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Monitoring Subsidence Deformation of Suzhou Subway Using InSAR Timeseries Analysis

机译:使用INSAR时间序列分析监测苏州地铁的沉降变形

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Subway subsidence and the consequent geological hazards have become one of the major concerns especially in China’s large cities. Large spatial-scale and temporally continuous measurements of surface deformation due to subway subsidence is the basis of the intelligent management for urban transit, as well as the guarantee for the early warning of geological disasters. In this paper, we used persistent scatterer interferometric synthetic aperture radar (PS InSAR) measurements to study and characterize the deformation process resulted from subway-induced subsidence in the construction and operation periods of Suzhou subway using 24-scene high-resolution TerraSAR data (from December 2009 to April 2015) and 19-scene low-resolution Sentinel-1A SAR data (from May 2017 to February 2020). Our results indicate spatially different subsidence rates at different segments along the Suzhou subway lines 1, 2, 3, 4, 5 and 6. TerraSAR data indicate the maximum subsidence rate of −13 mm/yr, while Sentinel-1A data demonstrate the maximum subsidence rate of −20 mm/yr. The decadal-scale subway subsidence deformation is generally characterized by its multistage dynamic processes. The significant subway subsidence is mainly distributed around interchange hubs at the intersection area of railway lines. The Peck function model is used to fit the subway subsidence displacement profiles, which reveals the normal distribution characteristics of the subway surface subsidence. Based on our timeseries analysis in different time periods, the logistic cycle function is used to invert the subway subsidence timing, which reveals four first-order stages during subway subsidence: slow acceleration period, fast acceleration period, decay period and steady-state period.
机译:地铁沉降和随之而来的地质灾害已成为特别是中国大城市的主要问题之一。由于地铁沉降引起的大型空间尺度和时间延续的表面变形测量是城市过境智能管理的基础,以及地质灾害的早期警告的保证。在本文中,我们使用了持久散射者干涉性合成孔径雷达(PS INSAR)测量来研究和表征由24场景高分辨率特派数据(来自2009年12月至2015年4月)和19场比赛的低分辨率Sentinel-1A SAR数据(从2017年5月到2020年2月)。我们的结果表明了沿苏州地铁1,2,3,4,5和6的不同段的空间不同的沉降速率。地区的地区数据指示了-13 mm / yr的最大沉降率,而Sentinel-1a数据显示最大沉降-20 mm / yr的速率。 Decadal-Scale地铁沉降变形通常是其多级动态过程的特征。重要地铁沉降主要分布在铁路线交叉区的交汇中心周围。 PECK功能模型用于拟合地铁沉降位移型材,揭示地铁表面沉降的正常分布特性。基于我们在不同时间段的时期分析,逻辑周期函数用于反转地铁沉降时机,该沉降时间在地铁沉降期间显示四个一阶阶段:缓慢加速周期,快速加速度,衰减周期和稳态周期。

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