Lushan Earthquake Research Articles

Stochastic Green’s Function Method Considering Non-Uniform Rise Time Distribution to Simulate 3D Broadband Ground Motion

The stochastic Green’s function method has been widely used in the field of ground motion simulation in recent years. It is generally assumed that the rise time of each subfault is the same in this method. Since the rise time significantly influences the amplitude of simulation results in the intermediate frequency band, to improve the accuracy of stochastic Green’s function method for near-fault broadband ground motion simulation, referring to the numerical simulation results of Day, the rise time is assumed to be non-uniformly distributed on the fault, and an improved approximate expression of rise time on a rectangular fault considering that the rupture starting point may be at any position and the aspect ratio may be arbitrary is proposed. Additionally, the contributions of P, SV and SH wave are considered, respectively, and an improved stochastic Green’s function method is proposed for 3D broadband ground motion simulation. Taking the 1994 Northridge earthquake in America and 2013 Lushan earthquake in China as examples, under different subfault division numbers, the synthesized source spectra are compared with the omega-squared theoretical source spectra of the large earthquake, and the simulated ground motions at observation points are compared with observed records to verify the effectiveness of the improved method. The results show that when the Northridge earthquake fault and Lushan earthquake fault are divided into 9 × 10 subfaults and 11 × 7 subfaults, respectively, the simulation results obtained using the improved method are close to the observed records in the broadband frequency range. Therefore, the improved method can effectively simulate the 3D ground motion in near-fault regions.

Modified stochastic finite-fault modelling based on the rupture process: a case study of the 2013 Mw 6.7 Lushan earthquake in China

Modified stochastic finite-fault modelling based on the rupture process: a case study of the 2013 Mw 6.7 Lushan earthquake in China

Stress heterogeneity in the eastern Tibetan Plateau and implications for the present-day plateau expansion

The eastward expansion of the Tibetan Plateau has resulted in different earthquake types in the eastern Tibetan Plateau, but the mechanism remains unclear. Here, we construct a three-dimensional visco-elastoplastic finite element model considering the topography to investigate the influence of fault geometry and rheological heterogeneity on stress fields. In our best-fitting model, the minimum principal stress is nearly vertical around the southern Huya fault zone, which is adjacent to the Longmen Shan fault zone, due to the significant mid-lower crust lateral rheological heterogeneity, and the thrust stress regime accounts for the reverse fault and thrust-dominated earthquakes. In this scenario, the eastward horizontal motion of the mid-lower crust is obstructed and facilitates thrust faulting, suggesting the limited eastward expansion of the Tibetan Plateau. In contrast, the northern Huya fault zone, one of the terminal branches of the East Kunlun fault, accommodates the continuous eastward extrusion of the East Kunlun fault, where the stress regime under a more homogenized crust favors the strike-slip faulting process, along with the dominant strike-slip earthquakes. Moreover, the best-fitting of stress regime explains the thrust-dominated 2008 Ms. 8.0 Wenchuan and 2013 Ms. 7.0 Lushan earthquakes on the Longmen Shan fault zone. Combining geophysical and geodetic observations and model analyses, we propose that the hybrid deformation mode in the eastern Tibetan Plateau is accommodated by upper crustal shear and thrusting deformation and mid-lower crustal thickening driven by the gravitational potential energy gradient. Our results elucidate the mechanism for differences in strong historical earthquakes and, more importantly, isolate the effect of fault geometry from those of heterogeneous viscosity on crustal deformation and stress heterogeneity in the eastern Tibetan Plateau.

How does active coping affect post‐traumatic growth? An empirical analysis from Lushan earthquake survivors

AbstractTraumatic experiences from major natural disasters, such as earthquakes, can lead to mental health challenges, which in turn may foster post‐traumatic growth (PTG). In this study, 859 survivors of the 2013 Lushan Earthquake in China were selected as research participants. We explored the relationship between active coping and PTG, the indirect effect of resilience in this relationship, and the moderating effect of social support. The empirical results revealed that active coping positively influenced PTG, with resilience has an indirect effect in this relationship. Social support moderated the relationship between resilience and PTG but did not affect the relationship between active coping and PTG. This study not only provides new insights into how earthquake survivors achieve PTG through active coping strategies but also emphasises the important role of psychological resilience in facilitating post‐traumatic recovery.

Extraction of pre-earthquake anomalies from borehole strain data using Graph WaveNet: a case study of the 2013 Lushan earthquake in China

Abstract. On 20 April 2013, Lushan experienced an earthquake with a magnitude of 7.0. In seismic assessments, borehole strainmeters, recognized for their remarkable sensitivity and inherent reliability in tracking crustal deformation, are extensively employed. However, traditional data-processing methods encounter challenges when handling massive dataset-s. This study proposes using a Graph WaveNet graph neural network to analyze borehole strain data from multiple stations near the earthquake epicenter and establishes a node graph structure using data from four stations near the Lushan epicenter, covering the years 2010–2013. After excluding the potential effects of pressure, temperature, and rainfall, we statistically analyzed the pre-earthquake anomalies. Focusing on the Guza, Xiaomiao, and Luzhou stations, which are the closest to the epicenter, the fitting results revealed two acceleration events of anomalous accumulation that occurred before the earthquake. Occurring approximately 4 months before the earthquake event, the first acceleration event indicated the pre-release of energy from a weak fault section. Conversely, the acceleration event observed a few days before the earthquake indicated a strong fault section that reached an unstable state with accumulating strain. We tentatively infer that these two anomalous cumulative accelerations may be related to the preparation phase for a large earthquake. This study highlights the considerable potential of graph neural networks in conducting multistation studies of pre-earthquake anomalies.

A method for rapidly assessing landslide hazard—taking the landslide in Yongxing town, Mingshan area as an example

To overcome the reliance on large samples and high-quality data in existing evaluation methods, while also improving evaluation efficiency and accuracy, this paper proposes a method for rapid landslide hazard assessment. This method utilizes existing research findings and specific analytical techniques for the study area to conduct rapid assessments. Taking the landslide in Yongxing Town, Mingshan Area, Ya’an City, Sichuan Province as an example, the Analytic Hierarchy Process (AHP) is combined with the Information Value (IV) method, Certainty Factor (CF) method, and Frequency Ratio (FR) method from previous studies, The AHP-IV and AHP-FR methods assess the study area as a moderately hazardous zone, while the AHP-CF method assesses it as a slightly hazardous zone. Affected by the strong 2013 Lushan earthquake, the landslide in the study area caused permanent damage. Field investigation results show that the landslide hazard in the study area is moderate, and the AHP-IV and AHP-FR methods are more consistent with the actual field results. The AHP-CF method, due to not considering the water system factor and having certain errors in its discrimination method, leans towards a safer assessment, The results of the three evaluation methods are somewhat consistent.

Near-Real Prediction of Earthquake-Triggered Landslides on the Southeastern Margin of the Tibetan Plateau

Earthquake-triggered landslides (ETLs) feature large quantities, extensive distributions, and enormous losses to human lives and critical infrastructures. Near-real spatial prediction of ETLs can rapidly predict the locations of coseismic landslides just after a violent earthquake and is a vital technical support for emergency response. However, near-real prediction of ETLs has always been a great challenge with relatively low accuracy. This work proposes an ensemble prediction model of EnPr by integrating machine learning tree models and a deep learning convolutional neural network. EnPr exhibits relatively strong prediction and generalization performance and achieves relatively accurate prediction of ETLs. Six great seismic events occurring from 2008 to 2022 on the southeastern margin of the Tibetan Plateau are selected to conduct ETL prediction. In a chronological order, the 2008 Ms 8.0 Wenchuan, 2010 Ms 7.1 Yushu, 2013 Ms 7.0 Lushan, and 2014 Ms 6.5 Ludian earthquakes are employed for model training and learning. The 2017 Ms 7.0 Jiuzhaigou and 2022 Ms 6.1 Lushan earthquakes are adopted for ETL prediction. The prediction accuracy merits of ACC and AUC attain 91.28% and 0.85, respectively, for the Jiuzhaigou earthquake. The values of ACC and AUC achieve 93.78% and 0.88, respectively, for the Lushan earthquake. The proposed EnPr algorithm outperforms the algorithms of XGBoost, random forest (RF), extremely randomized trees (ET), convolutional neural network (CNN), and Transformer. Moreover, this work reveals that seismic intensity, high and steep relief, pre-seismic fault tectonics, and pre-earthquake road construction have played significant roles in coseismic landslide occurrence and distribution. The EnPr model uses globally accessible open datasets and can therefore be used worldwide for new large seismic events in the future.

A risk-averse distributionally robust optimisation approach for drone-supported relief facility location problem

Drone-supported last-mile humanitarian logistics applications play a crucial role in the rapid and efficient delivery of essential relief items, such as medicine, blood, and vaccines, during disaster and emergency situations. This paper explores a novel drone-supported relief facility location problem (DSRFLP) aimed at establishing an effective drone-supported last-mile humanitarian logistics system. The problem involves making joint decisions for both pre-disaster and post-disaster phases while considering the characteristics of drone-based delivery operations and uncertain demands. In the pre-disaster phase, we make the decisions regarding the locations of drone-supported relief facilities, inventory prepositioning of relief items, assignment of drones to the opened facilities, and allocation of drones to disaster demand sites. In the post-disaster phase, we make decisions related to delivery quantities. To tackle the challenge of incomplete demand distribution information in chaotic disaster environments, we establish a distributionally robust optimisation (DRO) model to handle the uncertainty of demands. This model adopts worst-case mean Conditional Value-at-Risk as the risk measurement, reflecting the risk-averse attitude of humanitarian organisers. In this paper, three ambiguity sets (box, ellipsoidal, and polyhedral) are considered to describe the ambiguity distributions of demands. To overcome the computational challenge, we reformulate the DRO model under three ambiguity sets into two mixed-integer linear programming models and one second-order cone programming model, which can be efficiently solved by off-the-shelf solvers. Furthermore, we validate our proposed DRO model through a small-scale example and a large-scale case study based on the Lushan earthquake in China. The computational outcomes underscore the superior performance of the proposed DRO model to mitigate the impact arising from incomplete probability distributions. We propose managerial implications and insights to support the decision-making of humanitarian organisations based on the experimental results. Finally, we propose two extended models to incorporate multiple relief items and equity constraints in priority settings and conduct numerical experiments to adapt to various real-world disaster scenarios.

Spatio-temporal characteristics of seismic strain anomalies reveal seismic risk zones along the Longmenshan fault zone and adjacent areas

Introduction: Identifying and quantifying earthquake precursors, and analyzing their physical mechanisms, continues to be a challenge for earthquake forecasting. In this study, orthogonal functions were developed to effectively identify precursor anomalies, thereby improving the forecasting of strong earthquakes.Methods: To study the spatio-temporal contour anomalies in seismic strain fields, we assessed them for seismic activity variables and natural orthogonal function expansion, in six strong earthquakes near the Longmenshan Fault Zone, China, that have occurred since 2008.Results: We observed that, prior to these earthquakes, the temporal factor (the time variation characteristics of the strain field) displayed anomalies with high/low values exceeding the mean square error within a stable context. The anomalies exhibited multi-component characteristics and were primarily concentrated in the first four-strain fields. Short-term and impending-earthquake anomalies were observed in the temporal factor before the 2008 Wenchuan (M8.0) and 2013 Lushan (M7.0) earthquakes, while medium-term and long-term anomalies appeared before the other four strong earthquakes, without notable short-term anomalies. The temporal evolution of strain field contour anomalies, and the strain contours positive and negative intersection, showed that central areas surrounded by multiple strain field contour anomalies were potential locations for strong earthquakes. This suggests a potential approach for earthquake location forecasting. Since 2009, there have been five strong earthquakes, each affected to varying degrees by anomalous strain fields from the 2008 Wenchuan (M8.0) earthquake.Conclusion: The results of this study corroborate the findings of the focal mechanism’s node shear stress, indicating significant physical implications of the anomalies and the reliability of these conclusion.

Reactive Transport Process of Earthquake‐induced Hydro‐chemical Changes in Guanding Thermal Spring, Western Sichuan, China

AbstractEarthquake‐related hydrochemical changes in thermal springs have been widely observed; however, quantitative modeling of the reactive transport process is absent. In the present study, we apply reactive transport simulation to capture the hydrochemical responses in a thermal spring following the Wenchuan Ms 8.0 and Lushan Ms 7.0 earthquakes. We first constrain deep reservoir geothermal fluid compositions and temperature by multicomponent geothermometry, and then a reactive geochemical transport model is constructed to reproduce the hydrochemical evolution process. The results show that the recharge from the shallow aquifer increases gradually until it reaches a peak because of the permeability enhancement caused by the Lushan earthquake, which may be the mechanism to explain the earthquake‐related hydrochemical responses. In contrast to the postseismic effect of the Wenchuan earthquake, the chemical evolution can be considered as hydrochemical anomalies related to the Lushan earthquake. This study proves that the efficient simulation of reactive transport processes is useful for investigating earthquake‐related signals in hydrochemical time series.

Electrical Structure of Southwestern Longmenshan Fault Zone: Insights into Seismogenic Structure of 2013 and 2022 Lushan Earthquakes

On 1 June 2022, a magnitude 6.1 earthquake struck the southern segment of the Longmenshan fault zone on the eastern edge of the Tibetan Plateau, once again causing casualties and economic losses. Understanding the deep-seated dynamic mechanisms that lead to seismic events in the Lushan earthquake area and assessing the potential hazards in seismic gap areas are of significant importance. In this study, we utilized 118 magnetotelluric datasets collected from the Lushan earthquake area and employed three-dimensional electromagnetic inversion with topographic considerations to characterize the deep-seated three-dimensional resistivity structure of the Lushan earthquake area. The results reveal that the Shuangshi–Dachuan fault in the Lushan earthquake area can be divided into two relatively low-resistivity zones: a western zone dipping southeastward and an eastern zone with a steeper slightly northwestern dip. These two zones intersect at a depth of approximately 20 km, forming an extensional pattern resembling a “Y” shape. The epicenters of both the 2013 and 2022 Lushan earthquakes are primarily located in the upper constricted portion of the pocket-like low-resistivity body at depth. The distribution of seismic aftershocks is confined within the region enclosed by the high-resistivity body, following the pattern of the Y-shaped low-resistivity zone.

Carbon Monoxide Anomalies for the 2013 Mw=6.7 Lushan Earthquake Using MSSA-RST Approach

Carbon Monoxide Anomalies for the 2013 Mw=6.7 Lushan Earthquake Using MSSA-RST Approach

Structural geometry and active faulting along the southern segment of the Longmenshan thrust belt: Insights into the seismotectonics of the 1327 Tianquan and 2013 Lushan earthquakes (eastern Tibetan Plateau, China)

The 2008 Mw 7.9 Wenchuan and 2013 Mw 6.7 Lushan earthquakes ruptured the central and southern segments of the Longmenshan thrust belt on the eastern Tibetan Plateau, respectively. The ∼M 7 Tianquan earthquake in 1327 is also thought to have occurred on the southern segment with effects spatially overlapping those of the Lushan earthquake, but the deformation style and seismogenic mechanism of the segment remain poorly understood. Satellite image interpretation, field investigations, and trenching reveal that the Shuangshi-Dachuan fault (SDF) on the southern segment displaces landforms, dips southeast, and is dominated by thrust motion with a left-lateral strike-slip component. The latest and penultimate events along the fault occurred in 1085–1457 CE and 1215 BCE-104 CE, respectively. Some ancient landslides exits along the fault, and one of them formed close to 703 ± 17 yr BP. The ages of the latest event and of the landslide agree well with the occurrence of the Tianquan earthquake, indicating that the earthquake was most likely generated by the SDF. Magnetotelluric imaging and the aftershock belt of the Lushan earthquake show that the Longmen Shan forms a wedge-shaped high-resistivity body protruding eastward. The SDF corresponds well to the east-dipping upper electrical boundary of the wedge-shaped body and is interpreted as a shallow backthrust; the seismogenic fault of the Lushan earthquake corresponds to the west-dipping lower boundary and is interpreted as a blind thrust fault. The left-lateral slip component of the SDF may be a response to the lateral extension of the weak lower crust.

An Infinite Slope Model Considering Unloading Joints for Spatial Evaluation of Coseismic Landslide Hazards Triggered by a Reverse Seismogenic Fault: A Case Study of the 2013 Lushan Earthquake

Coseismic landslides pose a significant threat to the sustainability of both the natural environment and the socioeconomic fabric of society. This escalation in earthquake frequency has driven a growing interest in regional-scale assessment techniques for these landslides. The widely adopted infinite slope model, introduced by Newmark, is commonly utilized to assess coseismic landslide hazards. However, this conventional model falls short of capturing the influence of rock mass structure on slope stability. A novel methodology was previously introduced, considering the roughness of potential slide surfaces on the inner slope, offering a fresh perspective on coseismic landslide hazard mapping. In this paper, the proposed method is recalibrated using new datasets from the 2013 Lushan earthquake. The datasets encompass geological units, peak ground acceleration (PGA), and a high-resolution digital elevation model (DEM), rasterized at a grid spacing of 30 m. They are integrated within an infinite slope model, employing Newmark’s permanent deformation analysis. This integration enables the estimation of coseismic displacement in each grid area resulting from the 2013 Lushan earthquake. To validate the model, the simulated displacements are compared with the inventory of landslides triggered by the Lushan earthquake, allowing the derivation of a confidence level function that correlates predicted displacement with the spatial variation of coseismic landslides. Ultimately, a hazard map of coseismic landslides is generated based on the values of the certainty factor. The analysis of the area under the curve is utilized to illustrate the improved effectiveness of the proposed method. Comparative studies with the 2014 Ludian earthquake reveal that the coseismic landslides triggered by the 2013 Lushan earthquake predominantly manifest as shallow rock falls and slides. Brittle coseismic fractures are often associated with reverse seismogenic faults, while complaint coseismic fractures are more prevalent in strike–slip seismogenic faults. The mapping procedure stands as a valuable tool for predicting seismic hazard zones, providing essential insights for decision-making in infrastructure development and post-earthquake construction endeavors.

The crustal structure of the Longmenshan fault zone and its implications for seismogenesis: new insight from aeromagnetic and gravity data

Abstract. Although many geophysical models have been proposed in the Longmenshan fault zone (LFZ) and its surrounding areas, the deep structure of the seismic gap and its constraint of the Wenchuan and Lushan earthquakes remain uncertain. Based on the compiled aeromagnetic data and Bouguer gravity data, we have tried to create a more detailed and reasonable magnetic and density model using 2D forward modeling and 3D inversion and made the deep structure of the LFZ visible. The research shows that structure is heterogenous across the LFZ. The earthquake epicenters are located in regions with high-magnetic anomalies and gravity gradients that are associated with rigid blocks that were likely to accumulate stress. However, the seismic gap shows low-magnetic anomalies and transition of gravity anomalies related to a weak zone. The Sichuan Basin has two NE-trending banded high-magnetic blocks extending beneath the LFZ that firmly support the idea that the crust of the Sichuan Basin has subducted downward the LFZ. More importantly, the basement subducts to approximately 33 km west of the Wenchuan–Maoxian fault, with a low dip angle beneath the middle segment of the LFZ, whereas the distance decreases to approximately 17 and 19 km under the southern segment. Thus, the crust of the Sichuan Basin beneath the middle segment extends farther than that beneath the southern segment, with the seismic gap as the transition zone. Therefore, we propose that the structural heterogeneity of the basement on the western margin of the Sichuan Basin may be the main reason for the different focal mechanisms and geodynamics of the Wenchuan and Lushan earthquakes.

Pre-earthquake anomaly extraction from borehole strain data based on machine learning

Borehole strain monitoring plays a critical role in earthquake precursor research. With the accumulation of observation data, traditional data processing methods struggle to handle the challenges of big data. This study proposes a segmented variational mode decomposition method and a GRU-LUBE deep learning network based on machine learning theory. The algorithm enhances data correlation during decomposition and effectively predicts borehole strain data changes. We extract pre-earthquake anomalies from four-component borehole strain data of the Guza station for two major earthquakes in Sichuan (Wenchuan and Lushan earthquakes), obtaining more comprehensive anomalies than previous studies. Statistical analysis reveals similar abnormal phenomena in the Guza station’s borehole strain data before both earthquakes, suggesting shared crustal stress accumulation and release patterns. These findings highlight the need for further research to improve earthquake prediction and preparedness through understanding underlying mechanisms.

Failure history, mechanism, and recent run-out reproduction of the Xinhua Village landslide triggered by the 2022 Ms 6.1 Lushan earthquake

Failure history, mechanism, and recent run-out reproduction of the Xinhua Village landslide triggered by the 2022 Ms 6.1 Lushan earthquake

Slip distribution inversion of seismic sub-fault dip iteration using gradient based optimizer algorithm

This paper considers setting different dips for different sub-faults to fit the actual rupture situation based on the fault rupture of the 2013 Lushan MS7.0 earthquake. Meanwhile, combined with the coseismic GNSS data of the Lushan earthquake, the source parameters and sliding distribution of the Lushan earthquake fault are inversed. Firstly, we use the gradient based optimizer (GBO) in nonlinear inversion to obtain the source parameters of this seismic fault. The inversion results indicate that the strike of the fault is 206.52°, the dip is 44.10°, the length is 21.92 km, and the depth is 12.79 km. To refine the sliding distribution of the seismic fault, the seismic fault is divided into 3 × 3 sub-faults. Then, we fix the central sub-fault dip of 44.10°; the dip of other sub-faults is obtained by iteration. After that, the model is further divided into a fault layer model composed of 23 × 19 sub fault slices, and using the Matlab fitting function is used to fit the dip of the 23 × 19 sub faults. Finally, the Lushan seismic fault plane is established as a shovel structure with steep upper and gentle lower, steep south and gentle north. The slip distribution inversion results indicate that the depth of the slip peak is 13 km, the corresponding maximum slip momentum is 0.67 m, the seismic moment is 1.10 × 1019 N·m and the corresponding moment magnitude is MW6.66. The results above are consistent with the research results of seismology.

The Source Characteristics of the 2022 Ms 6.1 Lushan Earthquake: An Event That Occurred on the Conjugated Fault of the 2013 Ms 7.0 Lushan Earthquake

Abstract The 2022 Ms 6.1 Lushan earthquake occurred in the southern segment of the Longmenshan fold-and-thrust belt, Sichuan Province, China, ∼7 km north-northwest of the 2013 Ms 7.0 Lushan earthquake. To understand its source characteristics and interplay with the 2013 Lushan event, we evaluate the 2022 Lushan earthquake in terms of focal mechanism, aftershock relocation, and rupture process. Results show that it is a purely thrust earthquake with a source depth of 14 km and a focal mechanism nodal plane of 29°/51°/80°. The automatic phase picker-based aftershock locus clarifies that the 2022 Lushan earthquake occurred on a blind northeast-trending conjugate fault of the 2013 Lushan earthquake. The finite-fault model constrained by teleseismic waveforms suggests that the high slip is concentrated in an asperity near the epicenter with a peak slip of 42 cm, which is characterized by thrust components. Based on the promotion of postseismic deformation following the 2013 Lushan earthquake and the similarity of the seismotectonic system, we argue that the 2022 Lushan earthquake may be an aftershock of the 2013 Lushan earthquake that occurred on a blind thrust fault.

Building rural community disaster resilience in developing countries: insights from a Chinese NGO's Safe Rural Community Program.

As rural areas in developing countries are generally more vulnerable to disasters triggered by natural hazards than urban areas, it has become critical to strengthen rural community disaster resilience (CDR) to reduce the disaster risks. Using follow-up interviews, surveys, and secondary data, this study reviewed the "Safe Rural Community" (SRC) program implemented by One Foundation, a Chinese civilian NGO, after the 2013 Lushan earthquake in China. The study focused on five main key resilience elements; networks, infrastructure, institutions, capabilities, and culture. It was found that the SRC program successfully developed standardized, systematic, interrelated, and practical activities, such as local volunteer rescue teams, emergency supplies, disaster reduction training, community emergency plans, and regular emergency rescue drills. Third-party evaluations and a 2022 Lushan earthquake test revealed that this NGO-led, community-based, and team-oriented program had been effective. The SRC program in Lushan, therefore, provides some guidance on the development of effective CDR programs in rural communities in developing countries.