甘肃积石山Ms6.2级地震诱发滑坡快速评估OA
Rapid Assessment of Landslides Induced by Jishishan Ms6.2 Earthquake in Gansu Province
震后迅速获取同震滑坡分布及灾情评估对于应急救援和重建工作至关重要,采用IDNPM(InSAR data-newmark physical fusion driver model)方法对 2023年 12月 18日甘肃积石山地震引发的滑坡进行快速评估,以期迅速精准掌握滑坡灾害的宏观分布.首先,通过时序星基增强系统(SBAS)-InSAR揭示该地区有着严重的冲沟发育和溯源侵蚀现象,这些地质特征为滑坡提供了有利的孕育环境;然后,运用IDNPM方法对积石山地震进行滑坡快速评估,预测出赵木川村、塔沙坡村、大河家镇等地的陡峭斜坡及沟壑两侧为地震诱发滑坡的高风险区域;最后,综合实地考察、数值模拟及卫星识别技术,验证该模型在实际应用中的可靠性.结果表明:全区共有 2.657%的高风险区,需要重点关注此类区域;对已发生崩滑的坡体紧急清理和加固,对于未发生滑移的区域,应采取监测和评估措施,以防范可能发生的震后次生滑坡事件;研究成果可为受灾区的灾后应急救援和恢复重建工作提供有力的数据支撑.
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.
曾营;张迎宾;储峰;柳静;冯振海;苏金蓉
西南交通大学土木工程学院,四川 成都 610031西南交通大学土木工程学院,四川 成都 610031中国路桥工程有限责任公司,北京 100011西南交通大学土木工程学院,四川 成都 610031西南交通大学土木工程学院,四川 成都 610031四川省地震局,四川 成都 610041
天文与地球科学
地震滑坡应急评估预测模型InSAR技术
earthquakelandslideemergency assessmentprediction modelInSAR technology
《西南交通大学学报》 2026 (1)
31-40,10
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.
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