摘要
2022年9月5日四川甘孜泸定县发生6.8级地震,诱发了大量地质灾害,造成房屋损毁和多处道路阻断,并导致了严重的人员伤亡。快速预测地震诱发地质灾害空间分布对震后应急救援至关重要。为此,成都理工大学地质灾害防治与地质环境保护国家重点实验室利用已建立的地震诱发滑坡近实时预测模型,在震后2 h内,快速预测了地震诱发滑坡空间分布概率。同时,利用震后重点区域的无人机影像和国产高分六号影像,对地震诱发滑坡进行了智能识别和人工解译及现场调查复核,共解译滑坡3633处,总面积13.78 km^(2)。研究发现本次泸定地震诱发滑坡,较2008年汶川和2017年九寨沟地震滑坡,规模相对较小。本次地震诱发滑坡主要分布于鲜水河断裂带和大渡河两侧,呈带状分布,在磨西镇、得妥镇及王岗坪彝族藏族乡等Ⅸ度烈度区相对集中。对控制滑坡空间分布的地形地貌、地质和地震3类因素9个因子进行分析,发现其主要分布在坡度35°~55°、高程1000~1800 m范围内;受断层控制强烈,主要分布在距断层1 km范围内;在花岗岩中最为发育。上述研究成果获得的地震诱发滑坡及受损道路和房屋分布情况,为震后应急救援提供了重要支撑。
A M_(S)6.8 earthquake struck Luding County,Ganzi Tibetan Autonomous Prefecture,Sichuan Province,China on September 5,2022.The earthquake triggered large amounts of geological hazards,such as landslides,rockfalls,and debris flows,and caused severe fatalities and infrastructure damages.Utilizing the recently developed near real-time prediction model,we rapidly predicted the spatial distribution probability of the coseismic landslides in the region within two hours after the earthquake.Meanwhile,based on the high-resolution imagery from the Unmanned Aerial Vehicles(UAVs)and the Gaofen-6 Satellite,we interpreted a total of 3,633 coseismic landslides in the region with an area of 13.78 km^(2).We then validated the results based on the field investigation.The results indicate that the coseismic landslides induced by the Luding earthquake are smaller than those triggered by the 2008 Wenchuan earthquake and the 2017 Jiuzhaigou earthquake.The coseismic landslides,which show a banded distribution pattern,mainly located on the sides of the Xianshuihe fault zone and the Dadu River and concentrate in the areas with a shaking intensity ofⅨdegrees,such as Moxi Town,Detuo Town,and Wanggangping Town.We further analyzed nine controlling factors of the coseismic landslides,such as topography,lithology,ground motion parameters,et al.We find that landslides mainly occurred in the regions with an elevation range of 1000~1800 m and a slope range of 35°~55°.Most coseismic landslides are distributed within 1 km of the fault zone and in the granite formation,which highlights the strong impact of the fault zone.This study's results provide essential support to emergency response and risk mitigation.
作者
范宣梅
王欣
戴岚欣
方成勇
邓宇
邹城彬
汤明高
魏振磊
窦向阳
张静
杨帆
陈兰
魏涛
杨银双
张欣欣
夏明垚
倪涛
唐小川
李为乐
戴可人
董秀军
许强
FAN Xuanmei;WANG Xin;DAI Lanxin;FANG Chengyong;DENG Yu;ZOU Chengbin;TANG Minggao;WEI Zhenlei;DOU Xiangyang;ZHANG Jing;YANG Fan;CHEN Lan;WEI Tao;YANG Yinshuang;ZHANG Xinxin;XIA Mingyao;NI Tao;TANG Xiaochuan;LI Weile;DAI Keren;DONG Xiujun;XU Qiang(State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu 610059,China)
出处
《工程地质学报》
CSCD
北大核心
2022年第5期1504-1516,共13页
Journal of Engineering Geology
基金
国家杰出青年科学基金(资助号:42125702).
关键词
泸定地震
地质灾害
同震滑坡
机器学习
预测模型
Luding Earthquake
Geological hazard
Coseismic landslide
Machine learning
Prediction model