading state in the northern section of the Anninghe fault zone was obvious, the risk of future earthquakes was greater, and continuous monitoring and risk assessment were required.

Author Contributions

Conceptualization, W.P. and X.H.; methodology, X.H.; software, Z.W.; validation, W.P., X.H. and Z.W.; formal analysis, W.P.; investigation, W.P.; resources, W.P.; data curation, W.P.; writing—original draft preparation, X.H.; writing—review and editing, W.P.; visualization, X.H.; supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Acknowledgments

The SAR images acquired by Sentinel-1A were downloaded from the Copernicus Open Access Hub and the NASA Distributed Active Archive Center at the Alaska Satellite Facility (https://earthdata.nasa.gov/eosdis/daacs/asf, accessed on 29 September 2021). The Shuttle Radar Topography Mission (SRTM) DEM with a resolution of about 90 m/pixel were downloaded from the National Aeronautics and Space Administration (https://data.nasa.gov, accessed on 21 September 2022).

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AcronymComplete Phrase
XSHFXianshuihe fault zone
USGSUnited States Geological Survey
GCMTGlobal centroid moment tensor
GFZGerman Research Centre for Geosciences
IPGPThe Geophysical Agency in Paris
GNSSGlobal navigation satellite system
InSARInterferometric synthetic aperture radar
DEMDigital elevation model
GACOSGeneric Atmospheric Correctiononline Service
MCFMinimum cost flow
CFSCoulomb fault stress
DFDaliangshan fault zone
AFAnninghe fault zone
LMSFLongmenshan fault zone

References

  1. Qu, Z.; Zhu, B.; Cao, Y.; Fu, H. Rapid report of seismic damage to buildings in the 2022 M6.8 Luding earthquake, China. Earthq. Res. Adv. 2022, 3, 100180. [Google Scholar] [CrossRef]
  2. Xu, T.R.; Dai, D.Q.; Yang, Z.G.; Xi, N.; Deng, W.Z.; Zhang, J.Y.; Liu, F.L. Preliminary study of emergency production and source parameters of the M6. 8 earthquake on 5 September 2022 in Luding, Sichuan Province. Earthq. Res. China 2022, 38, 412–424. (In Chinese) [Google Scholar]
  3. Deng, Q.; Zhang, P.; Ran, Y.; Yang, X.; Min, W.; Chu, Q. Basic characteristics of active tectonics in China. Sci. China Ser. D 2002, 32, 1020–1030. (In Chinese) [Google Scholar]
  4. Zhang, P.; Deng, Q.; Zhang, G.; Ma, J.; Gan, W.; Min, W.; Mao, F.; Wang, Q. Active tectonic blocks and strong earthquakes in the continent of China. Sci. China Ser. D 2003, 33 (Suppl. 1), 12–20. (In Chinese) [Google Scholar]
  5. Li, T.T. The Xianshuihe Fault Zone and Risk Assessment of Strong Earthquake along It; Chengdu Cartographic Publishing House: Chengdu, China, 1997; 224p. (In Chinese) [Google Scholar]
  6. Wang, E.Q.; Burchfiel, B.C.; Royden, L.H. Late Cenozoic Xianshuihe-Xiaojiang, Red River, and Dali Fault Systems of Southwestern Sichuan and Central Yunnan, China; Geological Society of American Special Paper; Geological Society of America: New York, NY, USA, 1998; Volume 327, 108p. [Google Scholar]
  7. Qian, H.; Allen, C.R.; Luo, Z.; Wen, X.; Zhou, X.; Huang, W. The active characteristics of the Xianshuihe Fault in Holocene. Earthq. Res. China 1988, 4, 9–18. (In Chinese) [Google Scholar]
  8. Wen, X.; Allen, C.R.; Luo, Z.; Qian, H.; Zhou, H.; Huang, W. Segmentation, geometric features, and their seismotectonic implications for the Holocene Xianshuihe fault zone. Acta Seismol. Sin. 1989, 11, 362–372. (In Chinese) [Google Scholar]
  9. Allen, C.R.; Luo, Z.; Qian, H.; Wen, X.; Zhou, X.; Huang, W. Field study of a highly active fault zone: The Xianshuihe Fault of southwestern China. Geol. Soc. Am. Bull. 1991, 13, 11781199. [Google Scholar] [CrossRef]
  10. Tang, R.C.; Han, W.B.; Huang, Z.Z.; Zian, H.; Zhang, Y. Active Faults and Earthquakes in Sichuan Province; Seismological Press: Beijing, China, 1993; pp. 12–20. (In Chinese) [Google Scholar]
  11. Zhou, R.J.; He, Y.L.; Huang, Z.Z.; Li, X.G.; Yang, T. The slip rate and recurrence interval of strong earthquake on the Qianning-Kangding segment of the Xianshuihe fault zone. Acta Seismol. Sin. 2001, 23, 250–261. (In Chinese) [Google Scholar] [CrossRef]
  12. Ran, Y.; Chen, L.; Cheng, J.; Gong, H. Late Quaternary activity and large earthquake recurrence along the Moxi Fault. In The Neo-Tectonics and Environment; Yan-chou, L., Ed.; Seismological Press: Beijing, China, 2001; pp. 255–266. (In Chinese) [Google Scholar]
  13. Yang, W.; Cheng, J.; Liu, J.; Zhang, X. The Kangding earthquake swarm of November, 2014. Earthq. Sci. 2015, 28, 197–207. [Google Scholar] [CrossRef] [Green Version]
  14. Zhang, Y.; Yao, X.; Yu, K.; Du, G.; Guo, C. Late-Quaternary slip rate and seismic activity of the Xianshuihe fault zone in Southwest China. Acta Geol. Sin. 2016, 90, 525–536. [Google Scholar] [CrossRef]
  15. Liu, L.; Yang, J.; Liu, X.; Mao, X.; Qin, R. Historic Earthquakes for the Xianshuihe Fault Derived From Lake Mugeco in the Southeastern Margin of the Tibetan Plateau During the Past 300 Years. Front. Earth Sci. 2022, 10, 2296–6463. [Google Scholar]
  16. Xu, X.; Wu, X.; Yu, G.; Tan, X.; Li, K. SeismoGeological Signatures for Identifying M≥7. 0 Earthquake Risk Areas and Their Premilimary Application in China’s Mainland. Seismol. Geol. 2017, 39, 219–275. [Google Scholar]
  17. Papadimitriou, E.; Wen, X.; Karakostas, V.; Jin, X. Earthquake Triggering Along the Xianshuihe Fault Zone of Western Sichuan, China. Pure Appl. Geophys. 2004, 161, 1683–1707. [Google Scholar] [CrossRef]
  18. Xu, X.; Cheng, J.; Xu, C.; Li, X.; Yu, G.; Chen, G.; Tan, X.; Wu, X. Discussion on Block Kinematic Model and Future Themed Areas for Earthquake Occurrence in the Tibetan Plateau:Inspiration from the Ludian and Jinggu Earthquakes. Seismol. Geol. 2014, 36, 1116–1134. [Google Scholar]
  19. Cheng, J.; Thomas, C.; Xu, X.W. Multisegment Rup-ture Hazard Modeling along the Xianshuihe Fault Zone, Southeastern Tibetan Plateau. Seismol. Res. Lett. 2021, 92, 951–964. [Google Scholar] [CrossRef]
  20. Pan, Y.J.; Shen, W.B. Contemporary Crustal Movement of Southeastern Tibet: Constraints from Dense GPS Measurements. Sci. Rep. 2017, 7, 45348. [Google Scholar] [CrossRef] [Green Version]
  21. Zhang, L.; Su, J.; Wang, W.; Fang, L.; Wu, J. Deep fault slip characteristics in the Xianshuihe-Anninghe-Daliangshan Fault junction region (eastern Tibet) revealed by repeating micro-earthquakes. J. Asian Earth Sci. 2022, 227, 105115. [Google Scholar] [CrossRef]
  22. Avouac, J.-P.; Meng, L.; Wei, S.; Wang, T.; Ampuero, J.-P. Lower edge of locked Main Himalayan Thrust unzipped by the 2015 Gorkha earthquake. Nat. Geosci. 2015, 8, 708–711. [Google Scholar] [CrossRef] [Green Version]
  23. Grandin, R.; Klein, E.; Métois, M.; Vigny, C. Three-dimensional displacement field of the 2015 Mw8.3 Illapel earthquake (Chile) from across- and along-track Sentinel-1 TOPS interferometry. Geophys. Res. Lett. 2016, 43, 2552–2561. [Google Scholar] [CrossRef] [Green Version]
  24. Wang, T.; Wei, S.; Shi, X.; Qiu, Q.; Li, L.; Peng, D.; Weldon, R.J.; Barbot, S. The 2016 Kaikōura earthquake: Simultaneous rupture of the subduction interface and overlying faults. Earth Planet. Sci. Lett. 2018, 482, 44–51. [Google Scholar] [CrossRef]
  25. Jiang, H.; Feng, G.; Wang, T.; Bürgmann, R. Toward full exploitation of coherent and incoherent information in Sentinel-1 TOPS data for retrieving surface displacement: Application to the 2016 Kumamoto (Japan) earthquake. Geophys. Res. Lett. 2017, 44, 1758–1767. [Google Scholar] [CrossRef] [Green Version]
  26. Ji, L.Y.; Liu, C.J.; Xu, J.; Liu, L.; Long, F.; Zhang, Z.W. InSAR Observation and Inversion of the Seismogenic Fault for the 2017 Jiuzhaigou M,7.0 Earthquake in China. Chin. J. Geophys. 2017, 60, 4069–4082. [Google Scholar]
  27. Salvi, S.; Stramondo, S.; Funning, G.; Ferretti, A.; Sarti, F.; Mouratidis, A. The Sentinel-1 mission for the improvement of the scientific understanding and the operational monitoring of the seismic cycle. Remote Sens. Environ. 2012, 120, 164–174. [Google Scholar] [CrossRef]
  28. Wei, S.; Barbot, S.; Graves, R.; Lienkaemper, J.J.; Wang, T.; Hudnut, K.; Fu, Y.; Helmberger, D. The 2014 Mw 6.1 South Napa Earthquake: A Unilateral Rupture with Shallow Asperity and Rapid Afterslip. Seism. Res. Lett. 2015, 86, 344–354. [Google Scholar] [CrossRef] [Green Version]
  29. Shan, X.J.; Zhang, G.H.; Wang, C.S.; Li, Y.C.; Qu, C.Y.; Song, X.G.; Yu, L.; Liu, Y.H. Joint inversion for the spatial fault slip distribution of the 2015 Nepal Mw 7.9 earthquake based on InSAR and GPS observations. Chin. J. Geophys. 2015, 58, 4266–4276. (In Chinese) [Google Scholar] [CrossRef]
  30. Wang, L.Y.; Gao, H.; Feng, G.C. InSAR and GPS inversion for source parameters of the 2016 MW6.4 Meinong, Taiwan earthquake. Chin. J. Geophys. 2017, 60, 2578–2588. [Google Scholar] [CrossRef]
  31. Li, Y.; Zhao, D.; Shan, X.; Gao, Z.; Huang, X.; Gong, W. Coseismic Slip Model of the 2022 Mw 6.7 Luding (Tibet) Earthquake: Pre- and Post-Earthquake Interactions With Surrounding Major Faults. Geophys. Res. Lett. 2022, 49, e2022GL102043. [Google Scholar] [CrossRef]
  32. Zhao, Y.; Huang, X.; Chen, Y.; Song, H.; Zhang, D.; Cui, X.; Dai, Y. Application of the directionally steerable pyramid method to identify geological boundaries in a conglomerate sand reservoir. Geophys. Prospect. Pet. 2021, 60, 414–420. [Google Scholar] [CrossRef]
  33. Carballo, G.F.; Fieguth, P.W. Probabilistic Cost Functions for Network Flow Phase Unwrapping. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2192–2201. [Google Scholar] [CrossRef]
  34. Yong, Y. Study on Phase Unwrapping Algorithmbased on Network Plan; Chinese Academy of Sciences: Beijing, China, 2002. [Google Scholar]
  35. Yu, C.; Li, Z.; Penna, N.T. Interferometric Synthetic Aperture Radar Atmospheric Correction Using a GPS-Based Iterative Tropospheric Decomposition Model. Remote Sens. Environ. 2018, 204, 109–121. [Google Scholar] [CrossRef]
  36. Li, P.; Gao, M.; Li, Z.; Wang, H. Evaluation of Wide-Swath InSAR Tropospheric Delay Estimation Methods over the Altyn Tagth Fault. Geomat. Inf. Sci. Wuhan Univ. 2020, 45, 879–887. [Google Scholar]
  37. Okada, Y. Internal Deformation due to Shear and Tensile Faults in a Half-Space. Bull. Seismol. Soc. Am. 1992, 82, 1018–1040. [Google Scholar] [CrossRef]
  38. Jónsson, S.H.; Zebker, P.; Segall, F. Amelung Fault slip distribution of the 1999 Mw 7.1 Hector Mine, California, earthquake, estimated from satellite radar and GPS measurements. Bull. Seismol. Soc. Am. 2002, 92, 1377–1389. [Google Scholar] [CrossRef]
  39. Burgmann, R. Deformation During the 12 November 1999 Duzce, Turkey, Earthquake, from GPS and InSAR Data. Bull. Seismol. Soc. Am. 2002, 9, 161–171. [Google Scholar] [CrossRef]
  40. Wen, Y.; Xu, C.; Liu, Y.; Feng, W.; Li, Z. The 2007 Ali Earthquake Inversion from Ascending and Descending InSAR Observations. Acta Geod. Cartogr. Sin. 2015, 44, 649–654. [Google Scholar]
  41. Wen, Y.; Li, Z.; Xu, C.; Ryder, I.; Bürgmann, R. Postseismic motion after the 2001 MW7.8 Kokoxili earthquake in Tibet observed by InSAR time series. J. Geophys. Res. Atmos. 2012, 117, B08405. [Google Scholar] [CrossRef]
  42. Lohman, R.B.; Simons, M. Some Thoughts on the Use of InSAR Data to Constrain Models of Surface Deformation:Noise Structure and Data Down Sampling. Geochem. Geophys. Geosyst. 2005, 6, 359–361. [Google Scholar] [CrossRef]
  43. Yukutake, Y.; Lio, Y. Why do aftershocks occur? Relationship between mainshock rupture and aftershock sequence based on highly resolved hypocenter and focal mechanism distributions. Earth Planets Space 2017, 69, 68. [Google Scholar] [CrossRef]
  44. Wen, X.-Z.; Ma, S.-L.; Xu, X.-W.; He, Y.-N. Historical pattern and behavior of earthquake ruptures along the eastern boundary of the Sichuan-Yunnan faulted-block, southwestern China. Phys. Earth Planet. Inter. 2008, 168, 16–36. [Google Scholar] [CrossRef]
  45. Peng, Z.; Zhao, P. Migration of early aftershocks following the 2004 Parkfield earthquake. Nat. Geosci. 2009, 2, 877–881. [Google Scholar] [CrossRef]
  46. Xu, C.; Xu, X.; Yao, X.; Dai, F. Three (nearly) Com-plete Inventories of Landslides Triggered by the May 12,2008 Wenchuan Mw 7. 9 Earthquake of China and Their Spatial Distribution Statistical Analysis. Landslides 2014, 11, 441–461. [Google Scholar] [CrossRef] [Green Version]
  47. Shan, X.J.; Qu, C.Y.; Gong, W.Y.; Zhao, D.Z.; Zhang, Y.F.; Zhang, G.H.; Song, X.G.; Liu, Y.H.; Zhang, G.F. Co-seismic Deformation Field of the Jiuzhaigou Ms 7. 0Earthquake from Sentinel-1A InSAR Data and Fault Slip Inversion. Chin. J. Geophys. 2017, 60, 4527–4536. [Google Scholar]
  48. Xie, Z.; Zheng, Y.; Yao, H.; Fang, L.; Zhang, Y.; Liu, C.; Wang, M.; Shan, B.; Zhang, H.; Ren, J. Pre-liminary Analysis on the Source Properties and Seismogenic of the 2017 Ms 7. 0 Jiuzhaigou Earthquake. Sci. China Earth Sci. 2018, 48, 79–92. [Google Scholar]
  49. Xu, J.; Shao, Z.G.; Liu, J.; Ji, L.Y. Analysis of Interaction Between Great Earthquakes in the Eastern Bayan Har Block Based on Changes of Coulomb Stress. Chin. J. Geophys. 2017, 60, 4056–4068. [Google Scholar]
  50. Jia, C.; Xiwei, X. Features of Earthquake Clus-tering from Calculation of Coulomb Stress Around the Bayan Har Block, Tibetan Plateau. Seismol. Geol. 2018, 40, 133–154. [Google Scholar]
  51. Li, Y.; Li, Y.; Liang, K.; Li, H.; Jiang, W. Coseismic displacement and slip distribution of the 21 May 2021, Ms6.4, Yangbi Earthquake derived from GNSS observations. Chin. J. Geophys. 2021, 64, 2253–2266. (In Chinese) [Google Scholar] [CrossRef]
  52. Li, Y.C.; Shan, X.J. Interseismic coupling along the Xianshuihe-Xiaojiang fault system. In China Seismic Experimental Site; Springer: Singapore, 2022; pp. 135–146. [Google Scholar]
  53. Yi, G.-X.; Long, F.; Wen, X.-Z.; Liang, M.-J.; Wang, S.-W. Seismological structure of the M6. 3 Kangding earthquake sequence on 22 November 2014, southwestern China. Chin. J. Geophys. 2015, 58, 1205–1219. (In Chinese) [Google Scholar]
  54. Zhang, J.Y.; Dai, D.Q.; Yang, Z.G.; Xi, N.; Deng, W.Z.; Xu, T.R.; Sun, L. Preliminary analysis of emergency production and source parameters of the M6.1 Earthquake on 1 June 2022 in Lushan, Sichuan Province. Earthq. Res. China 2022, 38, 360–369. (In Chinese) [Google Scholar]
  55. Jiang, G.; Wen, Y.; Liu, Y.; Xu, X.; Fang, L.; Chen, G.; Gong, M.; Xu, C. Joint analysis of the 2014 Kangding, southwest China, earthquake sequence withseismicity relocation and InSAR inversion. Geophys. Res. Lett. 2015, 42, 3273–3281. [Google Scholar] [CrossRef]
  56. Li, Y.; Bürgmann, R. Partial Coupling and Earthquake Potential Along the Xianshuihe Fault, China. J. Geophys. Res. Solid Earth 2021, 126, e2020JB021406. [Google Scholar] [CrossRef]
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Figure 1. Plate tectonic setting and seismogenic environment of the 2022 Luding Ms 6.8 earthquake.
Figure 1. Plate tectonic setting and seismogenic environment of the 2022 Luding Ms 6.8 earthquake.
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Figure 2. Tectonic map of the southeastern Tibetan Plateau.
Figure 2. Tectonic map of the southeastern Tibetan Plateau.
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Figure 3. Coseismic deformation field of Luding 2022 earthquake: (a) the ascending orbit interference fringe; (b) the descending orbit interference fringe; (c) the ascending orbit LoS deformation; (d) the descending orbit LoS deformation. The yellow star is the location of the epicenter given by the CENC.
Figure 3. Coseismic deformation field of Luding 2022 earthquake: (a) the ascending orbit interference fringe; (b) the descending orbit interference fringe; (c) the ascending orbit LoS deformation; (d) the descending orbit LoS deformation. The yellow star is the location of the epicenter given by the CENC.
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Figure 4. Observed, modeled coseismic displacements and residuals of the 2022 Luding earthquake. (a) Observed Ascending displacement. (b) Modeled Ascending displacement. (c) Residuals of Ascending displacement. (d) Observed Descending displacement. (e) Modeled Descending displacement. (f) Residuals of Descending displacement. The black box is the seismic fault.
Figure 4. Observed, modeled coseismic displacements and residuals of the 2022 Luding earthquake. (a) Observed Ascending displacement. (b) Modeled Ascending displacement. (c) Residuals of Ascending displacement. (d) Observed Descending displacement. (e) Modeled Descending displacement. (f) Residuals of Descending displacement. The black box is the seismic fault.
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Figure 5. Fault slip distribution from InSAR inversion. (a) 2D coseismic sliding distribution model (b) 3D coseismic sliding distribution model.
Figure 5. Fault slip distribution from InSAR inversion. (a) 2D coseismic sliding distribution model (b) 3D coseismic sliding distribution model.
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Figure 6. LoS displacement acquired by InSAR ascending (a) and descending (b) orbits. Yellow star is the epicenter.
Figure 6. LoS displacement acquired by InSAR ascending (a) and descending (b) orbits. Yellow star is the epicenter.
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Figure 7. Cross-sections along the SE-NW and SW-NE transects shown in Figure 3 panels for the cumulated ascending (a,b) and descending (c,d) LoS displacement maps. Red lines are profile lines by fitting the points on the deformation field the section line passes. The gray areas are formation sections.
Figure 7. Cross-sections along the SE-NW and SW-NE transects shown in Figure 3 panels for the cumulated ascending (a,b) and descending (c,d) LoS displacement maps. Red lines are profile lines by fitting the points on the deformation field the section line passes. The gray areas are formation sections.
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Figure 8. Aftershocks and Coulomb stress distribution of the 2022 event. (a) CFS and aftershocks displacement on XSHF. (b) CFS and aftershocks distribution on profile A–A’. (c) CFS and aftershocks distribution on profile B–B’. (d) CFS and aftershocks distribution on profile C–C’. (e) CFS and aftershocks distribution on profile D–D’.
Figure 8. Aftershocks and Coulomb stress distribution of the 2022 event. (a) CFS and aftershocks displacement on XSHF. (b) CFS and aftershocks distribution on profile A–A’. (c) CFS and aftershocks distribution on profile B–B’. (d) CFS and aftershocks distribution on profile C–C’. (e) CFS and aftershocks distribution on profile D–D’.
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Figure 9. Spatial and temporal evolution of aftershocks. (a) Aftershocks distribution 0–80 min after main shock. (b) Aftershocks distribution 80 min to 24 h after main shock. (c) Aftershocks distribution 24 h to 2 days after main shock. The orange circles are the aftershocks distribution, and the yellow pentagram is the epicenter. Black lines are projections of faults on the ground.
Figure 9. Spatial and temporal evolution of aftershocks. (a) Aftershocks distribution 0–80 min after main shock. (b) Aftershocks distribution 80 min to 24 h after main shock. (c) Aftershocks distribution 24 h to 2 days after main shock. The orange circles are the aftershocks distribution, and the yellow pentagram is the epicenter. Black lines are projections of faults on the ground.
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Figure 10. Coulomb stress change caused by Luding earthquake to the surrounding fault. (a) CFS change of 2022 Luding earthquake on Longmenshan fault. (b) CFS change of 2022 Luding earthquake on Anninghe fault. (c) CFS change of 2022 Luding earthquake on Daliangshan fault.
Figure 10. Coulomb stress change caused by Luding earthquake to the surrounding fault. (a) CFS change of 2022 Luding earthquake on Longmenshan fault. (b) CFS change of 2022 Luding earthquake on Anninghe fault. (c) CFS change of 2022 Luding earthquake on Daliangshan fault.
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Table
Table 1. Fault geometry and source mechanisms published by different research institutions.
Table 1. Fault geometry and source mechanisms published by different research institutions.
SourceMagnitudeDepth/kmDipStrikeRake
USGS6.61273254178
GCMT6.718.48816317
GFZ6.618801648
IPGP6.8982163170
This paper6.6512.881160.32.3
Table
Table 2. Details of SAR data used in this study.
Table 2. Details of SAR data used in this study.
Orbit TypeTrackReferenceSecondaryTime IntervalSpatial Interval
Ascending2626 August 202219 September 20222432.8
Descending1352 September 202214 September 202212−49.3
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MDPI and ACS Style

Peng, W.; Huang, X.; Wang, Z. Focal Mechanism and Regional Fault Activity Analysis of 2022 Luding Strong Earthquake Constraint by InSAR and Its Inversion. Remote Sens. 2023, 15, 3753. https://doi.org/10.3390/rs15153753

AMA Style

Peng W, Huang X, Wang Z. Focal Mechanism and Regional Fault Activity Analysis of 2022 Luding Strong Earthquake Constraint by InSAR and Its Inversion. Remote Sensing. 2023; 15(15):3753. https://doi.org/10.3390/rs15153753

Chicago/Turabian Style

Peng, Wenshu, Xuri Huang, and Zegen Wang. 2023. "Focal Mechanism and Regional Fault Activity Analysis of 2022 Luding Strong Earthquake Constraint by InSAR and Its Inversion" Remote Sensing 15, no. 15: 3753. https://doi.org/10.3390/rs15153753

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