强震山区地震诱发滑坡发育规律与易发性评估  

Study on Development Patterns and Susceptibility Evaluation of Coseismic Landslides within Mountainous Regions Influenced by Strong Earthquakes

作　　者：李永威 徐林荣[1,2] 张亮亮 陆志强 苏娜 Li Yongwei;Xu Linrong;Zhang Liangliang;Lu Zhiqiang;Su Na(School of Civil Engineering,Central South University,Changsha 410075,China;National Engineering Research Center of High‐Speed Railway Construction Technology,Central South University,Changsha 410075,China)

机构地区：[1]中南大学土木工程学院,湖南长沙410075 [2]中南大学高速铁路建造技术国家工程研究中心,湖南长沙410075

出　　处：《地球科学》2023年第5期1960-1976,共17页Earth Science

基　　金：国家自然科学面上项目(No.42172322);国家自然科学基金项目(No.42007419);国家重点研发计划项目(No.2018YFC1505403);湖南省自然科学基金项目(No.2020JJ5981);湖南省教育厅科学研究项目优秀青年基金项目(No.21B0226);中南大学研究生自主探索创新项目(No.2022ZZTS0646).

摘　　要：强震山区地形陡峭,植被茂盛,使同震滑坡“点多面广”,难以探测,为灾害防控带来困难.滑坡易发性评估能够预测灾害空间分布.但传统评估方法存在数据源有限、数据量化标准不一等问题,难以获取准确的易发性评价结果及难以掌握复杂孕灾环境下滑坡发育特征.鉴于此,通过多源监测数据、空间分析和深度学习方法,分析同震滑坡的发育规律,探究滑坡的地震响应机制,并进行滑坡易发性区划.结果表明:地震通过影响地形地貌的应力场及岩土体结构对地震波的地震响应的作用,使同震滑坡表现不同形式的发灾效应(如锁固段效应、微地形效应和地层倾向效应等);采用基于卷积神经网络(CNN)和深度神经网络(DNN)的深度学习模型取得了良好的易发性评价结果(AUC值分别为0.901和0.865),CNN模型的预测性能优于DNN模型.两模型精度都较高,均能较为准确识别潜在的滑坡区域;极高和高滑坡易发性区域广泛分布于丹祖沟等13条沟道中,这些沟道在暴雨下更容易发生泥石流.Coseismic landslides are imperceptible and widely distributed in the mountainous regions with abundant vegetation influenced by strong earthquakes,which hinders the development of prevention and control for disaster.Landslide susceptibility evaluation can predict the landslide-prone areas.But few studies have obtained the high-precision landslide susceptibility maps,and it was difficult to uncover the patterns of coseismic landslides under the complex geo-environment,because the lack of data and there was no unified standards for quantification of data based on traditional landslide susceptibility evaluation method.Therefore,the multi-source monitoring data,spatial analysis and deep learning method were adopted in this work to find out the development patterns,seismic response mechanism of landslide and to obtain landslide susceptibility maps.The main conclusions are as follows:the seismic response mechanism of landslide was proposed based on the seismic response analysis of landform and rock structure.The CNN (convolutional neural network) and DNN (deep neural network) achieved the relatively good area under the curve (AUC)value (AUC were 0.901 and 0.865, respectively), which could obtain the high-precision landslide susceptibility maps.Extremely high and high susceptibility region of landslides were widely distributed in 13 gullies, such as Danzu gully and soon. And these gullies were more prone to debris flow under rainstorm.

关 键 词：发育规律 深度学习 滑坡易发性 发灾效应 灾害地质. 

分 类 号：P694[天文地球—地质学]