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Open AccessArticle

Goaf Locating Based on InSAR and Probability Integration Method

by Sen Du 1,2,3, Yunjia Wang 1,*, Meinan Zheng 1,2, Dawei Zhou 3 and Yuanping Xia 4
1
NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology (CUMT), Xuzhou 221116, China
2
School of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), Xuzhou 221116, China
3
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology(CUMT), Xuzhou 221116, China
4
Faculty of Geomatics, East China University of Technology(ECUT), Nanchang 330029, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 812; https://doi.org/10.3390/rs11070812
Received: 22 February 2019 / Revised: 29 March 2019 / Accepted: 2 April 2019 / Published: 4 April 2019
(This article belongs to the Special Issue Remote Sensing of Engineering Geological Science)
Mining goafs can cause many hazards, such as burst water, spontaneous combustion of coal seams, surface collapse, etc. In this paper, a feature-points-based method for the efficient location of mining goafs is proposed. Different interferometric synthetic aperture radar (DInSAR) is used to monitor the subsidence basin caused by mining. Using the principles of the probability integral method (PIM), the inflection points and the boundary points of the basin monitored by DInSAR are determined and used as feature points to locate the goaf. In this paper, the necessity of locating goafs and the traditional methods used for this task are discussed first. Then, the results of verifying the proposed method by both a simulation experiment and real data experiment are presented. Six RADARSAT-2 images from 13th October 2015 to 5th March 2016 were used to acquire the subsidence basin caused by the 15235 working faces of the Jiulong mining area. The average relative errors of the simulation experiment and real data experiment were about 6.43% and 12.59%, respectively. The average absolute errors of the simulation experiment and real data experiment were about 28 m and 38 m, respectively. In the final part of this paper, the error sources are discussed to illustrate the factors that can affect the location result. View Full-Text
Keywords: DInSAR; PIM; mining goaf locating; numerical simulation DInSAR; PIM; mining goaf locating; numerical simulation
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MDPI and ACS Style

Du, S.; Wang, Y.; Zheng, M.; Zhou, D.; Xia, Y. Goaf Locating Based on InSAR and Probability Integration Method. Remote Sens. 2019, 11, 812.

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