Rupture Process of the M_s 7.0 Lushan Earthquake Determined by Joint Inversion of Local Static GPS Records, Strong Motion Data, and Teleseismograms

0. INTRODUCTION

The M_s 7.0 Lushan earthquake that struck Lushan County and its surroundings in Sichuan Province caused serious damage in the source region. This earthquake, located in the southern segment of the Longmenshan, occurred about 100 km from the epicenter of the Wenchuan M_w 7.9 earthquake; thus, both of these large earthquakes occurred in the Longmenshan fault system. The Longmenshan fault belt is the major active fault zone at the eastern boundary of the Tibetan Plateau. It is composed mainly of three faults: a back-range fault (Maoxian-Wenchuan fault), a central fault (Yingxiu-Beichuan fault), and a front-hill fault (Pengxian-Guanxian fault, Chen et al., 2007). Because the tectonics of the source region is highly complex and the topography is very steep, this region has a high risk of landslides and devastating damage. Considering that the Lushan earthquake killed about 200 people and injured more than 10 000 people until the end of May 2013 and that a gap exists between the rupture regions of the Wenchuan and Lushan earthquakes, the fault belt is likely capable of generating strong earthquakes in the future (Liu et al., 2013). Therefore, studies of the characteristics of the Lushan earthquake are critically important for emergency rescue operations and seismogenic studies.

A lot of research has been done on the earthquake focal mechanism solution and fault rupture process (Xu et al., 2016; Yang et al., 2016; Zhang et al., 2014, 2013; Liu et al., 2013; Wang et al., 2013; Xie et al., 2013; Zeng et al., 2013). Compared with other source parameters, the rupture process is of greatest importance for emergency rescue operations because it is one of the most important factors for estimating strong ground shaking (peak ground velocity (PGV) and peak ground acceleration (PGA)) near the field rupture ( Chen et al., 2010), calculating the Coulomb stress change (Shan et al., 2013, 2009), and computing co- and post-seismic deformation (Wang et al., 2013; Liu et al., 2011). Thus, many rupture process models, including those of Liu et al. (2013), Wang et al. (2013), Zhang et al.(2014, 2013), and Hao et al.(2013), were built soon after the occurrence of the Lushan earthquake. All of these models show that the Lushan earthquake was dominated by a thrust component and that most slip occurred around the hypocenter, where the peak slip was about 1.5 m. Most of the rupture slips occurred within the first 10 s. Most of the seismic energy was released near the hypocenter, with a length of 28 km, especially on both sides of the hypocenter within a range of 20 km; the seismic energy released was relatively smaller in other areas. In general, the rupture occurred at substantial depth, and weak or even no rupturing occurred at the surface. These results have been proven and were important for the subsequent emergency rescue work and field surveys.

However, all of these models were built by using unique teleseismic data or by combining strong motion records and teleseismic data. As is well known, because of the low frequency filtering effect caused by long-range paths, teleseismic data cannot well resolve the detailed rupture image, which results in relatively blurred rupture models and a relatively lower reliability of results. Combining near-field strongmotion data and teleseismic data could improve the resolution of the rupture model. However, relatively unevenly distributed strong motion data and the baseline shift caused by integrating the acceleration data to velocity weaken the reliability of the inversed rupture model. In order to solve these problems, we collected strong motion data and static GPS data to constrain the detailed rupture process. The purpose of this work was to obtain a more precise rupture model to better understand the mechanics of the seismogenic fault and also to provide model reference for further studies on stress change and estimated ground shaking and for assessing future seismic activity given the impact of the Lushan M_s 7.0 earthquake.

1. METHODOLOGY AND DATA

The geometry of the rupture model is quite important for its reliability. Because the Lushan earthquake was relatively small and there is little evidence showing that the earthquake may have rupturedalong a curved fault or on more than one fault, we assume a rectangular fault, as indicated in Fig. 1. Based on the locations of aftershocks provided by the China Earthquake Network Center and the location of the Longmenshan active faults system, combined with the current preliminary focal mechanism solution (Xie et al., 2013) and aftershocks distribution(Zhao et al., 2013), we determined a rupture model with some perturbations to better fit the data. The slip model consists of a single rupture plane with a spatial scale of 66.5 km along strike and 35 km down dip and with strike and dip angles of 214° and 38°, respectively.

Figure  1.  The datasets used in this study. The epicenter of the Lushan earthquake is represented by the beach ball, the locations of the seven near-field strong motion stations used in this study are represented with black triangles, and the locations of the GPS stations are shown with white triangles. The inset shows the locations of the teleseismic P-waveform stations used in this study.

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In order to better obtain the rupture process, we divided the fault plane into 190 subfaults with a spatial scale of 3.5 km×3.5 km. On each subfault, we simultaneously inverted for slip, rake, rise time, and average rupture velocity using a simulated annealing algorithm (Ji et al., 2002). The rupture was set to initiate at the relocated epicenter (Zhao et al., 2013; 30.284°N, 102.956°E) at a depth of 15 km. The geological structure is complicated in Longmenshan region, with crustal velocity and thickness variations in horizontal directions, which may cause some deviations in the rupture process inversion. So an accurate velocity model is required for improving the inversion accuracy. We build combined model with two unique crust models (Liu et al., 2009; Zhu et al., 2008; Wang et al., 2003; Zhao et al., 1997) to simulate the structure model in the source region (Fig. 2). During the inversion, the slip value for each subfault was allowed to vary from 0 to 3 m, and the angle was searched in the range from 61° to 121°, with a search step of 2°. The average rupture velocity was allowed to vary from 0.5 to 3.5 km/s. In addition, the rise time in the inversion model was allowed t o vary from 0.6 to 3.6 s.

Figure  2.  Crustal velocity models used in the rupture process inversion.

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The data used in inverting the M_s 7.0 Lushan earthquake contained six acceleration records recorded at seven CSMN stations with epicentral distances within 60 km (Fig. 1), and the accelerograms were bandpass filtered between 0.02 and 0.5 Hz. Twenty-two teleseismic P waveforms with good signal-to-noise ratio and good azimuthal distribution (Fig. 1) were also applied in the inversion, the instrument responses were deconvolved from the original recordings to obtain ground velocities to improve the spatiotemporal resolution of the inversion (Wald et al., 1996), and the seismograms were band-pass filtered with a frequency band of 0.002-1 Hz. Compared with seismograms, static displacements have been proven particularly useful for defining the slip distribution for large complex ruptures, such as the M_w 7.2 El Mayor-Cucapah event(Wei et al., 2011). Since GPS stations had been deployed before the Lushan earthquake and some of these GPS data have been recently compiled by Jiang et al. (2013) and Wu et al. (2013), static offset data were also collected to determine the finalized rupture model. In this work, 12 static GPS data were used during the inversion. The distributions of the strong motion stations, teleseismic stations, and static GPS stations are shown in Fig. 1.

2. INVERSION RESULTS

In seismology, checkerboard tests can provide a direct visualization of the resolution of inversion by using different datasets. Here we conducted checkerboard tests to investigate the resolution of each type of dataset and to explore the potential advantage of joint inversion. Based on the testing results (Fig. 3), we found that each individual dataset only provides limited resolution of inverted slip models and no single dataset can resolve the input model well. Compared with the inversion using a single kind of dataset, the joint inversion can resolve the rupture process much better, overcome the limitations of separate inversions, and ideally achieve good resolution across the entire fault model; both the slip pattern and the slip amplitude are significantly improved.

Figure  3.  Checker board tests for the model resolution. 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 (a). The inversion results using only the teleseismic data (b), strong motion data (d), static GPS (d), strong motion and teleseismic (e) and combined all above datasets (f). The star indicate the epicenter of the main shock, the contours display rupture initiation time in seconds.

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Based on the source parameters and datasets described in Section 2, we inversed the rupture process of the Lushan M_s 7.0 earthquake, which was constrained by joint inversion with strong motion data, static GPS, and teleseismic P waveforms. The inverted slip model is shown in Fig. 4a, and the smoothed rise times and rupture times are shown in Fig. 4b. The comprehensive research result shows that this earthquake occurred in the Longmenshan fault system with a thrust-fault mechanism. The Lushan earthquake was basically a pure thrust event. The rupture initiated at a depth of 15 km, and the major rupture slips occurred around the hypocenter with a peak value of about ~1.5 m. The total seismic moment of the whole fault was 1.01×10 ¹⁹ Nm, equivalent to a magnitude M_w 6.6. Most of the seismic moment occurred within the first 8 s, which is our estimate of rupture duration. The average rupture velocity was about 2 km/s.

Figure  4.  (a) Slip model for the Lushan M_s 7.0 earthquake determined by joint inversion. The direction of the fault plane is indicated by the large black arrow, and the star indicates the hypocenter. White arrows display the slip directions. (b) Rise-time distributions on the fault. Contours display rupture initiation time in seconds.

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The inverted slip model could explain most of the observed coseismic displacements (Fig. 5), especially the vertical displacements. However, three stations (QLAI, LS01, and LS04) located in the footwall had relatively large misfits. As for the strong motion records (Fig. 6), most of the waveforms were well matched in both amplitude and phase, but large mismatches were observed for the strong motion stations (PJD and YAM) within the Sichuan Basin, particularly in the PJD records. These misfits are presumably due to 3-D basin effects that cannot be appropriately modeled using a 1-D crustal velocity model. Figure 7 shows a comparison between the observed teleseismic records and synthetic seismograms predicted by the slip model. The predicted waveforms are quite similar to the observations in the direct P-wave segments, but mismatches for the tail of the waveforms still exist, especially for the high frequency components. These mismatches maybe caused by complicated structures, heterogeneity within the earth.

Figure  5.  Comparison between observed horizontal displacements (a), vertical displacements (b), and those predicted from our best-fitting model. The black arrows are GPS data, and the white arrows are synthetics.

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Figure  6.  Comparison of velocity records for three components (black) and synthetic seismograms (red) predicted by the joint inversion. The station names are indicated at the right of the traces. The peak amplitudes of the data in cm/s are indicated above the ends of the traces.

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Figure  7.  Comparison of teleseismic velocity records in black and synthetic seismograms predicted by the slip model in red. The seismograms are band-pass filtered with a frequency band of 0.002~1 Hz. Both the observed data and synthetic seismograms are aligned with the P and SH arrivals. The number at the end of each trace is the peak velocity of the data in micrometers per second, and it is used to normalize both records and synthetics. The azimuth and distance in degrees are shown at the beginning of each record with the azimuth on top.

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In addition, because the slip occurred mainly in a relatively compact area (Fig. 4a), it can be estimated using the formula of Kanamori and Anderson (1975) assuming a circular slip asperity. Based on our joint inversion model, we estimated the stress drop to be about 1.8 MPa, which is relatively lower than that of global studies that show that interplate earthquake stress drops have a median value of around 3.3 MPa (Allmannand Shearer, 2009; Kanamori and Anderson, 1975).

3. DISCUSSION AND CONCLUSION

Based on the tectonic environment of the Longmenshan fault system and the focal mechanism inverted by Xie et al. (2013), we obtained the rupture process of the Lushan earthquake by combining strong motion data, static GPS, and teleseismic P waveforms. Compared with our previous model, this joint inversion model is closer to field surveys (Liu et al., 2014) and can provide more detailed model information about the rupture process.

The joint inversion showed that the main rupture asperity was dominated by a pure thrust component, which was located around the hypocenter within a range of ~20 km. The peak slip amplitude was about 1.5 m, and the moment magnitude was M_w 6.6. The rupture focus was at a depth of ~15 km, and most of the moment was released during the first 8 s after onset of the rupture (Fig. 8). Based on the rupture model, there was no significant rupture near the surface, which is consistent with the phenomenon that the earthquake-affected area was larger than the main rupture area itself. Recent field geological surveys could not find obvious rupture failures in the rupture zones, which provide additional strong evidence supporting the reliability of our rupture model. Moreover, our rupture model was generally consistent with the distribution pattern of aftershocks ( Fig. 8). Hence, considering the distribution of aftershocks and the properties of the Longmenshan fault system, we speculate that this earthquake probably occurred on the outer-hill fault of the Longmenshan fault system.

Figure  8.  Surface projection of the inverted slip distribution. The black circles indicate aftershocks of the Lushan M_s 7.0 earthquake. The upper left inset shows the moment-rate function of this earthquake.

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From the slip model of the Lushan earthquake, most of the seismic energy was released near the hypocenter, with a length of 20 km, and especially on both sides of the hypocenter. Thus, we speculate that the strain has not yet been completely released. The abundant aftershock sequence and the relatively large aftershock events provide proof of this point. In the near future, we should pay particular attention to the aftershocks and the long-term strain rate in the rupture fault and determine whether the aftershocks and post-seismic movements, such as after-slip effects and viscoelastic relaxation, have released the majority of the accumulated strain on the gap between the asperities. If this has not occurred, there would be a large potential for strong aftershocks in these areas.

To date, a number of slip models have been derived for this event by using teleseismic data (Liu et al., 2013; Wang et al., 2013; Zhang et al., 2013) and joint inversion of multiple datasets(Zhang et al., 2014; Hao et al., 2013). We found from these studies that, even for the same earthquake, the results of the inversions by different studies vary because of the influence of some uncertain factors, such as the time windows that define the source-time function, the assumed strike and dip of the fault, the velocity structure, and noise (Zhou et al., 2004). Although some models have been obtained by joint inversion of strong motion and teleseismic data, but viewing from the checkerboard tests (Fig. 3), Although the resolution of combing the strong motion and teleseismic data is much better, especially for the slip pattern, the slip amplitude is not well recovered (Fig. 3e). Actually, real improvement in inversion resolution may come from directly combining all available data, particularly seismic and geodetic data as the combination can offer a more broadband frequency range of observations than individual seismic datasets (Ji et al., 2001). Additionally, static data (the static component of the seismic data or GPS geodetic data) are particularly helpful for reducing the trade-off between timing and slip distribution. In this study, we added substantial static GPS displacement data during the joint inversion, which will help to provide a more reliable and complete view of the rupture process of the Lushan earthquake, the checkerboard tests are further demonstrate the importance of adding static GPS data in the final inversion.

In addition, the Wenchuan M_w 7.9 earthquake ruptured the Longmenshan fault system, and the rupture extended to the outer-hill fault of the Longmenshan fault system. Considering the slip model of Shen et al. (2009) and the aftershocks distribution of the Wenchuan earthquake (Lü et al., 2012; Zheng et al., 2009; Huang et al., 2008), an area lacking earthquakes with a length of about 50 km exists between the Wenchuan earthquake and this earthquake (Liu et al., 2013; Gao et al., 2013; Zheng et al., 2013). For this reason, detailed information about the accumulated strain on the rupture in this area is needed for evaluating the risk of strong earthquake. Although an M_s 6.2 earthquake occurred in this area decades ago, this earthquake was too small to have released the total strain in this area. Aside from earthquakes, aftershocks and post-seismic relaxation also play important roles in the accumulation of strain. Based on the distributions of the aftershock sequences of the Wenchuan and Lushan earthquakes, very few aftershocks occurred in this area. Thus, the most important work at present is to monitor post-seismic relaxations. If significant a seismic creep has occurred, such as in the case of the Sumatra earthquake (Subarya et al., 2006), the risk of strong earthquake in the future is low. If not, we should pay careful attention to the potential strong earthquake hazard in this area.