Great earthquakes in mountain areas always trigger severe geologic hazards such as landslides, debris flows and rock falls, thereby causing tremendous property damage and casualties. On 19th June, 1781, a Ms 7.5 earth...Great earthquakes in mountain areas always trigger severe geologic hazards such as landslides, debris flows and rock falls, thereby causing tremendous property damage and casualties. On 19th June, 1781, a Ms 7.5 earthquake occurred in Tongwei of Pan'an, Gansu Province, west China,展开更多
The identification of large-giant bedrock landslides triggered by earthquake aims to the landslide prevention and control. Previous studies have described the basic characteristics, distribution, and the formation mec...The identification of large-giant bedrock landslides triggered by earthquake aims to the landslide prevention and control. Previous studies have described the basic characteristics, distribution, and the formation mechanism of seismic landslides (Bijan Khazai et al., 2003; Chong Xu et al., 2013; Lewis a. Owen et al., 2008; Randall W. Jibson et al., 2006). However, few researches have focused on the early identification indicators of large-giant bedrock landslides triggered by earthquake (David k. Keefer., 1984; Janusz Wasowski et al., 2011; Alexander L.Strom., 2009; Patrick Meunier et al., 2008; Shahriar Vahdani et al., 2002; Bijan Khazai et al., 2003). This paper presents the identification indicators of large-giant bedrock landslides triggered by earthquake in the Longmenshan tectonic belt on the basic of their characteristics, distribution and the relationship between seismic landslides and the peak ground motion acceleration.展开更多
In this paper, we focus on the characteristics of the landslides developed in the epicentral area of AD 1556 M^8.5 Huaxian Earthquake, and discuss their relations to the active normal faults in the SE Weihe Graben, Ce...In this paper, we focus on the characteristics of the landslides developed in the epicentral area of AD 1556 M^8.5 Huaxian Earthquake, and discuss their relations to the active normal faults in the SE Weihe Graben, Central China. The results from analyzing high-resolution remote-sensing imagery and digital elevation models(DEMs), in combination with field survey, demonstrate that:(i) the landslides observed in the study area range from small-scale debris/rock falls to large-scale rock avalanches;(ii) the landslides are mostly developed upon steep slopes of ≥30°; and(iii) the step-like normal-fault scarps along the range-fronts of the Huashan Mountains as well as the thick loess sediments in the Weinan area may facilitate the occurrence of large landslides. The results presented in this study would be helpful to assess the potential landslide hazards in densely-populated areas affected by active normal faulting.展开更多
In this study,Bayesian probability method and machine learning model are used to study the real occurrence probability of earthquake-induced landslide risk in Taiwan region.The analyses were based on the 1999 Taiwan C...In this study,Bayesian probability method and machine learning model are used to study the real occurrence probability of earthquake-induced landslide risk in Taiwan region.The analyses were based on the 1999 Taiwan Chi-Chi Earthquake,the largest earthquake in the history in this Region in a hundred years,thus can provide better control on the prediction accuracy of the model.This seismic event has detailed and complete seismic landslide inventories identified by polygons,including 9272 seismic landslide records.Taking into account the real earthquake landslide occurrence area,the difference in landslide area and the non-sliding/sliding sample ratios and other factors,a total of 13,656,000 model training samples were selected.We also considered other seismic landslide influencing factors,including elevation,slope,aspect,topographic wetness index,lithology,distance to fault,peak ground acceleration and rainfall.Bayesian probability method and machine learning model were combined to establish the multi-factor influence of earthquake landslide occurrence model.The model is then applied to the whole Taiwan region using different ground motion peak accelerations(from 0.1 g to 1.0 g with 0.1 g intervals)as a triggering factor to complete the real probability of earthquake landslide map in Taiwan under different peak ground accelerations,and the functional relationship between different Peak Ground Acceleration and their predicted area is obtained.展开更多
基金support of China Geological Survey(Project No.12120114035901)NSFC(Award No.41472296 and No.41372374)
文摘Great earthquakes in mountain areas always trigger severe geologic hazards such as landslides, debris flows and rock falls, thereby causing tremendous property damage and casualties. On 19th June, 1781, a Ms 7.5 earthquake occurred in Tongwei of Pan'an, Gansu Province, west China,
基金financially supported by the Geological Survey Project of China Geological Survey (grant no.1212011014032,1212011220134)
文摘The identification of large-giant bedrock landslides triggered by earthquake aims to the landslide prevention and control. Previous studies have described the basic characteristics, distribution, and the formation mechanism of seismic landslides (Bijan Khazai et al., 2003; Chong Xu et al., 2013; Lewis a. Owen et al., 2008; Randall W. Jibson et al., 2006). However, few researches have focused on the early identification indicators of large-giant bedrock landslides triggered by earthquake (David k. Keefer., 1984; Janusz Wasowski et al., 2011; Alexander L.Strom., 2009; Patrick Meunier et al., 2008; Shahriar Vahdani et al., 2002; Bijan Khazai et al., 2003). This paper presents the identification indicators of large-giant bedrock landslides triggered by earthquake in the Longmenshan tectonic belt on the basic of their characteristics, distribution and the relationship between seismic landslides and the peak ground motion acceleration.
基金supported by the National Natural Science Foundation of China (No. 41502203)the Scientific Research Foundation for Returned Overseas Scholars of China (awarded to G. Rao)+1 种基金the Natural Science Foundation of Zhejiang Province (No. LY15D02001)a Science Project (No. 23253002)from the Ministry of Education, Culture, Sports, Science and Technology of Japan
文摘In this paper, we focus on the characteristics of the landslides developed in the epicentral area of AD 1556 M^8.5 Huaxian Earthquake, and discuss their relations to the active normal faults in the SE Weihe Graben, Central China. The results from analyzing high-resolution remote-sensing imagery and digital elevation models(DEMs), in combination with field survey, demonstrate that:(i) the landslides observed in the study area range from small-scale debris/rock falls to large-scale rock avalanches;(ii) the landslides are mostly developed upon steep slopes of ≥30°; and(iii) the step-like normal-fault scarps along the range-fronts of the Huashan Mountains as well as the thick loess sediments in the Weinan area may facilitate the occurrence of large landslides. The results presented in this study would be helpful to assess the potential landslide hazards in densely-populated areas affected by active normal faulting.
基金supported by the National Key Research and Development Program of China(2018YFC1504703)。
文摘In this study,Bayesian probability method and machine learning model are used to study the real occurrence probability of earthquake-induced landslide risk in Taiwan region.The analyses were based on the 1999 Taiwan Chi-Chi Earthquake,the largest earthquake in the history in this Region in a hundred years,thus can provide better control on the prediction accuracy of the model.This seismic event has detailed and complete seismic landslide inventories identified by polygons,including 9272 seismic landslide records.Taking into account the real earthquake landslide occurrence area,the difference in landslide area and the non-sliding/sliding sample ratios and other factors,a total of 13,656,000 model training samples were selected.We also considered other seismic landslide influencing factors,including elevation,slope,aspect,topographic wetness index,lithology,distance to fault,peak ground acceleration and rainfall.Bayesian probability method and machine learning model were combined to establish the multi-factor influence of earthquake landslide occurrence model.The model is then applied to the whole Taiwan region using different ground motion peak accelerations(from 0.1 g to 1.0 g with 0.1 g intervals)as a triggering factor to complete the real probability of earthquake landslide map in Taiwan under different peak ground accelerations,and the functional relationship between different Peak Ground Acceleration and their predicted area is obtained.