西南交通大学学报2026，Vol.61Issue(1)：31-40,10.DOI:10.3969/j.issn.0258-2724.20240041
甘肃积石山Ms6.2级地震诱发滑坡快速评估
Rapid Assessment of Landslides Induced by Jishishan Ms6.2 Earthquake in Gansu Province
摘要
Abstract
Rapidly obtaining co-seismic landslide distribution and conducting disaster assessments after earthquakes are vital for effective emergency relief and reconstruction.Therefore,in this study,the InSAR data-Newmark physical fusion driver model(IDNPM)was used to rapidly assess the landslides triggered by the earthquake in Jishishan,Gansu Province on December 18,2023,with a view to quickly and accurately grasping the macroscopic distribution of landslide hazards.Firstly,through the time series satellite-based augmentation system(SBAS)-InSAR,it was revealed that there was serious gully development and retrogressive erosion in this area.These geological characteristics provided a favorable breeding environment for landslides.Secondly,the IDNPM was used to quickly evaluate the landslide of Jishishan earthquake,and it was predicted that the steep slopes and gully sides of Zhaomuchuan Village,Tashapo Village,and Dahejia Town were the high-risk areas for earthquake-induced landslide.Finally,based on the field investigation,numerical simulation,and satellite identification technology,the reliability of the model in practical application was proven.The results indicate that a total of 2.657%of the region is at high risk.There is a need to focus on such zones by urgently clearing and stabilizing slopes where landslides have occurred.For areas where no landslides have occurred,monitoring and assessment measures should be taken to guard against possible post-earthquake secondary landslide events.The research results can provide strong data support for emergency relief and reconstruction work after earthquakes in the affected areas.关键词
地震/滑坡/应急评估/预测模型/InSAR技术Key words
earthquake/landslide/emergency assessment/prediction model/InSAR technology分类
天文与地球科学引用本文复制引用
曾营,张迎宾,储峰,柳静,冯振海,苏金蓉..甘肃积石山Ms6.2级地震诱发滑坡快速评估[J].西南交通大学学报,2026,61(1):31-40,10.基金项目
国家自然科学基金项目(52378370,52278372) 感谢国家青年拔尖人才万人计划、中国路桥总公司(P2220447)以及所有为本研究提供数据支持的单位. （52378370,52278372）
