摘要
目前合成孔径雷达干涉(interfermoetic SAR,InSAR)理论与技术一直处于快速发展态势,在地质灾害隐患识别方面已有阶段性的进展,但是为了提高识别的效率与准确度还需进一步的研究。因此提出一种联合时序InSAR(Interfermoetic SAR)技术和滑坡灾害易发性的方法对云阳县进行滑坡灾害早期识别。该方法通过小基线集干涉测量(Small Baseline Subset InSAR,SBAS-InSAR)技术获得该地区地表形变信息,再结合通过信息量模型以地貌类型、土地利用类型、断层距离、高程、工程地质岩组、坡度、坡向、曲率与河流距离9个评价因子作为信息量值获得的滑坡灾害易发性区划与谷歌影像分层次进行潜在滑坡灾害的识别。然后对识别结果进行验证,研究表明了该种方法在此地区应用的可行性,且研究成果为地质灾害进一步的调查提供了参考与技术支持。
At present,the theory and technology of synthetic aperture radar(InSAR)have been developing rapidly,and progress has been made in the identification of hidden geological disasters.However,further research is needed to improve the efficiency and accuracy of the identification.Therefore,a method combining time-series InSAR(Interfermoetic SAR)technology and landslide disaster susceptibility is proposed for early landslide disaster identification in Yunyang County.In this method,the surface deformation information in this area is obtained by Small Baseline Subset InSAR(SBAS-InSAR)technology.Then,the potential landslide hazard is identified by combining the landslide hazard susceptibility zoning and Google image stratification obtained by the information model with nine evaluation factors,namely landform type,land use type,fault distance,elevation,engineering geological rock group,slope,slope aspect,curvature and river distance.Then the identification results are verified,the feasibility of this method in this area is shown,and the research results provide reference and technical support for the further investigation of geological disasters.
作者
马博
朱杰勇
刘帅
杨得虎
MA Bo;ZHU Jie-yong;LIU Shuai;YANG De-hu(Faculty of Land Resources Engineering,Kunming University of Technology,Kunming 650093,China)
出处
《地质灾害与环境保护》
2023年第3期8-14,共7页
Journal of Geological Hazards and Environment Preservation
关键词
滑坡灾害识别
SBAS-InSAR
滑坡灾害易发性
元阳
landslide disaster identification
SBAS-InSAR
landslide disasters susceptibility assessment
Yuanyang
作者简介
马博(1997-),男,现正攻读地质工程专业硕士学位,主要从事地质灾害研究。E-mall:1104123343@qq.com;通讯作者:朱杰勇(1961-),男,教授,硕士生导师,主要从事矿产普查与勘探、地质灾害、工程地质与水文地质研究。E-mail:zhujieyong@kmust.edu.cn。
