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基于时序InSAR的广州典型沉降区特征及成因分析

Analysis on the characteristics and causes of typical land subsidence in Guangzhou by time-series InSAR
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摘要 广州作为珠三角地区重要经济中心,城市发展迅速,但广州南部地区由于垦海造田等历史原因,软土层分布广泛,近年来遭受地面沉降等地质灾害影响较为严重,已经影响到人民生命财产安全和社会经济发展。PS-InSAR时序技术作为一种新型技术手段,能够大范围高精度地监测地面沉降的发展,并有效预测地面沉降的发展趋势,为研究区地面沉降科学防治提供依据。本文运用PS-InSAR时序技术对广州市典型沉降区南沙2015-2020年的53景Sentinel-1A数据进行处理分析,并将已有的地面沉降自动化远程监测仪器同期结果与PS-InSAR监测结果进行对比。研究数据表明:PS-InSAR的分析结果较为准确,最大沉降速率超过50 mm/a,但整体上地面沉降速率呈减缓趋势,从成因上看地面沉降与地下水变化、软土固结以及人类活动关系密切。 Guangzhou is an important economic center in Pearl river delta region,with rapid urban development.However,due to historical reasons,such as land reclamation,soft soil layers are widely distributed in the southern part of Guangzhou.In recent years,it has been severely affected by geological disasters,such as ground subsidence,which has already affected people’s lives and property safety,as well as socio-economic development.PS-InSAR timing technology,as a new technological means,can monitor the development of land subsidence with high precision on a large scale and effectively predict the development trend of land subsidence,providing a basis for scientific prevention and control of land subsidence in the research area.In this paper,time-series PS-InSAR technology is used to process and analyze the Sentinel-1A data of 53 scenes from 2015 to 2020 in Nansha,a typical subsidence area in Guangzhou.At the same time,the existing automated remote monitoring instruments for land subsidence were compared with PS-InSAR monitoring results.The research data showed that PS-InSAR analysis results were relatively accurate,with a maximum subsidence rate exceeding 50 mm/a,but overall,the land subsidence rate showed a slowing trend.From a causal perspective,land subsidence is closely related to changes in groundwater,soft soil consolidation and human activities.
作者 孙嘉琪 古远 陈欣欣 祁明静 丁琛 SUN Jiaqi;GU Yuan;CHEN Xinxin;QI Mingjing;DING Chen(Guangzhou Geological survey,Guangzhou 510440,China)
出处 《地质装备》 2024年第5期43-48,共6页 Equipment for Geotechnical Engineering
基金 广州市地质灾害详细调查(1∶5万)(穗[2016]01)。
关键词 地面沉降 监测和预测 PS-INSAR Sentinel-1A land subsidence monitoring and prediction PS-InSAR Sentinel-1A
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