Jiuzhaigou Earthquake Research Articles

Real-time assessment of human loss in disasters based on social media mining and the truth discovery algorithm

The cycle of traditional disaster loss assessment methods is too long because the assessment results need to be passed among multiple issues. Such a long cycle causes relevant departments to be unable to make emergency decisions in a timely manner. This paper builds a dynamic and real-time model for disaster loss assessment based on social media data. This model presents a method for mining quantitative human loss information from social media data and a truth discovery algorithm for handling social media data conflicts. An experiment using the actual data of the Jiuzhaigou earthquake is conducted to verify the model. The results confirm that the method proposed in this study can accelerate the process of reporting traditional human loss information between multiple levels and departments and assess human loss in a real-time and dynamic manner. The findings are important for providing timely assistance to official agencies and the public.

Mapping topo-bathymetry of transparent tufa lakes using UAV-based photogrammetry and RGB imagery

Seasonal or interannual precipitation differences lead to changes in the water level of a tufa lake, while the underwater topography affects its local depth. Therefore, topo-bathymetry is important to study and protect the aquatic environment of tufa lakes. However, traditional field-based topo-bathymetric surveying methods (e.g., sounding rod or sonar) inevitably disturb the fragile lake ecosystem. In recent years, the emerging remote sensing technology of unmanned aerial vehicles (UAV) has provided a cost-effective solution for measuring topo-bathymetry without disturbance. In this paper, taking Spark Lake in Jiuzhaigou, China, as an example, we captured red-green-blue (RGB) images using a fixed-wing UAV and produced a digital elevation model (DEM) prior to the Jiuzhaigou Earthquake using Structure-from-Motion (SfM) photogrammetry. The underwater topography of Spark Lake was obtained by refraction correction and water color inversion based on the DEM and orthophoto, respectively. For refraction correction, a water depth correction model based on Snell's Law was used. For water color inversion, general band ratio models were replaced by a band difference model (blue band - green band). The qualities of the resulting DEMs produced by the two methods were evaluated against the topography of the drained Spark Lake after the earthquake, and the corresponding DEMs of difference (DoD) were also analyzed. The coefficient of determination (R2) and root mean square error (RMSE) are 0.88 and 1.32 m for refraction correction, and 0.86 and 1.37 m for water color inversion, respectively. The results demonstrated the feasibility and effectiveness of applying UAV-acquired RGB imagery and the two optical remote sensing methods to topo-bathymetric mapping of transparent tufa lakes.

Integrated coseismic displacement derived from high-rate GPS and strong-motion seismograph: Application to the 2017 Ms 7.0 Jiuzhaigou Earthquake

Near-field coseismic displacement information is very important for seismological research. High-rate GPS and seismic sensors are mutually advantageous for capturing earthquake-induced coseismic displacements. The combination of them can help to improve the near-field observation results. In this paper, we propose a combined model of fusing GPS and strong-motion records by taking into consideration the corrections of baseline shift, and then apply a multi-rate Kalman filter to analyze the collocated GPS and strong-motion data. The performance of the proposed approach is validated using 1 Hz GPS data and 200 Hz strong-motion data collected within 150 km from the epicenter during the Ms 7.0 Jiuzhaigou earthquake occurred on August 8, 2017 in Sichuan Province, China (33.20°N, 103.82°E). It shows that the GPS displacements based on the Epoch-relative Positioning Approach well reflect the instantaneous motion state of stations during the earthquake, indicating that the maximum instantaneous displacement of SCJZ station is 3.2 cm in north and −2.0 cm in east, and the permanent displacement of SCSP station is −0.6 cm in north. More accurate and reliable broadband displacements less than 1 cm are obtained by the combination model, which provide the full spectrum of the seismic motion. The integrated displacements have more characteristics of strong-motion results in the early stage of the earthquake with less noise, and in the strong shaking period, tightly bound by the GPS displacements, the integrated displacements will not diverge, and thus accurately record the long-period information of seismic waves.

Dynamic background of the 2017 Ms7.0 Jiuzhaigou (China) earthquake

Dynamic background of the 2017 Ms7.0 Jiuzhaigou (China) earthquake

Quantitative Analysis of the Effects of an Earthquake on Rainfall Thresholds for Triggering Debris-Flow Events

The impacts of destructive earthquakes on rainfall thresholds for triggering the debris flows have not yet been well investigated, due to lacks of data. In this study, we have collected the debris-flow records from the Wenchuan, Lushan, and Jiuzhaigou earthquake-affected areas in Sichuan Province, China. By using a meteorological dataset with 3 h and 0.1° resolutions, the dimensionless effective rainfall and rainfall intensity-duration relationships were calculated as the possible thresholds for triggering the debris flows. The pre- and post-seismic thresholds were compared to evaluate the impacts of the various intensities of earthquakes. Our results indicate that the post-quake thresholds are much smaller than the pre-seismic ones. The dimensionless effective rainfall shows the impacts of the Wenchuan, Lushan, and Jiuzhaigou earthquakes to be ca. 26, 27, and 16%, respectively. The Wenchuan earthquake has the most significant effect on lowering the rainfall intensity-duration curve. Rainfall threshold changes related to the moment magnitude and focal depth are discussed as well. Generally, this work may lead to an improved post-quake debris-flow warning strategy especially in sparsely instrumented regions.

Velocity Structure of the Northeastern End of the Bayan Har Block, China, and the Seismogenic Environment of the Jiuzhaigou and Songpan-Pingwu Earthquakes: Inferences from Double-Difference Tomography

ABSTRACT The 2017 Mw 6.5 Jiuzhaigou mainshock hit the northeastern end of the Bayan Har block, which has experienced many historical earthquakes, including the 1976 M 7.2 Songpan-Pingwu earthquake swarm. We used the double-difference tomography method to perform a joint inversion of the seismic source and P-wave velocity (VP) structure of the Jiuzhaigou-Songpan-Pingwu region. The results show significant lateral heterogeneity in the VP in the mid-upper crust. The velocity structure in the shallow crust correlates well with the surface geology. The Jiuzhaigou mainshock and Songpan-Pingwu earthquake swarm both occurred at the boundary between high- and low-VP anomalies. The Songpan-Pingwu earthquake swarm may be related to the eastward flow of low-viscosity material in the mid-lower crust of the Tibetan plateau. Low-viscosity material intrudes into the bedrock when it encounters the rigid Motianling massif, resulting in surface uplift and thrust earthquakes. By contrast, the Jiuzhaigou earthquake is associated with strain energy accumulating at the boundary between high- and low-VP anomalies related to the different movement rates of the low-VP material in the mid-lower crust and the high-VP body in the mid-upper crust. In this case, the high-VP body ruptures with a strike-slip sense to the southeast.

On magnetic disturbances induced by rotation of coil-type magnetometer driven by seismic waves

SUMMARY Rotation of coil-type magnetometers caused by seismic waves can change the magnetic flux of the earth's magnetic field through the coils and thereby give rise to rotation-induced magnetic fields (RIMFs), which have contribution to so-called coseismic magnetic disturbances during earthquake events. In this study, we present a method to numerically calculate the rotational motion and RIMFs excited by a seismic source in a horizontally layered model. Like computing synthetic seismograms, this method allows calculating the full response of magnetic disturbances during an earthquake event. Numerical examples are conducted to investigate characters of the magnetic fields under excitation of different kinds of sources. The results show that the P-SV-mode waves mainly contribute to radial and vertical magnetic disturbances, while the SH-mode waves dominate tangential magnetic disturbances. We also apply this method to the 2017 Jiuzhaigou earthquake. The synthetic magnetic signals explain well the observed data, suggesting that the coseismic magnetic fields observed during this earthquake are mainly caused by the rotation of the magnetometers.

Dam-break dynamics at Huohua Lake following the 2017 Mw 6.5 Jiuzhaigou earthquake in Sichuan, China

Jiuzhaigou County in Sichuan, China is renowned for its beautiful natural scenery, recognized as a World Heritage Site and World Biosphere Reserve by the UNESCO. On August 8, 2017, a Ms 7.0 (Mw 6.5) earthquake struck the region, causing severe damage and casualties. The earthquake triggered thousands of landslides and the collapse of the natural travertine dam impounding Huohua Lake, an important tourist attraction in the reserve. In this study, the dam failure process was investigated through field surveys and remote sensing observations. The propagation of the flood surge was analyzed by Massflow, a depth-integrated continuum model based on the MacCormack-TVD finite difference algorithm. The model results were validated against vegetation maps evaluated from satellite multispectral data recorded before and after the event. The relation between changes in vegetation coverage and computed flooded areas, flow depth, and velocities was elucidated. This study is preliminary in nature, yet the proposed approach could be used, after further validation, in flood hazard and vegetation damage assessments, which may be especially valuable in nature reserves.

Probing the Fault Complexity of the 2017 Ms 7.0 Jiuzhaigou Earthquake Based on the InSAR Data

On 8 August 2017, a surface wave magnitude (Ms) 7.0 earthquake occurred at the buried faults extending to the north of the Huya fault. Based on the coseismic deformation field obtained from interferometric synthetic aperture radar (InSAR) data and a series of finite fault model tests, we propose a brand-new two-fault model composed of a main fault and a secondary fault as the optimal model for the Jiuzhaigou earthquake, in which the secondary fault is at a wide obtuse angle to the northern end of the main fault plane. Results show that the dislocation distribution is dominated by sinistral slip, with a significant shallow slip deficit. The main fault consists of two asperities bounded by an aftershock gap, which may represent a barrier. In addition, most aftershocks are located in stress shadows and appear a complementary pattern with the coseismic high-slip regions. We propose that the aftershocks are attributable to the background tectonic stress, which may be related to the velocity-strengthening zones.

The mediating role of rumination in the relation between self-compassion, posttraumatic stress disorder, and posttraumatic growth among adolescents after the Jiuzhaigou earthquake

Previous studies indicated that self-compassion can alleviate posttraumatic stress disorder (PTSD) and promote posttraumatic growth (PTG). Rumination is a cognitive process in which individuals repeatedly think about traumatic events and their consequences, which includes invasive rumination (IR) and deliberate rumination (DR). IR is a process of thoughts over traumatic event that invades the cognitive world in an undesired state, whereas DR is an individual’s conscious and repeated thinking on events. IR and DR have also been shown to have effects on PTSD and PTG. Studies have examined the relationship between self-compassion, IR, deliberate rumination DR, PTSD, and PTG, respectively, but only a few studies have examined their combined roles in PTSD and PTG among adolescents who have experienced an earthquake. In addition, recent studies have shown that the positive and negative components of self-compassion have different effects on post-traumatic stress responses, but few studies have explored the mechanisms of positive and negative self-compassion on PTSD and PTG in adolescents who experience earthquakes. This study examines the mediating roles of rumination in the relationship between self-compassion, PTSD and PTG among Chinese adolescents after the Jiuzhaigou earthquake. Ten months after the Jiuzhaigou earthquake, a questionnaire survey was conducted among 621 middle school students in Jiuzhaigou county. The participants were assessed by using the following systems: Trauma Exposure Questionnaire, Self-Compassion Scale, Event-Related Rumination Inventory, PTSD Checklist for DSM-5, and Posttraumatic Growth Inventory. When we controlled for the gender, age, and trauma exposure in the direct effect model, positive self-compassion (PSC) had a negative effect on PTSD and a positive effect on PTG. Moreover, negative self-compassion (NSC) had a positive effect on PTSD, but the effect on PTG was nonsignificant. In the indirect model, PSC had an indirect and positive effect on PTSD and PTG via deliberate rumination (DR). NSC had an indirect and positive effect on PTSD and PTG via invasive rumination (IR), whereas NSC also had an indirect and negative effect on PTSD and PTG, as well as via an indirect path from IR to DR on PTSD and PTG. Findings indicated that increased PSC may reduce PTSD and be beneficial for PTG. NSC may exacerbate PTSD. Rumination plays a significant mediating role between self-compassion, PTSD, and PTG.

Evidence for Fluids at the Hypocenter of the 2017 Ms 7.0 Jiuzhaigou Earthquake Revealed by Local Earthquake Tomography

AbstractThe seismogenic structure of the 2017 Ms 7.0 Jiuzhaigou earthquake is still under debate. To address this issue, we use arrival times of P‐ and S‐ waves from 1766 local earthquakes recorded by our deployed 18 temporary seismic stations to perform the local earthquake tomography and present the 3‐D distributions of the P‐ wave and S‐ wave velocities (Vp and Vs), Vp/Vs ratio and the locations of the relocated aftershocks in the source region of the 2017 Jiuzhaigou earthquake. The aftershocks occurred at depths of 2–10 km, and can be divided into a NNW‐SSE trending northern segment and a NW‐SE trending southern segment relative to hypocenter of the main shock. Combined with previous observations, it is proposed that the Jiuzhaigou earthquake occurred on an unmapped fault which is a branch of the easternmost segment of the eastern Kunlun fault zone. The source region of the Jiuzhaigou earthquake is characterized by high Vp and low Vs anomalies as well as high Vp/Vs ratios, suggesting the presence of fluids in the fractured rocks. These fluids may have contributed to the occurrence of the Jiuzhaigou earthquake by reducing the effective normal stress and thus causing the brittle failure of rocks.

Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery

The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment.

Crowdsourced social media and mobile phone signaling data for disaster impact assessment: A case study of the 8.8 Jiuzhaigou earthquake

Rapid and accurate disaster impact assessment is crucial for disaster emergency decision-making. The perception and behavioral characteristics of people derived from crowdsourced data, such as social media and mobile phone signaling data, can provide information sources for disaster assessment and assist disaster emergency management. This topic has attracted extensive attention from scholars. However, these data types are incomplete and have wide-ranging quality, which makes it difficult to evaluate the disaster. Social media data contain many public descriptions of disasters, but the spatial distribution is uneven. Mobile phone signaling data cover a wide range and are spatiotemporal continuous, but semantic information is lacking. Thus, this paper develops a disaster impact assessment method that integrates social media data and mobile signaling data based on a classification of social media data with a character-level convolutional neural network (Char-CNN), anomaly detection in mobile signaling data, and feature mining of human activity. The result shows that social media and mobile signaling data complement each other well. The proposed method improves the assessment accuracy and provides detailed information on the spatial distribution of disasters. The results of this study provide reference for the combination of different sources of information for disaster response decisions.

Impact of earthquakes on natural area-driven tourism: Case study of China's Jiuzhaigou National Scenic Spot

Tourism is one of the most economically important industries worldwide, but is particularly vulnerable to natural disasters. Located in a remote and impoverished mountainous region, Sichuan's Aba Tibetan and Qiang Autonomous Prefecture (Aba) has become highly tourism-dependent, being home to China's highly visited Jiuzhaigou National Scenic Spot (Jiuzhaigou). Jiuzhaigou has experienced several recent turbulences following major earthquakes. To explore the implications of these types of events on resilient and sustainable tourism development, we examined the relationship between the tourism industry and the 2017 Jiuzhaigou Earthquake at multiple-scales (e.g., local, prefectural, and provincial) based on the tourism background trend line and (seasonal) autoregressive integrated moving average models. Following the 2017 Jiuzhaigou Earthquake, both Jiuzhaigou and Aba's tourism industries were devastated, but Sichuan's broader tourism industry experienced relatively little impact. Following the earthquake, subsequent visitation to Jiuzhaigou reduced by 56% (2017), 99% (2018) and 93% (2019), and Aba's international (54%, 68%, and 48%) and domestic (30%, 49%, and 40%) tourist volumes decreased over the same timespan. The impacts of the earthquake also varied significantly by month at the local and prefectural levels. Moreover, earthquakes considerably altered Jiuzhaigou's relative importance within Sichuan's broader tourism industry, with Aba's inbound tourism share decreasing from 35% (2004) to 8% (2008) and 2% (2017), following several major earthquakes. We highlight the importance of domestic tourism and economic diversity for achieving resilience and rejuvenating local economic activities following natural disasters.

Health-related quality of life among survivors in minority area 2 years after Jiuzhaigou earthquake: A cross-sectional study.

To investigate the health-related quality of life (HRQoL) and related factors, the symptoms of posttraumatic stress disorder (PTSD) among earthquake survivors in minority area 2 years after Jiuzhaigou earthquake.Two years after the Jiuzhaigou earthquake, a cross-sectional survey was conducted by a multi-stage sampling approach. HRQoL was measured by the short form 12 (SF-12), PTSD was measured by the PTSD Check List-Civilian Version (PCL-C), and social support was measured by the Multidimensional Scale of Perceived Social Support. Descriptive statistics, t-tests, ANOVA and multiple linear regression analysis were used for data analysis.Of the 561 participants, the mean scores on the physical component summary (PCS) and mental component summary (MCS) were 46.36 ± 12.79 and 55.03 ± 8.73, and 5.73% reported the symptoms of PTSD. Lower PCS and MCS after an earthquake were associated with elderly age, physical illness, and low level of social support (P < .05). Meanwhile, PTSD was significantly associated with MCS.These findings showed that physical disease and psychological symptoms can adversely affect the HRQoL of survivors. Moreover, providing higher social support to survivors should be considered as a way to improve the HRQoL outcomes of survivors.

Monitoring and Assessment for the Susceptibility of Landslide Changes After the 2017 Ms 7.0 Jiuzhaigou Earthquake Using the Remote Sensing Technology

Monitoring the change of post-seismic landslides could provide valuable information for geological disaster treatment. The 2017 Jiuzhaigou Ms 7.0 earthquake has triggered a large number of landslides in the Jiuzhaigou United Nations Educational, Scientific and Cultural Organization (UNESCO) Natural Heritage site, which provides a unique opportunity for monitoring the spatio-temporal characteristics and exploring the impact factors of post-seismic landslides change. In this study, the spatio-temporal characteristics of landslides and their post-seismic changes are analyzed using multi-source, multi-temporal, and multi-scale remote sensing data combining with the field study. The Support Vector Machine classification, visual interpretation, field investigation, and Geographic Information System technology are employed to extract landslides and analyze their spatial distribution patterns. Moreover, the Certainty Factor method is used to explore the susceptibility of landslides and to find key impact factors. Our results show that the net increase area of landslide is 1.2 km2 until September 27th, 2019, which are induced by the expansion of coseismic landslide, the post-seismic landslide, and the expansion of vegetation degradation. Moreover, the area expansion of the coseismic and post-seismic landslides is mainly related to the increase of debris flow induced by the post-seismic torrential rainfalls. The highest net increase rate of post-seismic landslide change does not distribute on the regions with the highest density of coseismic landslides. The susceptibility of post-seismic landslide change is greatly influenced by slope, altitude, aspect, peak ground acceleration fault, and strata. It is higher in the coseismic landslide area with low susceptibility. This study also suggests that the potential landslides will most likely occur in the unstable slope region affected by the additional driving force. Therefore, great attention should be paid to identify and prevent the potential landslides on unstable slopes in addition to treatments of the sliding slopes. This study provides a good example for the monitoring and assessment of post-seismic landslides in mountainous regions with a steep slope and deep valley.

Research on Soil Characteristics of Different Damaged Woodlands in Jiuzhaigou County

In order to explore the distribution situations of physical and chemical characteristics of different woodlands damaged in Jiuzhaigou earthquake and further improve the rationality of the rapid restoration model of forest-grass vegetation in the damaged habitat, the soil physical and chemical properties of different plots were analyzed according to the degree of earthquake damage in the study area. The results showed that: (1) The soil bulk density first decreased and then increased with the increasing degree of earthquake damage. (2) The contents of available phosphorus and ammonia nitrogen are positively correlated with the degree of earthquake damage, while the contents of available potassium and nitrate nitrogen showed the opposite pattern. Therefore, targeted soil improvement should be carried out according to the soil physical and chemical properties of different earthquake-damaged plots, in order to improve the recovery speed of forest-grass vegetation restoration, and enhance the supporting conditions of soil fertility in various restoration models.

A Bayesian Network Model for Seismic Risk Analysis.

Earthquakes are one of the most unpredictable natural disasters. A series of secondary and derived disaster events may occur afterwards and lead to even more consequences. In such situations, a seismic risk analysis that takes into account secondary and derived disaster events is vital in reducing the risks of such disasters. The absence of a holistic seismic risk analysis model-one that takes into account the derived disaster events-may mean that the serious consequences of the disaster chains set off by earthquakes are neglected. This article proposes a comprehensive seismic risk analysis that enables a better understanding of seismic disaster chains and rescue scenarios. The approach is based on a Bayesian network constructed using scenario-based methods. The final network structure is achieved by learning parameters. To determine the critical secondary disasters and the key emergency-response measures, probability adaptation and updating using the Bayesian model were performed. The practical application of the model is illustrated using the Wenchuan earthquake and the Jiuzhaigou earthquake in China. The two examples show that the model can be used to predict the potential effects of secondary disasters and the final seismic losses. The results of the model can help decisionmakers gain a comprehensive understanding of seismic risk and implement practical emergency-rescue measures to reduce risk and losses.

From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations

The vast majority of statistically-based landslide susceptibility studies assumes the slope instability process to be time-invariant under the definition that “the past and present are keys to the future”. This assumption may generally be valid. However, the trigger, be it a rainfall or an earthquake event, clearly varies over time. And yet, the temporal component of the trigger is rarely included in landslide susceptibility studies and only confined to hazard assessment. In this work, we investigate a population of landslides triggered in response to the 2017 Jiuzhaigou earthquake (M_w = 6.5) including the associated ground motion in the analyses, these being carried out at the Slope Unit (SU) level. We do this by implementing a Bayesian version of a Generalized Additive Model and assuming that the slope instability across the SUs in the study area behaves according to a Bernoulli probability distribution. This procedure would generally produce a susceptibility map reflecting the spatial pattern of the specific trigger and therefore of limited use for land use planning. However, we implement this first analytical step to reliably estimate the ground motion effect, and its distribution, on unstable SUs. We then assume the effect of the ground motion to be time-invariant, enabling statistical simulations for any ground motion scenario that occurred in the area from 1933 to 2017. As a result, we obtain the full spectrum of potential coseismic susceptibility patterns over the last century and compress this information into a hazard model/map representative of all the possible ground motion patterns since 1933. This backward statistical simulations can also be further exploited in the opposite direction where, by accounting for scenario-based ground motion, one can also use it in a forward direction to estimate future unstable slopes.

The spatial distribution characteristics of coseismic landslides triggered by the Ms7.0 Lushan earthquake and Ms7.0 Jiuzhaigou earthquake in southwest China.

In recent years, a series of earthquakes have occurred in Sichuan province, southwest China. On April 20, 2013, 1013 landslides were triggered in the Lushan earthquake within the research area of 1215.87km2. On August 8, 2017, 821 landslides were induced in the Jiuzhaigou earthquake within the study area of 541.61km2. The slope, aspect, elevation, distance to the river, distance to fault, stratum lithology, curvature, and PGA as evaluation factors were chosen to research the distribution characteristics of coseismic landslides. The relationship between influence factors and distribution of coseismic landslides were analyzed using landslide area density (LAD), defined as the ratio between the coseismic landslides area and the total area under the specific classification of eight factors, and landslide number density (LND), interpreted as the number of landslides per square kilometer affected by an earthquake. Both the LAD and LND curves show the common correlations between distribution characteristics of coseismic landslides and the classification of eight factors. The high density of coseismic landslides was found at locations where the valley evolves from U-shaped to V-shaped, along slopes with an inclination between 20 and 50°. Due to the effect of river cutting erosion and human engineering activities, massive coseismic landslides appeared within the scope of 200m from the river system. Eight hundred sixty small shallow coseismic landslides were found less than 3km from the seismogenic fault in both earthquakes. Both concave and convex terrain has the same effect of promoting coseismic landslides. The strata of the Carboniferous and Cretaceous are more prone to coseismic landslides occurrence. In the event of another earthquake of similar magnitude in southwest China in the future, the research results can quickly predict the distribution of coseismic landslides and provide a scientific basis for emergency rescue.