An Approach for Estimating Underground‐Goaf Boundaries Based on Combining DInSAR with a Graphical Method

Bu, Pu; Li, Chaokui; Mengguang, Liao; Yang, Wentao; Zhu, Chuanguang; Fang, Jun

doi:10.1155/2020/9375056

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…Remote Sens. 2024, 16, x FOR PEER REVIEW 2 of 15 recent years, UAV (Unmanned Aerial Vehicles) and InSAR (Interferometric Synthetical Aperture Radar) methods have been widely applied in the study of mining subsidence in mining areas due to their advantages of wide range, full coverage, and the efficient noncontact measurement of surface deformation [7][8][9][10][11][12]. This study employs "Space-Sky-Earth" remote sensing measurements, integrating InSAR to trace historical deformations [13,14], drone observations for current surface damage morphologies [7,8], and underground mining space comparisons.…”

Section: Study Areamentioning

confidence: 99%

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Research on Collapse Detection in Old Coal Mine Goafs Based on Space–Sky–Earth Remote Sensing Survey

Yao,

Han,

Zhu

et al. 2024

Remote Sensing

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A considerable number of coal mines employed room and pillar mining in the last century in northern China, where the goaf remained stable for a period of time; however, with the increased exposure of coal pillars, their collapse may gradually increase. The stability assessment of these old rooms and pillar goafs is challenging due to their concealment, irregular mining patterns, and the long passage of time. The methodology developed in this study, based on “space-sky-earth” remote sensing such as InSAR to trace historical deformation, the UAV observation of current surface damage, and comparison of mining spaces, can rapidly detect on a large scale the collapse of old goafs and the trend of damage. This study is conducted with an example of a coal mine in Yulin, Northern China, where obtained quantitative surface deformation values were integrated with qualitative surface damage interpretation results, followed by a yearly analysis of the overlying rock movement in accordance with the underground coal mining process. The results show that from 2007 to 2021, corresponding surface deformation and damage occurred following mining progress. However, the room and pillar goaf areas had not undergone any surface deformation, nor had there been incidents of landslides or ground fissures; therefore, it was speculated that no roof collapse had occurred in this region. The surface deformation and damage associated with underground coal mining are complex and influenced by the coal seam occurrence, mining methods, strata lithology, terrain slope, temporal evolution, and anthropogenic modifications. These phenomena are representative of the coal mining area, and this methodology can provide a reference for similar endeavors.

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Section: Study Areamentioning

confidence: 99%

“…In recent years, UAV (Unmanned Aerial Vehicles) and InSAR (Interferometric Synthetical Aperture Radar) methods have been widely applied in the study of mining subsidence in mining areas due to their advantages of wide range, full coverage, and the efficient non-contact measurement of surface deformation [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Research on Collapse Detection in Old Coal Mine Goafs Based on Space–Sky–Earth Remote Sensing Survey

Yao,

Han,

Zhu

et al. 2024

Remote Sensing

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“…In most existing goaf-location inversion methods, the PIM parameters are given based on experience [21][22][23][24], which leads to large errors. To obtain more accurate model parameters, information should be known about the goaf to invert the model parameters [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods

Zhang

Fan

et al. 2021

Remote Sensing

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The goafs caused by coal mining cause great harm to the surface farmland, buildings, and personal safety. The existing monitoring methods cost a lot of workforce and material resources. Therefore, this paper proposes an inversion approach for establishing the locations of underground goafs and the parameters of the probability integral method (PIM), thus integrating distributed scatter interferometric synthetic aperture radar (DS-InSAR) data and the PIM. Firstly, a large amount of surface deformation observation data above the goaf are obtained by DS-InSAR, and the line-of-sight deformation is regarded as the true value. Secondly, according to the obtained surface deformations, the ranges of eight goaf location parameters and three PIM parameters are set. Thirdly, a correlation function between the surface deformation and the underground goaf location is constructed. Finally, a particle swarm optimization algorithm is used to search for the optimal parameters in the range of the set parameters to meet the requirement for minimum error between the surface deformation calculated by PIM and the line-of-sight deformation obtained by DS-InSAR. These optimal parameters are thus regarded as the real values of the position of the underground goaf and the PIM parameters. The simulation results show that the maximum relative error between the position of the goaf and the PIM parameters is 2.11%. Taking the 93,604 working face of the Zhangshuanglou coal mine in the Peibei mining area as the research object and 12 Sentinel-1A images as the data source, the goaf location and PIM parameters of the working face were successfully inverted. The inversion results show that the maximum relative error in the goaf location parameters was 16.61%, and the maximum relative error in the PIM parameters was 26.67%.

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Monitoring, Analyzing, and Modeling for Single Subsidence Basin in Coal Mining Areas Based on SAR Interferometry with L-Band Data

Wang

Zhang

et al. 2021

Scientific Programming

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Excessive exploitation of underground mine resources has caused serious land subsidence in China. This paper focused on monitoring and modeling the single subsidence basin in coal mining area based on SAR interferometry (InSAR). The optimum InSAR processing strategy to monitor the mining subsidence was built to obtain the land subsidence with large deformation. And a method of three-dimensional mathematical modeling of single subsidence basin based on InSAR measurements was presented. Using Jining Coalfield (China) as the study area, we acquired 7 L-band PALSAR images from January 2008 to February 2010 to monitor the land subsidence in Jining Coalfield. The deformation maps in Jining Coalfield in different periods were obtained. Taking the Geting Coal Mine within the Jining coalfield as an example, we finely analyzed and interpreted the deformation maps. Compared with the simultaneous filed measurements, the precision of deformation measurement using D-InSAR in mining area was analyzed. The root mean square error was 1.37 cm. The method of fine interpretation and analysis for a single subsidence basin was established. The experiments have proved that InSAR technique with L-band InSAR data is suitable for monitoring mining subsidence with large deformation. And the 3D mathematical modeling method could be used for the single subsidence basin in coal mining area.

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An Approach for Estimating Underground‐Goaf Boundaries Based on Combining DInSAR with a Graphical Method

Cited by 3 publications

References 30 publications

Research on Collapse Detection in Old Coal Mine Goafs Based on Space–Sky–Earth Remote Sensing Survey

Research on Collapse Detection in Old Coal Mine Goafs Based on Space–Sky–Earth Remote Sensing Survey

Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods

Monitoring, Analyzing, and Modeling for Single Subsidence Basin in Coal Mining Areas Based on SAR Interferometry with L-Band Data

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