首页|基于PS-InSAR技术的晋城矿区地表形变监测及地质灾害风险预警

基于PS-InSAR技术的晋城矿区地表形变监测及地质灾害风险预警

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地表形变是一种严重的地质灾害现象,不仅严重影响灾害区居民的日常生活,而且会造成巨大的社会经济危害,尤其在采煤区.针对传统地表沉陷监测方法费时费力、无法获取地表沉降面状信息、难以进行地表沉陷灾害评估的不足,基于高分辨率SAR卫星影像,利用永久散射体合成孔径雷达干涉测量(PS-InSAR)技术对山西省晋城市晋城矿区2018年1月至2018年12月期间地表沉陷进行监测,分析获取了该地区地表连续形变情况,并利用该技术获取的海量PS点建立支持向量机(SVM)地质灾害风险评估预警模型,对晋城矿区周边居民点地质灾害风险进行了识别和预测.结果表明:晋城矿区10个煤矿及其周边区域存在较大的地表形变;晋城矿区平均LOS向年平均地表形变速率范围为-37~30.3 mm/a;PS-InSAR技术在晋城矿区地表形变监测中具有可行性,且可以实现矿区地质灾害风险综合识别和预警.
Surface deformation monitoring and geological hazard risk early warning of Jincheng mining area based on PS-InSAR technology
Surface deformation not only affects the normal livings of local residents,but also causes social and economic losses,especially in coal mining areas.The traditional subsidence monitoring method is time-consuming and laborious,and the subsidence surface information cannot be obtained,making it difficult to evaluate the subsidence hazard.In this paper,based on the influence of high-resolution SAR satellites,per-manent scatterer synthetic aperture radar interferometry(PS-InSAR)was used to monitor the surface sub-sidence of Jincheng Mine in Jincheng City,Shanxi Province from January,2018 to December,2018,and ana-lyze the three-dimensional continuous surface deformation of the area.The results show that the 10 coal mines in Jincheng mining area and their surrounding areas have large three-dimensional surface deforma-tion.The annual average surface deformation rate in the LOS direction in Jincheng mining area ranges from-37 mm/a to 30.3 mm/a.In addition,the support vector machine(SVM)risk assessment and prediction model was established based on the massive PS points obtained by this technology,and the geological haz-ard risk points of residential areas around Jincheng mining area were identified and predicted.The research shows that PS-InSAR technology is feasible to monitor surface deformation and realize comprehensive iden-tification and early warning of geological disasters in mining areas.

PS-InSAR technologyJincheng mining areasurface deformation monitoringgeological hazard risk early warningsupport vector machine(SVM)

王新龙、车子杰、马飞、高旭波

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长治学院计算机系,山西长治 046011

合肥工业大学土木与水利工程学院,安徽 合肥 230003

中国地质大学(武汉)环境学院,湖北 武汉 430078

PS-InSAR技术 晋城矿区 地表形变监测 地质灾害风险预警 支持向量机

山西省基础研究计划项目

202103021223381

2024

安全与环境工程
中国地质大学

安全与环境工程

CSTPCD北大核心
影响因子:1.03
ISSN:1671-1556
年,卷(期):2024.31(2)
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