Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm
After a coal mine is closed, the coal rock mass could undergo weathering deterioration and strength reduction due to factors such as stress and groundwater, which in turn changes the stress and bearing capacity of the fractured rock mass in the abandoned goaf, leading to secondary or multiple surfac...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/3/788 |
| _version_ | 1797484923484897280 |
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| author | Bingqian Chen Hao Yu Xiang Zhang Zhenhong Li Jianrong Kang Yang Yu Jiale Yang Lu Qin |
| author_facet | Bingqian Chen Hao Yu Xiang Zhang Zhenhong Li Jianrong Kang Yang Yu Jiale Yang Lu Qin |
| author_sort | Bingqian Chen |
| collection | DOAJ |
| description | After a coal mine is closed, the coal rock mass could undergo weathering deterioration and strength reduction due to factors such as stress and groundwater, which in turn changes the stress and bearing capacity of the fractured rock mass in the abandoned goaf, leading to secondary or multiple surface deformations in the goaf. Currently, the spatiotemporal evolution pattern of the surface deformation of closed mines remains unclear, and there is no integrated monitoring and prediction model for closed mines. Therefore, this study proposed to construct an integrated monitoring and prediction model for closed mines using small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) and a deep learning-based long short-term memory (LSTM) neural network algorithm to achieve evolution pattern and dynamic prediction of spatiotemporal surface deformation of closed mines. Taking a closed mine in the western part of Xuzhou, China, as an example, based on Sentinel-1A SAR data between 21 December 2015, and 11 January 2021, SBAS InSAR technology was used to obtain the spatiotemporal evolution pattern of the surface during the 5 years after mine closure. The results showed that the ground surface subsided in the early stage of mine closure and then uplifted. In 5 years, the maximum subsidence rate in the study area is −43 mm/a, and the cumulative maximum subsidence is 310 mm; the maximum uplift rate is 29 mm/a, and the cumulative maximum uplift is 135 mm. Moreover, the maximum tilt and curvature are 3.5 mm/m and 0.19 mm/m<sup>2</sup>, respectively, which are beyond the safety thresholds of buildings; thus, continuous monitoring is necessary. Based on the evolution pattern of surface deformation, the surface deformation prediction model was proposed by integrating SBAS InSAR and an LSTM neural network. The experiment results showed that the LSTM neural network can accurately predict the deformation trend, with a maximum root mean square error (RMSE) of 5.1 mm. Finally, the relationship between the residual surface deformation and time after mine closure was analyzed, and the mechanisms of surface subsidence and uplift were discussed, which provide a theoretical reference for better understanding the surface deformation process of closed mines and the prevention of surface deformation. |
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| id | doaj.art-150865d1994c4dceba8bbc3d4a895474 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:11:25Z |
| publishDate | 2022-02-01 |
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| series | Remote Sensing |
| spelling | doaj.art-150865d1994c4dceba8bbc3d4a8954742023-11-23T17:43:32ZengMDPI AGRemote Sensing2072-42922022-02-0114378810.3390/rs14030788Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM AlgorithmBingqian Chen0Hao Yu1Xiang Zhang2Zhenhong Li3Jianrong Kang4Yang Yu5Jiale Yang6Lu Qin7School of Geography and Geomatics and Urban-Rural Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography and Geomatics and Urban-Rural Planning, Jiangsu Normal University, Xuzhou 221116, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources of P.R. China, Beijing 100048, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaSchool of Geography and Geomatics and Urban-Rural Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography and Geomatics and Urban-Rural Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography and Geomatics and Urban-Rural Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography and Geomatics and Urban-Rural Planning, Jiangsu Normal University, Xuzhou 221116, ChinaAfter a coal mine is closed, the coal rock mass could undergo weathering deterioration and strength reduction due to factors such as stress and groundwater, which in turn changes the stress and bearing capacity of the fractured rock mass in the abandoned goaf, leading to secondary or multiple surface deformations in the goaf. Currently, the spatiotemporal evolution pattern of the surface deformation of closed mines remains unclear, and there is no integrated monitoring and prediction model for closed mines. Therefore, this study proposed to construct an integrated monitoring and prediction model for closed mines using small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) and a deep learning-based long short-term memory (LSTM) neural network algorithm to achieve evolution pattern and dynamic prediction of spatiotemporal surface deformation of closed mines. Taking a closed mine in the western part of Xuzhou, China, as an example, based on Sentinel-1A SAR data between 21 December 2015, and 11 January 2021, SBAS InSAR technology was used to obtain the spatiotemporal evolution pattern of the surface during the 5 years after mine closure. The results showed that the ground surface subsided in the early stage of mine closure and then uplifted. In 5 years, the maximum subsidence rate in the study area is −43 mm/a, and the cumulative maximum subsidence is 310 mm; the maximum uplift rate is 29 mm/a, and the cumulative maximum uplift is 135 mm. Moreover, the maximum tilt and curvature are 3.5 mm/m and 0.19 mm/m<sup>2</sup>, respectively, which are beyond the safety thresholds of buildings; thus, continuous monitoring is necessary. Based on the evolution pattern of surface deformation, the surface deformation prediction model was proposed by integrating SBAS InSAR and an LSTM neural network. The experiment results showed that the LSTM neural network can accurately predict the deformation trend, with a maximum root mean square error (RMSE) of 5.1 mm. Finally, the relationship between the residual surface deformation and time after mine closure was analyzed, and the mechanisms of surface subsidence and uplift were discussed, which provide a theoretical reference for better understanding the surface deformation process of closed mines and the prevention of surface deformation.https://www.mdpi.com/2072-4292/14/3/788interferometric synthetic aperture radarclosed minesdeformation monitoringtime series analysisdeformation prediction |
| spellingShingle | Bingqian Chen Hao Yu Xiang Zhang Zhenhong Li Jianrong Kang Yang Yu Jiale Yang Lu Qin Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm Remote Sensing interferometric synthetic aperture radar closed mines deformation monitoring time series analysis deformation prediction |
| title | Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm |
| title_full | Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm |
| title_fullStr | Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm |
| title_full_unstemmed | Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm |
| title_short | Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm |
| title_sort | time varying surface deformation retrieval and prediction in closed mines through integration of sbas insar measurements and lstm algorithm |
| topic | interferometric synthetic aperture radar closed mines deformation monitoring time series analysis deformation prediction |
| url | https://www.mdpi.com/2072-4292/14/3/788 |
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