Characterization and spatial analysis of coseismic landslides triggered by the Luding Ms 6.8 earthquake in the Xianshuihe fault zone, Southwest China

On September 5, 2022, a magnitude Ms 6.8 earthquake occurred along the Moxi fault in the southern part of the Xianshuihe fault zone located in the southeastern margin of the Tibetan Plateau,resulting in severe damage and substantial economic loss. In this study, we established a coseismic landslide database triggered by Luding Ms 6.8 earthquake, which includes 4794 landslides with a total area of 46.79 km^(2). The coseismic landslides primarily consisted of medium and small-sized landslides, characterized by shallow surface sliding. Some exhibited characteristics of high-position initiation resulted in the obstruction or partial obstruction of rivers, leading to the formation of dammed lakes. Our research found that the coseismic landslides were predominantly observed on slopes ranging from 30° to 50°, occurring at between 1000 m and 2500 m, with slope aspects varying from 90° to 180°. Landslides were also highly developed in granitic bodies that had experienced structural fracturing and strong-tomoderate weathering. Coseismic landslides concentrated within a 6 km range on both sides of the Xianshuihe and Daduhe fault zones. The area and number of coseismic landslides exhibited a negative correlation with the distance to fault lines, road networks, and river systems, as they were influenced by fault activity, road excavation, and river erosion. The coseismic landslides were mainly distributed in the southeastern region of the epicenter, exhibiting relatively concentrated patterns within the IX-degree zones such as Moxi Town, Wandong River basin, Detuo Town to Wanggangping Township. Our research findings provide important data on the coseismic landslides triggered by the Luding Ms 6.8 earthquake and reveal the spatial distribution patterns of these landslides. These findings can serve as important references for risk mitigation, reconstruction planning, and regional earthquake disaster research in the earthquake-affected area.

A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite vital to the emergency rescue in the 72-h‘‘golden window”.However,the limited quantity of interpreted landslides shortly after a massive earthquake makes landslide susceptibility prediction become a challenge.To address this gap,this work suggests an integrated method of Crossing Graph attention network and xgBoost(CGBoost).This method contains three branches,which extract the interrelations among pixels within a slope unit,the interrelations among various slope units,and the relevance between influencing factors and landslide probability,respectively,and obtain rich and discriminative features by an adaptive fusion mechanism.Thus,the difficulty of susceptibility modeling under a small number of coseismic landslides can be reduced.As a basic module of CGBoost,the proposed Crossing graph attention network(Crossgat)could characterize the spatial heterogeneity within and among slope units to reduce the false alarm in the susceptibility results.Moreover,the rainfall dynamic factors are utilized as prediction indices to improve the susceptibility performance,and the prediction index set is established by terrain,geology,human activity,environment,meteorology,and earthquake factors.CGBoost is applied to predict landslide susceptibility in the Gorkha meizoseismal area.3.43%of coseismic landslides are randomly selected,of which 70%are used for training,and the others for testing.In the testing set,the values of Overall Accuracy,Precision,Recall,F1-score,and Kappa coefficient of CGBoost attain 0.9800,0.9577,0.9999,0.9784,and 0.9598,respectively.Validated by all the coseismic landslides,CGBoost outperforms the current major landslide susceptibility assessment methods.The suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future.

The relationship between three-dimensional coseismic displacement and distribution of coseismic landslides

In mountainous areas,landslides induced by destructive earthquakes are one of the main causes of human casualties,which is an important link in the chain of earthquake hazards.Earthquake-triggered landslides are mainly controlled by three factors,namely seismic property,topography,and geology.Many studies have been conducted on these controlling factors of earthquake-triggered landslides.However,little is known about the effect of coseismic displacement on the distribution of landslides under different slope aspects and slope angles,hindering our understanding of the mechanism of inducing landslides by the combination of surface displacement and slope geometry at the local scale and leading to controversial opinions about the abnormal number of earthquake-triggered landslides in several cases.Here,we took the 2008 Wenchuan M_(w) 7.9 earthquake in China,the 2015 Gorkha M_(w) 7.8 earthquake in Nepal,and the 2016 Kaikōura M_(w) 7.8 earthquake in New Zealand as examples to investigate the relationship between the distribution of large earthquake-triggered landslides and the three-dimensional (3D)coseismic displacement field.We divided the landslide-prone area around the epicenter into regular grids and calculated the 3D coseismic displacement in each grid according to the radar satellite images and slip distribution model.Then,the 3D coseismic displacement was projected to two coordinate systems related to the slope where the landslides were located for statistical analysis.We determined that the surface uplift perpendicular to the slope is more likely to induce landslides,particularly when combined with large slope angles.Meanwhile,the number of landslides will be significantly reduced where the subsidence occurs.Regardless of uplift or subsidence,landslides are more likely to occur when the direction of coseismic horizontal displacement is far from the slope.The larger the slope angles are,the greater the effects of horizontal displacement and slope aspect.A dominant slope aspect also exists for earthquake-triggered landslides,which is different from the mean slope aspect calculated from the background topography.This dominant aspect angle is related to the focal mechanism and striking angle of surface rupture.These results indicate that we can simulate the 3D coseismic displacement field from known fault location and earthquake mechanism and combine the topographic data for landslide risk assessment in earthquake-prone mountainous areas to minimize the damage caused by possible earthquake-triggered landslides.

An open-accessed inventory of landslides triggered by the M_(S)6.8 Luding earthquake,China on September 5,2022

This study constructs a preliminary inventory of landslides triggered by the M_(S) 6.8 Luding earthquake based on field investigation and human-computer interaction visual interpretation on optical satellite images.The results show that this earthquake triggered at least 5007 landslides,with a total landslide area of 17.36 km^(2),of which the smallest landslide area is 65 m^(2)and the largest landslide area reaches 120747 m^(2),with an average landslide area of about 3500 m^(2).The obtained landslides are concentrated in the IX intensity zone and the northeast side of the seismogenic fault,and the area density and point density of landslides are 13.8%,and 35.73 km^(-2) peaks with 2 km as the search radius.It should be noted that the number of landslides obtained in this paper will be lower than the actual situation because some areas are covered by clouds and there are no available post-earthquake remote sensing images.Based on the available post-earthquake remote sensing images,the number of landslides triggered by this earthquake is roughly estimated to be up to 10000.This study can be used to support further research on the distribution pattern and risk evaluation of the coseismic landslides in the region,and the prevention and control of landslide hazards in the seismic area.

Application of logistic regression model for hazard assessment of landslides caused by the 2012 Yiliang Ms 5.7 earthquake in Yunnan Province,China

Accurate assessment of seismic landslides hazard is a prerequisite and foundation for postdisaster relief of earthquakes.An Ms 5.7 earthquake occurring on September 7,2012,in Yiliang County,Yunnan Province,China,triggered hundreds of landslides.To explore the characteristics of coseismic landslides caused by this moderate-strong earthquake and their significance in predicting seismic landslides regionally,this study uses an artificial visual interpretation method based on a planet image with 5-m resolution to obtain the information of the coseismic landslides and establishes a coseismic landslide database containing data on 232 landslides.Nine influencing factors of landslides were selected for this study:elevation,relative elevation,slope angle,aspect,slope position,distance to river system,distance to faults,strata,and peak ground acceleration.The real probability of coseismic landslide occurrence is calculated by combining the Bayesian probability and logistic regression model.Based on the coseismic landslides,the probabilities of landslide occurrence under different peak ground acceleration are predicted using a logistic regression model.Finally,the model established in this paper is used to calculate the landslide probability of the Ludian Ms 6.5 earthquake that occurred in August 2014,78.9 km away from the macro-epicenter of the Yiliang earthquake.The probability is verified by the real coseismic landslides of this earthquake,which confirms the reliability of the method presented in this paper.This study proves that the model established according to the seismic landslides triggered by one earthquake has a good effect on the seismic landslides hazard assessment of similar magnitude,and can provide a reference for seismic landslides prediction of moderate-strong earthquakes in this region.

Landslides triggered by the 1970 Ms 7.7 Tonghai earthquake in Yunnan,China: an inventory, distribution characteristics, and tectonic significance

The 1970 Tonghai earthquake, which occurred on January 5, 1970, in Tonghai County, Yunnan Province, China, triggered a large number of landslides. Since the occurrence of the earthquake, there have been a huge number of research reports on the seismogenic structure and earthquake mechanism, but rare reports on the seismic landslides. As the largest earthquake recorded in the Qujiang fault zone, the study on the coseismic landslides triggered by this earthquake are of great significance to the prevention and mitigation of earthquake-induced landslides in this region. In this study, we established a coseismic landslide inventory for the VⅢ–X seismic intensity areas of the Ms 7.7 Tonghai earthquake, and conducted spatial analysis on the coseismic landslides, mainly having analyzed the effect of the topographic factors, geological factors, and seismic factors on the development of the coseismic landslides. To enhance the understanding of this earthquake, we converted the earthquake epicenter and magnitude with empirical formulas based on the distributions and areas of the coseismic landslides. Comparing with coseismic landslides in other earthquake-hit areas, we found the capability that this earthquake could induce landslides is low. This study provides a useful supplement to the global coseismic landslide inventories and could be the basic data for seismic landslide assessment in this earthquake-prone region.

Near real-time spatial prediction of earthquake-induced landslides:A novel interpretable self-supervised learning method

Near real-time spatial prediction of earthquake-induced landslides(EQILs)can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake;thus,EQIL prediction is very crucial to the 72-hour‘golden window’for survivors.This work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau,a famous seismically-active zone,and proposes a novel interpretable self-supervised learning(ISeL)method for the near real-time spatial prediction of EQILs.This new method innovatively introduces swap noise at the unsupervised mechanism,which can improve the generalization performance and transferability of the model,and can effectively reduce false alarm and improve accuracy through supervisedfine-tuning.An interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial distribution.Experimental results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning methods.Furthermore,according to the interpretable module in the ISeL method,the critical controlling and triggering factors are revealed.The ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability.

An Updated Database and Spatial Distribution of Landslides Triggered by the Milin, Tibet M_(w)6.4 Earthquake of 18 November 2017

The M_(w)6.4 earthquake on November 18, 2017 in Milin County, Nyingchi City, Tibet triggered thousands of landslides. By comparing visual interpretation of satellite images acquired shortly before and after the earthquake and field survey, we have created a new landslide database which includes 3 130 coseismic landslides, each with an area of 0.01 to 4.35 km^(2). Six factors(elevation, slope angle, slope aspect, lithology, distance from the epicenter and distance from the seismogenic fault) were selected to correlate with the coseismic landslides. In addition, the area and density of landslides were counted as indicators. Results show that most landslides occurred where the elevation is between 2 000–3 000 m, with a 40°–50° slope angle and S, E or SE slope aspect, schist or gneiss lithologies, 10–15 km from the epicenter, and 5 km within the seismogenic fault. Most of the landslides, triggered by the M_(w)6.4 earthquake, are concentrated near the seismogenic fault rather than at the epicenter, indicating that the seismogenic structure is more influential than the location of the epicenter. Our findings may differ from other landslide database due to temporal image acquisition, interference from weather, and image resolution.

Landslides Triggered by the 3 August 2014 Ludian(China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution

The 3 August 2014 Ludian, Yunnan, China Mw 6.2(Ms 6.5) earthquake triggered a large number of coseismic landslides. Based on pre-and post-quake high-resolution optical satellite images, this study established a new, complete and objective database of these landslides with field investigations. The updated inventory shows that this earthquake triggered at least 12 817 landslides with a total occupation area of 16.33 km2, covering a nearly circular area about 600 km2, which all exceed those in our previous work and other relevant studies. In addition, we used this database to examine the correlations of the landslides with topographic, geologic, and seismic factors. Results show that the landslides occurred mostly at places with slope gradients 10o–40o, showing an increase tendency with steeper slopes. Affected by the propagation direction of the earthquake rupture, the eastward-facing slopes are more prone to landsliding. The differences between the landslide susceptibility in different strata indicate that lithology is also an important controlling factor. The landslide density of the places with peak ground acceleration(PGA) greater than 0.16 g is obviously larger than those with PGA less than 0.16 g. Meanwhile, the greater the distance from the epicenter, the lower the susceptibility of landslides is. This study suggests that when using satellite images to create coseismic landslide inventories, it should meet certain conditions, including high resolution, whole coverage, and timely data collection. The correct criteria of coseismic landslide inventorying also should be followed. Such inventories can provide a reliable basis for hazard assessment of earthquake-triggered landslides and other quantitative studies.

Preliminary identification of earthquake triggered multi-hazard and risk in Pleret Sub-District(Yogyakarta,Indonesia)

Yogyakarta is one of the large cities in Central Java,located on Java Island,Indonesia.The city,and the Pleret sub-district,where the study has taken place,is prone to earthquake hazards,because it is close to several seismically active zones,such as the Sunda Megathrust and the active fault known as the Opak Fault.Since a devastating earthquake of 2006,the population of the Pleret sub-district has increased significantly.Thus,the housing demand has increased,and so is the pace of low-cost housing that does not meet earthquake-safety requirements,and furthermore are often located on unstable slopes.The local alluvial material covering a jigsaw of unstable blocks and complex slope is conditions that can amplify the negative impacts of earthquakes.Within this context,this study is aiming to assess the multi-hazards and risks of earthquakes and related secondary hazards such as ground liquefaction,and coseismic landslides.To achieve this,we used geographic information systems and remote sensing methods supplemented with outcrop study and existing seismic data to derive shear-strain parameters.The results have revealed the presence of numerous uncharted active faults with movements visible from imagery and outcrops.show that the middle part of the study area has a complex geological structure,indicated by many unchartered faults in the outcrops.Using this newly mapped blocks combined with shear strain data,we reassessed the collapse probability of buildings that reach level>0.75 near the Opak River,in central Pleret sub-district.Classifying the buildings and from population distribution,we could determine that the highest risk was during nighttime as the buildings susceptible to fall are predominantly housing buildings.The secondary hazards follow a slightly different distribution with a concentration of risks in the West.