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利用SBAS-InSAR分析武汉地铁沿线地表形变特征

Surface Deformation Characteristic Analysis Method Along Wuhan Subway Lines Using SBAS-InSAR Technology
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摘要 轨道交通安全运行需要全线路高精度的形变监测技术支持。采用小基线数据集干涉合成孔径雷达(SBAS-InSAR)技术对2015—2023年231景Sentinel-1数据进行分析,获取了武汉市主要地铁沿线区域的长时序地面沉降形变信息,并结合地理加权回归(GWR)模型等地理分析方法分析了沉降的时空特征。结果表明,地表沉降和地铁建设运营存在一定的时空关联性,在空间上地铁6、7、8号线沿线均存在不同程度的沉降漏斗,沉降速率极值高于25 mm/a;在时间上不同区域地铁沿线的监测点均受到地铁施工建设的显著影响,2015—2017年高速沉降。研究结果体现了SBAS-InSAR技术在保障基础设施安全方面的有效性,可为地铁长期运营与维护提供理论依据与技术支持。 Safe operation of rail transit requires the support of high-precision deformation monitoring technology across the entire line.In this paper,we used the small baseline subset interferometric synthetic aperture radar(SBAS-InSAR)technique to analyze 231 scenes of Sentinel-1 data from 2015 to 2023,and obtained long-term surface deformation information along the main subway lines in Wuhan City.Combined with geographical analysis methods such as the geographically weighted regression(GWR)model,we analyzed the spatio-temporal characteristics of surface subsidence.The results show that there is a certain spatio-temporal correlation between surface subsidence and subway construction and operation.Spatially,varying degrees of subsidence funnels exist along the subway lines 6,7 and 8,with extreme subsidence speed exceeding 25 mm/a.Temporally,monitoring points along subway lines in different areas are significantly affected by subway construction,exhibiting rapid subsidence from 2015 to 2017.This result demonstrates the effectiveness of SBAS-InSAR technology in ensuring infrastructure safety,and provides a theoretical basis and technical support for the long-term operation and maintenance of subways.
作者 黄昊龙 史绪国 李梓龙 HUANG Haolong;SHI Xuguo;LI Zilong(S.K.Lee Honors College,China University of Geosciences(Wuhan),Wuhan 430074,China;School of Geography and Information Engineering,China University of Geosciences(Wuhan),Wuhan 430078,China)
出处 《地理空间信息》 2025年第7期35-40,共6页 Geospatial Information
基金 国家自然科学基金资助项目(42474061)。
关键词 武汉地铁 SBAS-InSAR 形变监测 地铁沉降 GWR模型 Wuhan subway SBAS-InSAR deformation monitoring subway subsidence GWR model
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