基于动态置信区间假设检验DS-InSAR技术的矿区形变监测研究

    Research on deformation monitoring in mining areas based on dynamic hypothesis testing of confidence interval DS-InSAR technology

    • 摘要: 煤炭资源的大范围开发会导致地表沉降、塌陷、水土流失等一系列地质灾害,为此,对矿区地表进行监测具有重要意义。分布式散射体合成孔径雷达干涉测量(DS-InSAR)技术中,同质点选取是重要的一步,会影响后续相位优化和分布式散射体目标的选择。而现有的同质点选取方法难以同时兼顾选取效率和准确度,为此提出了一种动态置信区间假设检验(动态HTCI)同质点选取方法,使用传统同质点选取方法和本文改进的方法进行同质点选取,利用蒙特卡罗模拟实验和真实实验证明了动态HTCI的有效性。基于2017—2018年共32景Sentinel-1数据对大同市某煤矿区地表形变进行了监测和分析,得到以下结论:①通过对比在同质区域和异质区域的同质点选取结果,相比于传统同质点选取方法,本文方法更适合地物区分度不明显的矿区同质点探测,提高了探测效率和选点准确度;②在监测时段内,该矿区共计发现12处明显的沉降漏斗,累计最大沉降为−157 mm;③改进的DS-InSAR技术的选点密度是永久散射体合成孔径雷达干涉测量(PS-InSAR)技术的9.47倍,与PS-InSAR结果的交叉验证分析证明了本文改进的DS-InSAR技术结果的准确性;④该方法为同质点选取步骤提供了新思路,并实现了对矿区的非接触、大范围的监测,可为矿区灾害的预防、治理等工作提供技术支持。

       

      Abstract: The large-scale development of coal resources can lead to a series of geological hazards, including ground subsidence, collapses, and soil erosion. Therefore, the surface monitoring of mining areas is important. In the context of Distributed Scatterers Interferometric Synthetic Aperture Radar (DS-InSAR) technology, the selection of homogeneous points is a crucial step that influences subsequent phase optimization and the choice of distributed scatterers targets. Existing methods for selecting of homogeneous points struggle to balance selection efficiency and accuracy. To address this, proposes a Dynamic Hypothesis Testing of Confidence Intervals (Dynamic HTCI) method for selecting homogeneous points. Using traditional homogeneous points selection methods and the improved method proposed in this paper for homogeneous points selection, and its effectiveness has been validated through Monte Carlo simulations and real-world experiments. Based on a total of 32 scenes of Sentinel-1data from 2017 to 2018, monitors and analyzes surface deformation in a coal mining area in Datong City. The results indicate the following conclusions: by comparing the results of selecting homogeneous points in homogeneous and heterogeneous regions, compared with traditional homogeneous point selection methods, this method is more suitable for detecting homogeneous points in mining areas with unclear land differentiation, improving both detection efficiency and selection accuracy. During the monitoring period, 12 significant subsidence funnels are identified in the mining area, with a maximum cumulative subsidence of −157 mm. The point selection density of the improved DS-InSAR method is 9.47 times that of Permanent Scatterer InSAR (PS-InSAR), and cross-validation with PS-InSAR results confirms the accuracy of improved DS-InSAR method. This method provides a new perspective for the process of homogeneous point selection and enables non-contact, large-scale monitoring of mining areas, offering technical support for disaster prevention and mitigation efforts in mining regions.

       

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