融合概率积分模型与D-InSAR的开采沉陷预计  

Prediction of Mining Subsidence Based on D-InSAR Combined with Probability Integral Model

作　　者：汪磊[1] 邓喀中[1] 薛继群 于德亮 

机构地区：[1]中国矿业大学环境与测绘学院,江苏徐州221116 [2]中化地质矿山总局浙江地质勘查院,浙江杭州310002 [3]兖州煤炭股份有限公司兴隆庄煤矿,山东济宁272102

出　　处：《金属矿山》2016年第2期160-163,共4页Metal Mine

基　　金：国家自然科学基金项目(编号:41071273);国土环境与灾害监测国家测绘地理信息局重点实验室开放基金项目(编号:LEDM2011B07);江苏高校优势学科建设工程基金项目(编号:SZBF2011-6-B35)

摘　　要：矿区地表植被多,开采沉陷速度快、量值大,所产生的地质灾害较一般性的地表沉陷严重,极易使得2景SAR影像失去相干性,造成解缠错误。针对矿区SAR影像相干性较低、下沉盆地中央相位值易丢失的情况,结合合成孔径雷达干涉差分技术(Differential interferometric synthetic aperture radar,D-In SAR)和基于遗传算法的概率积分模型,提出了一种矿区开采下沉盆地预计方法。以该方法利用矿区下沉盆地边缘一定数量的相干系数较高且下沉较明显的D-In SAR监测值和下沉盆地中央最大下沉点与拐点附近的少量观测值对某矿II3720工作面进行试验,首先利用概率积分模型反演概率积分法预计参数并采用遗传算法进行多次优化,然后利用得到的参数对该工作面下沉盆地进行模拟预计,结果表明:通过该方法得出的下沉盆地参数及下沉值与实测值较接近,有助于弥补由于矿区SAR影像干涉效果不佳而导致的预计精度不高的不足,通过少量的观测数据可较为有效地预计矿区下沉盆地,对于提高矿山开采沉陷监测与预计的精度有一定的参考价值。The vegetation is covered on the surface in mining area on a large scale,mining subsidence speed is fast and mining subsidence quantity is large, which generate the geological disasters is more serious than general surface subsidence, be- sides that,it is easy to make the two SAR images loss for coherence and phase unwrapping errors. In order to overcome the lim- itations of low coherence of SAR images in mining area and the missing of central phase of subsidence basin, combing with the differential interferometric synthetic aperture radar （D-InSAR） and the probability integral method based on genetic algorithm, a mining subsidence basin monitoring and predicting method is proposed. Some D-InSAR monitoring values that are obtained at the edge of the subsidence basin in mining area with the characteristics of high coherence and marked subsidence and a few monitoring values that are obtained near of the maximum subsidence points and inflection points in the mining basin in mining area are used to conducted the experiments of II3720 working face of a coal mine. To be specific, firstly, the probability integral model is used to conduct inversion the parameters of probability integral prediction method and they are optimized by genetic algorithm in a few times;then, the optimized parameters of probability integral method are adopted to predict the subsidence ba- sin in mining area. The experimental result show that the parameters and subsidence values of the subsidence basin in mining area obtained by the method proposed in this paper are consistent to the practical measured data basically, it is contributed to overcome the deficiencies of the low precise of the SAR images prediction results with poor interference effect,the subsidence basin in mining area can be predicted effectively based on a few measured data, the method proposed in this paper can provide reference for improving the precise of the mining subsidence monitoring and predicting.

关 键 词：开采沉陷 D-INSAR 遗传算法 概率积分法 下沉盆地 

分 类 号：TD325[矿业工程—矿井建设]