Earthq Sci (2019)32: 197–206 197 doi: 10.29382/eqs-2019-0197-02

The shallow crustal S-velocity structure of the Longmenshan fault zone using ambient noise tomography of a seismic dense array*

Dandan Li1,2 Gaochun Wang1,2 Ruihua Lin3 Kai Deng4,* Xiaobo Tian1,5,*

1 State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Sinosteel Tianjin Geological Academy LTD, Tianjin 300181, China 4 College of Geophysics, Chengdu University of Technology, Chengdu 610059, China 5 CAS Center for Excellence in Earth Sciences, Beijing 100101, China

Abstract The Longmenshan fault zone (LMSF), shallow velocity structure for detailed studies of the characterized by complex structures and strong seismicity, is Longmenshan fault zone. located at the junction between the eastern margin of the Tibetan Plateau and the north-western basin. Since Keywords: Longmenshan fault zone; ambient noise tomography; the Wenchuan earthquake on May 12, 2008, abundant studies S-wave velocity structure; short-period dense seis- of the formation mechanism of earthquakes along the LMSF mic arrays were performed. In this study, a short-period dense seismic array deployed across the LMSF was applied by ambient noise tomography. Fifty-two 3-D seismic instruments were used for data acquisition for 26 days. We calculated the 1 Introduction empirical Green's functions (EGFs) between different station- pairs and extracted 776 Rayleigh-wave dispersion curves Located at the junction of the Sichuan foreland basin between 2 and 7 s. And then, we used the direct-inversion and the Songpan-Garzê block, the Longmenshan fault zone method to obtain the fine shallow crustal S-wave velocity structure within 6 km depth in the middle section of the is formed by the strong eastward compression from the Longmenshan fault zone and nearby areas. Our results show Tibetan Plateau and the blocking of that the sedimentary layer (>5 km) exists in the northwest (Figure 1) (Burchfiel et al., 1995, 2008; Jia et al., 2006; margin of Sichuan Basin with a low S-wave velocity Hubbard and Shaw, 2009). Characterized by complex (~1.5−2.5 km/s) which is much thicker than that beneath the structures and strong seismicity, the huge Longmenshan Longmenshan fault zone and the Songpan-Garzê block. The fault zone is composed of three major faults (F1: high-velocity structures with clear boundaries below the Wenchuan-Maoxian Fault; F2: -Beichuan Fault; middle of Longmenshan fault zone (~2−4 km) and the F3: Hanwang-Anxian Fault) (Figure 1). Since the 21st cen- Songpan-Garzê block (~4.5−6 km) probably reveal the NW- tury, two strong earthquakes have occurred on the Long- SE distribution patterns of both the Pengguan complex and menshan fault zone, causing terrible casualities and eco- the high-density belt hidden in the northwest of the Pengguan nomic losses. The Wenchuan earthquake (M 8.0) in 2008 complex. And the obviously high-velocity anomalies S observed at the depth of ~1−2 km in the southeastern margin was caused mainly by thrust motion with stike-slip along of the Songpan-Garzê block can be considered as the F1 and F2 (Wang et al., 2008). And occurring at the F3 on Laojungou granites. Our results provide a high-resolution the southwest end of Longmenshan fault zone, the Lushan

earthquake (MW6.7) in 2013 was a thrust event (Lin et al., * Received 16 January 2020; accepted in revised form 3 April 2020; 2013). Therefore, it is urgent to utilize various geophysical published 16 May 2020. methods to study the underground structure of the * Corresponding author. e-mail: [email protected], txb@mail. Longmenshan fault zone. iggcas.ac.cn © The Seismological Society of China and Institute of Geophysics, In recent years, a series of work was carried out to China Earthquake Administration 2019 study the internal crustal structures of the Longmenshan

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101°E 102° 103° 104° 105° 33°N Hongyuan Seismic station Thrust fault epicenter

Songpan-Garzê MJF terrane LRBF zone

HYF L0

32° Beichuan

MEKF 6 MYLF LJG Wenchuan 4 XLB L0 PG 2 Wenchuan

31° Elevation (km) Danba earthquake LMSF zone 0

Chengdu Qaidam F 2 Lushan F1 F earthquake 3 LQSF 30° Tibet Sichuan Basin SB

Figure 1 Tectonic setting and distribution of seismic stations in the Longmenshan fault zone. LQSF: Longquanshan fault zone; IMSF zone: Longmenshan fault zone; F1: Wenchuan-Maoxian fault; F2: Yingxiu-Beichuan fault; F3: Hanwang-Anxian fault; MEKF: Barkam fault; HYF: Huya fault; LRBF zone: Longriba fault zone; MJF: Minjiang fault; LJG: Laojungou granite (Mesozoic granite); XLB: Xuelongbao complex (Neoproterozoic metamorphic complex); PG: Pengguan complex (Neoproterozoic metamorphic complex); SB: Sichuan Basin; Red dashed line L0: A profile line; Blue triangle: Seismic station. The red square is study area fault zone and the adjacent areas, including seismic travel the Longmenshan fault zone near the Songpan-Garzê area time tomography (Lei et al., 2009; Wu et al., 2009; Li et is characterized by low and medium wave velocity while al., 2011; Deng et al., 2014), S-wave velocity structure high velocity below the Sichuan Basin, and the weak zone inversion (Li et al., 2009a; Liu et al., 2014), deep seismic thickening towards the Sichuan Basin in the deep crust was reflection profile (Li et al., 2009b; Guo et al., 2013), wide- interpreted as a channel flow (Li et al., 2009a; Liu et al., angle reflection/refraction seismic profile (Jia et al., 2014; 2014). It’s worth noting that most of these studies Zhang et al., 2017), gravity and magnetic modeling (Tian concentrated on large-scale regional problems. et al., 2017; Xue et al., 2017), electrical detection (Zhao et Due to the rich information of the underground media al., 2012; Wang et al., 2014b), drilling exploration (Wang contained in the seismic ambient noise, researchers have et al., 2016), etc. The results show that seismic wave proposed seismic ambient noise imaging technique for 3D velocity in the shallow layer of the Sichuan Basin is velocity structure imaging (Claerbout, 1968; Michel and significantly lower than those in the Longmenshan fault Anne, 2003; Roux et al., 2005; Shapiro et al., 2005; Yao et zone and Songpan-Garzê block (Jia et al., 2014; Liu et al., al., 2006). This imaging method is not limited by seismic 2018). And the thrust strongly uplifted the upper crust and sources with the advantages of low requirements, high crystalline basement under the central fault system of efficiency and high imaging resolution. Nowadays, it has Longmenshan (Guo et al., 2013; Jia et al., 2014; Zhang et been widely applied to revealing the fine velocity structure al., 2017). The seismic wave velocity structure also at the junction of Songpan-Garzê block and Sichuan Basin, revealed that the high-velocity anomalous zone in the which plays an important role in studying the mechanism upper and middle crust of Longmenshan blocked the low- of earthquakes in Longmenshan fault zone (Li et al., 2010; velocity material come from the interior of the plateau, Chen et al., 2015). However, the previous researches also which leads to strain accumulation and controls the focused more on the deep crust and mantle velocity direction of earthquake occurrence and rupture propaga- structures, but less on the shallow fine velocity structure tion (Lei et al., 2009; Wu et al., 2009; Li et al., 2011; Deng with high imaging spatial resolution. In recent years, short et al., 2014; Li et al., 2019). The middle and lower crust of period ambient noise tomography (~1 s, station spacing

Earthq Sci (2019)32: 197–206 199 less than 10 km) has been proposed with the improvement We calculated the cross-correlation functions (CFs) of of seismic instruments and gradually applied to high- the every hour's background noise data of different station resolution shallow velocity structure imaging, which pairs and superposed the CFs. The periods of CFs are proved that ambient noise tomography can be developed 0.5 to 10 s, and the length is 100 s. The cross-correlation from large-scale research (hundreds of kilometers) to calculation results are shown in Figure 2.

small-scale research (hundreds of meters) (Lin et al., 2010; 150 Hannemann et al., 2014; Wang et al., 2018). The purpose of our study is to obtain the fine velocity structure of the middle section in the Longmenshan fault zone and the adjacent areas. The research steps include background noise data pre-processing, cross-correlation calculation, phase velocity dispersion curves extraction of 100 Rayleigh wave, and S-wave velocity structure inversion (Bensen et al., 2007). In the inversion process, the direct inversion method of surface-wave dispersion was used to

minimize the influence of complex terrain (Fang et al., Distance (km) 2015). 50 2 Data and methods

2.1 Station distribution and data collection

In our study, the data were recorded by an array of 52 0 0 50 100 EPS-2 instruments deployed across Longmenshan, with a −100 −50 t (s) runtime of up to 26 days (November 1 to November 26, 2017). The sampling frequency of the EPS-2 instrument is Figure 2 0.5−10 s interstation cross-correlation functions 100 Hz, and the frequency band width is 5 s−200 Hz. The 3) The extraction of Rayleigh-wave phase velocity array is almost perpendicular to the Longmenshan Range dispersions and extends from the Songpan-Garzê block to the edge of The empirical Green's functions (EGFs) of the medium the Sichuan Basin (Figure 1). The distance between between stations can be calculated from the first-order stations is about 2 km, and the total length of the array is time derivative of the CFs. In this study, the phase velocity about 150 km. dispersion curve was measured by using image transformation technique (Yao et al., 2005). 2.2 Ambient noise data processing The study area crosses Longmenshan fault zone where the terrain is very complex, especially the elevation chan- 1) Data processing ges greatly. Therefore, in order to minimize the impact of In this study, ambient noise data collected needs a elevation, we selected station pairs with a spacing greater series of preprocessing before cross-correlation calcula- than 6 times of the elevation when picking up the phase tions. We truncated the continuous data of the vertical velocity dispersion curves of Rayleigh wave in the case of component to 1 hour per segment, remove the instrument meeting the far-field conditions (cAB·T = λ ≥ ∆/3, where T response, eliminate the trend and the mean, and resampled is the period, λ is the wavelength, and ∆ is the mesa pitch). to 50 Hz. Then the noise data were filtered to a bandpass And the signal-noise ration (SNR) is set to SNR > 5, so as range of 0.1−2 Hz with spectral whitening and temporal to ensure the reliability of measurement. one-bit normalization (Bensen et al., 2007). After sorting all the extracted dispersion curves and 2) Cross-correlation function calculating excluding the dispersion curves with poor quality, a total Assuming that v (t) and v (t) represent the seismic A B of 776 phase velocity dispersion curves were picked up ambient noise data recorded by two stations A and B, the with a period of 2−8 s, in which the minimum phase cross-correlation function between the two stations A and velocity is about 2 km/s and the maximum is about B can be written as equation (1). t0 is the length of time for 3.5 km/s (Figure 3a). The ray path numbers of Rayleigh the cross-correlation calculation. w waves in different periods are shown in Figure 3b. Since t0 the distance between the pairs of stations limits the ray cAB(t) = (vA(τ)vBt + τ)dτ. (1) 0 coverage for long periods, only dispersion curves in the

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(a) (b) 5.0 1000

4.5 800 4.0

3.5 600

3.0 400

2.5 Number of paths Phase velocity (km/s) 200 2.0

1.5 0 23456789 2345678 Peroid (s) Period (s) Figure 3 Phase velocity dispersion curves in the 2–8 s period band (a) and number of the ray paths of Rayleigh waves at different periods (b) period of 2−7 s are used in this study in order to minimize resolution of the direct inversion of phase velocity the impact of the uneven data distribution. dispersion. The principle is that the theoretical travel time According to the ray paths distribution of Rayleigh of each ray path at different periods is calculated according wave phase velocity (Figure 4), it can be observed that the to the actual ray distribution which is based on the addition rays coverage in the 2−7 s period is sufficient, and the of positive and negative velocity anomalies to the initial entire coverage area of the ray crosses the Longmenshan velocity model, and then a certain random error is added to fault with showing a narrow-band shape. the theoretical travel time to invert and the inversion results are compared with the original velocity model. 3 Direct inversion of Rayleigh-wave In the checkerboard testing, the study area was phase velocity dispersions discretized into a 0.03°×0.03° grid, and the sampling interval of the Rayleigh-wave dispersion curves was set to 3.1 Resolution testing of the direct inversion 0.1 s. The depth of the initial velocity model ranges from 0 to 7 km with a 0.3 km/s velocity increase per kilometer 1) Checkerboard testing and the velocity is 1.5 km/s at 0 km. For the initial velocity Checkerboard testing was used to detect the lateral values of different depths, a velocity disturbance of

102°30′E 103°00′ 103°30′ 104°00′ 102°30′E 103°00′ 103°30′ 104°00′ 102°30′E 103°00′ 103°30′ 104°00′ (a) 2 s (b) 3 s (c) 4 s 32°00′N

31°30′

31°00′ 6

30°30′ 4

2 (d) 5 s (e) 6 s (f) 7 s 32°00′N Elevation (km)

0

31°30′

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30°30′ Figure 4 Different ray paths corresponding to different phase velocity periods (2−7 s)

Earthq Sci (2019)32: 197–206 201 sinusaidal fitribution is added, and the maximum absolute that is, the velocity anomalies with a thickness of at least value is 0.2 km/s (Figure 5a). According to the recovered 1.5 km and above can be distinguished well in this study. results (Figures 5b−5h), it can be seen that the imaging And no matter in the margin of the model with sparse rays resolution is usually better in the areas covered by denser or in the center of the model with dense rays, the model ray paths. And the theoretical velocity anomaly can be testing results both show that the anomalies are recovered recovered better at the depth of 1–6 km than at other depth. well. In addition, the checkerboard testing results show that the reliable area for ambient noise imaging is narrow and 3.2 Direct inversion for 3D vS structure perpendicular to the Longmenshan fault zone, because the We use the direct inversion method of surface wave stations are almost distributed on a line perpendicular to dispersion for three-dimensional shallow crustal structure. the mountain range. 2) Model testing Based on using frequency-dependent ray tracing and a In order to detect the longitudinal resolution of the wavelet-based sparsity-constrained tomographic technique, direct inversion method, a model test method was used in this method omits the construction of 2-D phase (or group) this study. The input models are plate-shaped anomalies in velocity maps and avoids the assumption of great-circle the L0 profile with a velocity anomaly value is 20% as propagation (Fang et al., 2015). shown in Figures 6a and 6c. The inversion results in The study area (102.5° E−104° E, 30.5° N−32° N) was Figures 6b and 6d both show that the method can better divided into 53×53 grids with a grid spacing of 0.03°× reconstruct the velocity anomaly with thickness of ~1.5 km, 0.03° in the horizontal direction to make sure that all the

102°30′E 103°00′ 103°30′ 104°00′ 102°30′E 103°00′ 103°30′ 104°00′ 102°30′E 103°00′ 103°30′ 104°00′ (a) (b) (c) 32°00′N

31°30′

31°00′

1 km 1 km 2 km 30°30′

(d) (e) (f) 32°00′N

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31°00′

3 km 4 km 5 km 30°30′

(g) (h) 32°00′N

0.2

31°30′ 0.1

0

31°00′ −0.1 S velocity anomaly (km/s) −0.2 6 km 6.5 km 30°30′ Figure 5 The results of checkerboard testing. (a) The input modle; (b−h) The output models at depths of 1, 2, 3, 4, 5, 6 and 6.5 km, respectively

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0 0 (a) (b) 1 1 2 2 3 3 4 4

Depth (km) 5 Depth (km) 5 6 6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 Distance (km) Distance (km) 0 0 (c) (d) 1 1 2 2 3 3 4 4

Depth (km) 5 Depth (km) 5 6 6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 Distance (km) Distance (km)

−20% −10% 0 10% 20% S wave velocity anomaly Figure 6 Model testing of the L0 profile shown in Figure 1. (a) and (c) show the input models at the middle and edge of the L0 profile respectively; (b) and (d) show the output models of model testing corresponding to the input models (a) and (c) sources and receivers are in the region, and 24 layers in the direction. depth direction with intervals ranging from 0.2 to 0.5 km. The initial velocity model is consistent with the initial 4 S-wave velocity structure and velocity model tested by the checkerboard. discussion According to the results of checkerboard testing, we set reasonable parameters in the direct inversion programs Using the method of short period ambient noise (Fang et al., 2015). The maximum number of iterations, tomography, we obtained the v structure images in the the balancing parameter between data fitting term and S depth range of 0−6 km in the middle section of smoothing regularization term (‘weight’), the input Longmenshan fault zone. The horizontal resolution of the parameter for LSQR (‘damp’) and the sparsity fraction imaging results is about 10 km, and the vertical resolution parameter were set to 10, 4.0, 0.1 and 0.2, respectively. In is about 1.5 km. the inversion process, an iteratively reweighted least As it is shown in the Figures 7a−7f, the S-wave squares algorithm is used to estimate the wavelet velocity within 0−6 km depth is significantly beneath the coefficients of the velocity model, and the new velocity Sichuan Basin lower than that beneath the Longmenshan model is used to update the surface wave ray paths and fault zone and the Songpan-Garzê block. And as the depth data sensitivity matrix in each iteration. In our iterative, the increases, the clear boundary of the low-velocity structure standard deviation of the travel time residuals is reduced gradually approaches F1 from F3 (Figure 1). Geological from 1.06 to 0.63 s, and the average residual of the final exploration data, borehole logging data, and seismic velocity model is 0.0032 s. Finally, based on the dispersion reflection profiles all show that the Sichuan Basin has been curves of surface wave phase velocity in 2−7 s with an in a stable sedimentary environment since the Late interval of 0.1 s, we obtained the 3D shear wave velocity Paleozoic so that it covered by >10 km Sinian to structure under the middle section of Longmenshan fault Quaternary sedimentary deposits (Burchfiel et al., 1995; zone by using the direct inversion method. And we Jia et al., 2006; Wang et al., 2014a). To the east of the assumed that the depth at each station is 0 km. Figure 7 Longmenshan fault zone, the sedimentary thickness of the shows the S-wave velocity slices at different depths (1, 2, Sichuan foreland basin is also greater than 4 km (Li et al., 3, 4, 5, 6 km). we can clearly distinguish that the high- 2003; Jia et al., 2006), so we interpret the shallow low- velocity anomalies correspond to Longmenshan fault zone velocity structure below the Sichuan Basin in our results as and Songpan-Garzê block, while the low-velocity thick sedimentary layers. anomalies mainly correspond to Sichuan Basin at every However, obvious high-velocity structures can be depth. The inversion results along the L0 profile (Figure 8) observed in Longmenshan fault zone and Songpan-Garzê clearly shows the distribution of anomalies in the vertical area (Figures 7a−f). The high-velocity structure below

Earthq Sci (2019)32: 197–206 203

102.5°E 103.0° 103.5° 104.0° 102.5°E 103.0° 103.5° 104.0° 102.5°E 103.0° 103.5° 104.0°

(a) (b) (c) 32.0°′N

LJG LJG LJG 31.5°

XLB XLB XLB

PG PG PG

31.0° 4.0

3.5

1 km 2 km 3 km 30.5° 3.0

32.0 2.5 (d) (e) (f) ′N S velocity (km/s) 2.0 LJG LJG LJG 1.5 31.5° XLB XLB XLB

PG PG PG

31.0°

4 km 5 km 6 km 30.5° Figure 7 Shallow shear wave velocity structure slices at different depths of 1, 2, 3, 4, 5, 6 km across the middle of LMSZ and the nearby areas

Longmenshan fault zone may be closely related to the low S-wave velocity structure (Jia et al., 2006; Li et al., Pengguan complex body (Neoproterozoic granite) where 2009a; Chen et al., 2015). The high-velocity structure the Wenchuan earthquake occurred. Taken the WFSD-2 below the Longmenshan fault zone thrusts over the drill hole located on the Yingxiu-Beichuan fault as the Sichuan Basin, which is consistent with the research object, the core can be divided into six segments including results of the wide-angle reflection/refraction seismic three sections of the Pengguan complex which is the profile. Originated from the deep crust, the Proterozoic product of melt-mixing of mantle-derived magma with granite (Pengguan complex) thrusts from the deep to the underplating and lower crust material (0−599.31 m, shallow, making the surface P-wave velocity up to 6 km/s 1211.49−1679.51 m, 1715.48−2081.47 m) (Zhang et al., (Jia et al., 2014; Zhang et al., 2017). And the 2012; Lu et al., 2014). The strong compression between comprehensive results of gravity profile, seismic profile the Tibetan Plateau and the Sichuan Basin caused the high- and other geological studies indicate that there are high- pressure rock mass in the lower crust to migrate to the density anomalies with the same thrusted/imbricated shallow layer and become an isolated Pengguan complex pattern as the Pengguan complex under the Songpan-Garzê with high density (2.75 g/cm3), whose boundary fold belt near the middle part of Longmenshan. And it is corresponds to the boundary of velocity change (Burchfiel considered to be equivalent to the Pengguan complex et al., 2008; Pei et al., 2014; Airaghi et al., 2017). The covered by Paleozoic sediments, which is produced by high-velocity anomalies (Figure 7a−7d) beneath the continuous crustal shortening and basement extrusion Songpan-Garzê fold belt may be attributed to the Mesozoic (Roger et al., 2004; Guo et al., 2013; De Sigoyer et al., granite intrusions such as the Laojungu rock bodies mainly 2014; Xue et al., 2017). Accordingly, the high-velocity composed of diorite granite and monzogranite produced in anomaly ~4.5−6 km beneath the Songpan-Garzê fold belt the late Indosinian and Yanshanian periods (Hu et al., indicates the shallow location of the hidden high-density 2005; Zhao et al., 2007; Yuan et al., 2010; Xue et al., body in the northwest of the Pengguan complex. There is 2017). also a high-velocity anomaly (~1−2 km) below the According to the distribution of the stations, the S- Songpan-Garzê block, which is very close to the location wave velocity structure of the L0 profile across the of the Laojungou granite (LJG in Figure 1). Previous Longmenshan fault zone was obtained (Figure 8) to reveal studies revealed that the Mesozoic granites such as the vertical characters. At a depth of 0−6 km below the Laojungou granites are widely intruded into the lower- Sichuan foreland basin, sedimentary cover results in the velocity Triassic turbidite deposits in the Songpan-Garzê

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SGT LMS SB 5 4 F1 3

2 F2 F3 1 Elevation (km) 0 0 3 1

2 3 3 3 4 4 5 Depth below stations (km) 6 0 20 40 60 80 100 120 140 160 Distance (km)

1.5 2.0 2.5 3.0 3.5 4.0 S velocity (km/s) Figure 8 Shear wave velocity structure of L0 profile. SB: Sichuan Basin; LMS: Longmenshan; SGT: Songpai-Garzê Terrane; F1: Wenchuan-Maoxian Fault; F2: Yingxiu-Beichuan Fault; F3: Hanwang-Anxian Fault area which may be caused by the delamination of thic- with clear boundaries corresponds well to the geological kened lithosphere or the underplating of mantle derived conditions in the middle section of the Longmenshan fault material in Songpan-Garzê area (Zhao et al., 2007; Yuan et zone and adjacent areas, providing a certain reference for al., 2010; Jia et al., 2014; Zhang et al., 2017). Therefore, the fine research work in the shallow layer of the area. we speculate that the high-velocity anomalous body may However, the restrictions on the distribution of stations correspond to the Laojungou rock bodies in the Songpan- and the difference in elevation on both sides of the Garzê area. But we cannot describe the specific 3D shapes Longmenshan fault zone partly affects the accuracy of the of the high-velocity anomalies due to the limitation of the inversion, and further improvements on this issue are stations’ distribution. needed in future work.

5 Conclusions Acknowledgments

Using the ambient noise tomography method to This work was supported by the National Key R&D process the noise data recorded by a short-period dense Program of China (No.2016YFC0600301) and the array (52 EPS-2 instruments) across the Longmenshan in National Natural Science Foundation of China November 2017, we obtained the S-wave velocity (No.41974053). Seismic instruments were provided by the structure of 0−6 km below the Longmenshan fault zone Short-period Seismograph Observation Laboratory, and the adjacent area with a vertical resolution of 1−2 km. IGGCAS. And we plotted figures by using the Generic On the east side of the middle part of Longmenshan fault Mapping Tools software (Wessel and Smith, 1998). The zone, the S-wave velocity structure reveals that the authors would like to thank Lianglei Guo, Zhen Liu, Wei sedimentary layer in the Sichuan foreland basin is thicker Li, Guiping Yu, Shitan Nie, Xusong Yang and Peixiao Du than 5 km. The high-velocity structural morphology ~2− for their help and suggestions that greatly improved this 4 km below Longmenshan fault zone describes the thrust paper. We acknowledge Hongjian Fang for providing overthrust of the high-density Pengguan complex clearly. software resources for the direct inversion method. And the high-velocity anomaly ~4.5−6 km below the Songpan-Garzê fold belt proves the existence of an References equivalent of the Pengguan complex. The high-velocity Airaghi L, de Sigoyer J, Lanari P, Guillot S, Vidal O, Monié P, anomaly ~1−2 km below the Songpan-Garzê block Sautter B and Tan XB (2 017) Total exhumation across the probably reflect the distribution of Laojungou granites in Beichuan fault in the Longmen Shan (eastern Tibetan plateau, this area. Our high-resolution S-wave velocity structure China): Constraints from petrology and thermobarometry. J

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