摘要
2022年9月5日四川省泸定县发生M_(w)6.6级地震,诱发了大量的山体滑坡、崩塌等次生灾害,造成了严重的人员伤亡与经济损失。快速获取灾区滑坡易发性和同震滑坡的空间分布对灾情评估尤为重要。首先,联合震前高分辨率光学遥感影像解译和GACOS辅助下的InSAR Stacking技术探测灾区震前滑坡;然后,基于该滑坡数据利用机器学习算法获得震中附近滑坡的空间易发性图;同时,通过使用震前、震后的多源光学遥感影像建立了同震滑坡编目(2692处滑坡),并采用空间统计法分析了此次地震诱发的滑坡与地形、地震和地质因素之间的关系。结果表明:易发性图中,中等及以上易发区的同震滑坡面积占滑坡总面积(47 km^(2))的70.2%;同震滑坡主要分布在高程1200~2400 m、坡度35°~50°、距震中4~20 km、距断层1 km范围内和砂岩板岩岩性中,并至少导致10.34 km道路受损。该研究可为灾区地质灾害排查和防治提供有力的数据支撑。
On 5 September 2022,an M_(w)6.6 earthquake struck Luding county,Sichuan province,China,which triggered a large number of geohazards such as landslides and collapses,leading to serious casualties and economic losses.Rapid access to landslide susceptibility and the actual distribution maps of coseismic landslides are critical for disaster management.In this study,we combined optical remote sensing image interpretation with the Generic Atmospheric Correction Online Service(GACOS)assisted InSAR stacking technique to map pre-event landslides,and then used the detected landslide dataset to generate a landslide susceptibility map in the earthquake-affected area using a machine learning algorithm.In addition,we used multi-source optical remote sensing images to establish an inventory of 2692 coseismic landslides and analyze its relationships with topography,seismic and geological factors.Our results showed that 70.2%of the total coseismic landslide area(47 km^(2))were within the areas with moderate or higher susceptibility.These coseismic landslides were mainly distributed in elevations of 1200-2400 m,with slopes of 35°-50°,4-20 km from the epicenter,within 1 km of faults and with a lithology of sericites silt slate.The coseismic landslides also damaged at least 10.34 km of roads.It is believed that this research can provide data support for the assessment and prevention of geohazards in the earthquake-affected areas.
作者
陈博
李振洪
黄武彪
刘振江
张成龙
杜建涛
宋闯
丁明涛
朱武
张双成
王建伟
彭建兵
CHEN Bo;LI Zhen-hong;HUANG Wu-biao;LIU Zhen-jiang;ZHANG Cheng-long;DU Jian-tao;SONG Chuang;DING Ming-tao;ZHU Wu;ZHANG Shuang-cheng;WANG Jian-wei;PENG Jian-bing(School of Geological Engineering and Geomatics,Chang an University,Xi'an 710054,Shaanxi,China;Big Data Center for Geosciences and Satellites,Chang an University,Xi'an 710054,Shaanxi,China;Key Laboratory of Western China s Mineral Resources and Geological Engineering of Ministry of Education,Chang an University,Xi'an 710054,Shaanxi,China;Key Laboratory of Ecological Geology and Disaster Prevention of Ministry of Natural Resources,Chang an University,Xi'an 710054,Shaanxi,China;College of Transportation Engineering,Chang an University,Xi'an 710064,Shaanxi,China;Engineering Research Center of Highway Infrastructure Digitalization of Ministry of Education,Chang an University,Xi'an 710064,Shaanxi,China)
出处
《地球科学与环境学报》
CAS
北大核心
2022年第6期971-985,共15页
Journal of Earth Sciences and Environment
基金
国家重点研发计划项目(2020YFC1512000)
陕西省科技创新团队项目(2021TD-51)
陕西省地学大数据与地质灾害防治创新团队项目(2022)
中央高校基本科研业务费专项资金项目(300102260301,300102262902,300102261108,300203211261)。
关键词
泸定地震
同震滑坡
光学遥感
INSAR
目视解译
滑坡易发性
空间分布
影响因素
Luding earthquake
coseismic landslide
optical remote sensing
InSAR
visual interpretation
landslide susceptibility
spatial distribution
influencing factor
