利用稳健的多时相 InSAR 方法和 Logistic 模型测量采矿相关的沉降量

Peifeng Ma;Chang Yu;Zherong Wu;Zhanze Wang;Jiehong Chen
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引用次数: 0

摘要

地面沉降是矿区具有代表性的地质灾害,威胁着人类安全和基础设施。干涉合成孔径雷达(InSAR)被用来测量与采矿活动有关的地面沉降。然而,矿区往往存在严重的时间和几何相关性问题,导致持久散射体(PS)稀疏,测量精度较低。为了改进形变测量,本文采用了一种稳健的多时空 InSAR(MT-InSAR)方法,在两层网络中联合探测持久散射体和分布式散射体(DSs)。为了解决传统线性速度模型中的不匹配问题,在 MT-InSAR 处理中引入了逻辑模型。我们利用在 2020 年 1 月 1 日至 2021 年 6 月 30 日期间获取的 44 幅 Sentinel-1A SAR 图像测量了中国河北省承德市周台子村的地面沉降情况,该地区因采矿活动诱发和加剧了地质灾害。我们观察到,在采矿区,使用逻辑模型(11 607 个)与恒速模型(10 980 个)相比,产生了更多的测量点,增加了 5.7%,而估计残差的标准偏差均值从 1.45 降至 1.13,减少了 22%。这些结果有利于矿区地质灾害的评估和管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining-Related Subsidence Measurements Using a Robust Multitemporal InSAR Method and Logistic Model
Ground subsidence is a representative geohazard in mining areas that threatens human safety and infrastructure. Interferometric synthetic aperture radar (InSAR) was used to measure ground subsidence related to mining activities. However, mining areas are often subjected to severe temporal and geometric decorrelation problems, resulting in sparse persistent scatterers (PSs) and lower measurement accuracy. To improve deformation measurements, a robust multitemporal InSAR (MT-InSAR) method that jointly detects PS and distributed scatterers (DSs) in a two-tier network was utilized here. To solve the mismatch in the traditional linear velocity model, a logistic model was introduced for MT-InSAR processing. Forty-four Sentinel-1A SAR images acquired between 1 January 2020 and 30 June 2021 were used to measure ground subsidence in Zhoutaizi Village, Chengde City, Hebei Province, China, which endured geohazards induced and exacerbated by mining activities. We observed that more measurement points were produced using the logistic model (11 607) compared with the constant velocity model (10 980) in the mining areas with an increase of 5.7%, while the mean value of the standard deviation of the estimated residuals reduced from 1.45 to 1.13 with a decrease of 22%. Results are beneficial for geohazard assessment and management in mining areas.
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