Prediction of old goaf residual subsidence integrating EDS-InSAR with EsLSTM in the Loess Plateau, China
In China, the Loess Plateau’s fragile geological structure leads to complex and variable surface subsidence in old gob areas following coal mining activities. Accurately predicting this residual subsidence remains a significant scientific challenge. In this study, a method for residual subsidence pr...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2024.1511785/full |
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| author | Fei Ma Fei Ma Qingbin Zhang Lichun Sui |
| author_facet | Fei Ma Fei Ma Qingbin Zhang Lichun Sui |
| author_sort | Fei Ma |
| collection | DOAJ |
| description | In China, the Loess Plateau’s fragile geological structure leads to complex and variable surface subsidence in old gob areas following coal mining activities. Accurately predicting this residual subsidence remains a significant scientific challenge. In this study, a method for residual subsidence prediction using an Exponential Smoothing Long Short-Term Memory (EsLSTM) model is proposed. The investigation centers on the 18,001# old goaf area of the Yangquan Coal Mine in Shanxi Province. Using Sentinel-1A imagery, continuous SAR data from 98 periods were acquired and processed via Enhanced Distributed Scatter InSAR technology. The EsLSTM model was then developed to capture the subsidence time-series characteristics of all surface scatter points and predict future ground subsidence. The analysis reveals that the EsLSTM model delivered excellent accuracy, achieving an R2 value of 0.975. It also outperformed SVR and traditional LSTM models, with a Mean Absolute Error of 2.2 mm and a Root Mean Square Error of 7.9 mm. Predicted results indicate that by October 2023, the maximum cumulative subsidence at the 18,001# working face of the Yangquan Coal Mine will reach 204 mm. The subsidence trend is expected to become more gradual and stable, suggesting a low likelihood of geological disasters in the area. |
| format | Article |
| id | doaj-art-abf0a702817741b8b6fddce9495a8154 |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Earth Science |
| spelling | doaj-art-abf0a702817741b8b6fddce9495a81542025-01-17T11:08:13ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-01-011210.3389/feart.2024.15117851511785Prediction of old goaf residual subsidence integrating EDS-InSAR with EsLSTM in the Loess Plateau, ChinaFei Ma0Fei Ma1Qingbin Zhang2Lichun Sui3Department of Computer Science, Changzhi University, Changzhi, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an, ChinaZhongjin Environmental Technology Co., Ltd., Taiyuan, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an, ChinaIn China, the Loess Plateau’s fragile geological structure leads to complex and variable surface subsidence in old gob areas following coal mining activities. Accurately predicting this residual subsidence remains a significant scientific challenge. In this study, a method for residual subsidence prediction using an Exponential Smoothing Long Short-Term Memory (EsLSTM) model is proposed. The investigation centers on the 18,001# old goaf area of the Yangquan Coal Mine in Shanxi Province. Using Sentinel-1A imagery, continuous SAR data from 98 periods were acquired and processed via Enhanced Distributed Scatter InSAR technology. The EsLSTM model was then developed to capture the subsidence time-series characteristics of all surface scatter points and predict future ground subsidence. The analysis reveals that the EsLSTM model delivered excellent accuracy, achieving an R2 value of 0.975. It also outperformed SVR and traditional LSTM models, with a Mean Absolute Error of 2.2 mm and a Root Mean Square Error of 7.9 mm. Predicted results indicate that by October 2023, the maximum cumulative subsidence at the 18,001# working face of the Yangquan Coal Mine will reach 204 mm. The subsidence trend is expected to become more gradual and stable, suggesting a low likelihood of geological disasters in the area.https://www.frontiersin.org/articles/10.3389/feart.2024.1511785/fullEDS-InSARminesdeformationpredictioneslstm algorithm |
| spellingShingle | Fei Ma Fei Ma Qingbin Zhang Lichun Sui Prediction of old goaf residual subsidence integrating EDS-InSAR with EsLSTM in the Loess Plateau, China Frontiers in Earth Science EDS-InSAR mines deformation prediction eslstm algorithm |
| title | Prediction of old goaf residual subsidence integrating EDS-InSAR with EsLSTM in the Loess Plateau, China |
| title_full | Prediction of old goaf residual subsidence integrating EDS-InSAR with EsLSTM in the Loess Plateau, China |
| title_fullStr | Prediction of old goaf residual subsidence integrating EDS-InSAR with EsLSTM in the Loess Plateau, China |
| title_full_unstemmed | Prediction of old goaf residual subsidence integrating EDS-InSAR with EsLSTM in the Loess Plateau, China |
| title_short | Prediction of old goaf residual subsidence integrating EDS-InSAR with EsLSTM in the Loess Plateau, China |
| title_sort | prediction of old goaf residual subsidence integrating eds insar with eslstm in the loess plateau china |
| topic | EDS-InSAR mines deformation prediction eslstm algorithm |
| url | https://www.frontiersin.org/articles/10.3389/feart.2024.1511785/full |
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