基于DS-InSAR的矿区铁路线沉陷监测与规律分析

Deformation monitoring and law analysis of railway in mining area based on DS-InSAR

为了克服传统测量方法在获得矿区地表变形时成本较高等缺陷,实现矿区开采沉陷高效监测,采用DS-InSAR技术处理覆盖研究区域2018年11月至2019年11月间的30景Sentinel-1A卫星影像,通过最大似然估计迭代优化原始干涉图,并联合图像中的永久散射体和分布式散射体两类目标,获得矿区地表及铁路沿线的形变规律。结果表明:采用最大似然估计的DS-InSAR技术能有效优化原始干涉图的相位,克服失相干影响,从而显著提升高相干性像元数量数目与形变解算质量;监测结果显示,矿区铁路在工作面采动期间持续发生形变,最大沉降值为271mm,最大倾斜值为0.82mm/m;根据铁路沿线沉降结果,求得最优概率积分预计参数:q=0.79,tanβ=1.63,s=0,θ_(0)=79°。

In order to more efficiently monitor mining subsidence and overcome the high cost of traditional measurement methods to obtain the surface deformation of mining area, DS-InSAR technology is used to process 30 Sentinel-1 A satellite images of a mining area from November 2018 to November 2019. The original interferograms are optimized by maximum likelihood estimation, and the deformation law of mine surface and the railway is obtained combining the permanent scatterers and distributed scatterers. The results show that: DS-InSAR can effectively optimize the phase of original interferograms and overcome the influence of incoherence, so that the number of high coherence pixels and the quality of deformation calculation are significantly improved;the mining area railway continuously deforms during the mining period, with the maximum settlement value of 271 mm and the maximum tilt value of 0.82 mm/m;according to the monitoring settlement results along the railway, the optimal probability integral prediction parameters are obtained: q=0.79,tanβ=1.63,θ_(0)=79°.