基于小基线亚子集干涉合成孔径雷达的软土路基沉降时间序列变形模型比较

Xuemin Xing, Hsing-Chung Chang, Lifu Chen, Zhihui Yuan
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摘要

软土路基上的道路和高速公路更容易发生沉陷和诱发失稳。因此,监测公路软土路基附近的长期地表变形对于了解沉降过程动力学和预防潜在危害至关重要。时间序列雷达干涉测量(InSAR)技术的变形估计精度受到时间变形模型的限制。本文通过对InSAR中常用的线性速度模型(LVM)、永久速度模型(PVM)、季节模型(SM)和三次多项式模型(CPM)四种时间序列变形模型进行比较,了解和评价路堤施工后的长期变形过程。为了评估和验证这四种选择的模型,使用小基线亚子集干涉合成孔径雷达(SBAS-InSAR)技术和TerraSAR-X卫星图像对伦贵高速公路(中国广东省软粘土路基上的典型高速公路)的模拟和真实变形数据进行了研究。使用模拟数据的场景表明,当使用奇异值分解(SVD)算法估计不同的变形系数时,四种模型都获得了满意的结果。而LVM模型在4种模型中准确率最低。这表明LVM由于其模型中的未知数数量较多,具有较高的估计误差。在实际数据实验中,采用3个精度指标分别测量高通变形的残差相位、平均时间相干性和均方根误差(RMSE)。结果表明,LVM和SM具有较好的性能。综上所述,SM更适合于软粘土路基公路的地表沉降建模与监测。
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A comparison of time series deformation models based on Small Baseline Subset Interferometric Synthetic Aperture Radar for soft clay subgrade settlement
Roads and highways built on soft clay subgrade are more prone to subsidence and induced instability. Therefore, monitoring long term surface deformation near the highways over soft clay subgrade is crucial for understanding the dynamics of the settlement process and prevent potential hazards. The precision of deformation estimation using time series radar interferometry (InSAR) techniques is restrained by the temporal deformation model. In this study, a comparison of four widely used time series deformation models in InSAR, namely Linear Velocity Model (LVM), Permanent Velocity Model (PVM), Seasonal Model (SM) and Cubic Polynomial Model (CPM), was conducted in order to understand and assess long term deformation process after constructing road embankment. To assess and validate these four selected models, both simulation and real deformation data over Lungui highway (a typical highway built on soft clay subgrade in Guangdong province, China) have been investigated using Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique with TerraSAR-X satellite imagery. The scenario using the simulated data showed all four models achieved satisfactory results when using Singular Value Decomposition (SVD) algorithm to estimate different deformation coefficients. However, LVM showed the least accuracy among the four models. This suggested LVM has higher estimation error due to its higher number of unknowns in the model. While in real data experiment, three precision indices were used to measure the residual phase, mean temporal coherence, and the root-mean-square-error (RMSE) of high-pass deformation, respectively. The results showed LVM and SM had better performance. In conclusion, SM is more suitable for the surface subsidence modeling and monitoring for highways built on soft clay subgrade in this case study.
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