陶秋香,,
刘国林,
王路遥,
王凤云,
王珂
山东科技大学 测绘与空间信息学院, 山东青岛 266590
基金项目: 国家自然科学基金项目（42074009，41404003），山东省自然科学基金项目（ZR2020MD044）资助
详细信息
作者简介: 陈洋, 女, 1995年生, 在读博士研究生, 主要从事InSAR数据处理与算法研究.E-mail: 15969682321@163.com
通讯作者: 陶秋香, 女, 1977年生, 博士, 副教授, 主要从事InSAR理论、方法及关键技术的研究.E-mail: qiuxiangtao@163.com
中图分类号: P631收稿日期:2020-11-03
修回日期:2021-08-25
上线日期:2021-10-10
Detailed mining subsidence monitoring combined with InSAR and probability integral method
CHEN Yang,TAO QiuXiang,,
LIU GuoLin,
WANG LuYao,
WANG FengYun,
WANG Ke
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao Shandong 266590, China
More Information
Corresponding author: TAO QiuXiang,E-mail:qiuxiangtao@163.com
MSC: P631--> Received Date: 03 November 2020
Revised Date: 25 August 2021
Available Online: 10 October 2021
摘要
摘要:结合InSAR与概率积分法的优势，提出一种InSAR和概率积分法联合进行矿区地表沉降的精细化监测方法.该方法首先计算InSAR时序累积沉降盆地，进而建立判别大梯度形变的约束条件，区分沉降边缘与沉降中心.对于形变较小的沉降边缘，保留InSAR结果，而对于大梯度沉降中心，则结合InSAR与概率积分模型建立矿区工作面的沉降盆地，并通过空间插值，获取地理坐标系下连续的地表沉降信息，最终得到完整的矿区地表沉降结果.论文以山东某矿区为研究区域，采用2016年10月16日—2018年3月4日期间的21景SAR影像和工作面水准实测数据对该方法的可行性和精度进行了验证.结果表明，该方法能够在减少水准监测工作量的前提下，获得与实际情况相吻合的沉降结果，其监测能力明显优于常规InSAR和概率积分法，可有效弥补两种技术单独在矿区地表沉降监测中的不足，获取更为准确、可靠的矿区地表沉降信息.
关键词: InSAR/
概率积分法/
矿区地表沉降/
形变梯度
Abstract:By combining the advantages of interferometric synthetic aperture radar (InSAR) and the Probability Integration Method, this paper proposes a refined method for monitoring mining subsidence. In this method, we first calculate the time-series cumulative basin subsidence monitored by InSAR. Then, we establish the constraint conditions for assessing the large gradient subsidence. Subsequently, the subsidence edges and centre can be distinguished. For the edges, with small subsidence, the InSAR results are retained; for the centre, with large gradient subsidence, the InSAR results and the probability integration method are combined to establish the subsidence basin. Finally, a complete surface subsidence result for the mining area is obtained. This study took a mining area in Shandong Province, China, as the study area and used 21 SAR images acquired from 16 October 2016 to 4 March 2018 and levelling data of the working face level to verify the feasibility and accuracy of the method. The results showed that this method can obtain subsidence results consistent with actual conditions on the premise of reducing the workload of levelling monitoring. In addition, its monitoring capability is significantly better than that of conventional InSAR or the probability integration method. It can effectively compensate for the deficiencies of the two technologies that exist when they are used alone and obtain more accurate and reliable mining subsidence information.
Key words:InSAR/
Probability Integration Method/
Mining subsidence/
Deformation gradient
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