Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approach
Deep coal-energy mining frequently results in microseismic (MS) events, which may be a precursor to the risk of rockbursts and pose risks to human safety and infrastructure. Therefore, quantitatively predicting the time, energy, and location (TEL) of future MS events is crucial for understanding and...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775524002518 |
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| author | Yue Song Enyuan Wang Hengze Yang Dong Chen Baolin Li Yangyang Di |
| author_facet | Yue Song Enyuan Wang Hengze Yang Dong Chen Baolin Li Yangyang Di |
| author_sort | Yue Song |
| collection | DOAJ |
| description | Deep coal-energy mining frequently results in microseismic (MS) events, which may be a precursor to the risk of rockbursts and pose risks to human safety and infrastructure. Therefore, quantitatively predicting the time, energy, and location (TEL) of future MS events is crucial for understanding and preventing potential catastrophic events. In this study, we introduced the application of spatiotemporal graph convolutional networks (STGCN) to predict the TEL of MS events induced by deep coal-energy mining. Notably, this was the first application of graph convolution networks (GCNs) in the spatiotemporal prediction of MS events. The adjacency matrices of the sensor networks were determined based on the distance between MS sensors, the sensor network graphs we constructed, and GCN was employed to extract the spatiotemporal details of the graphs. The model is simple and versatile. By testing the model with on-site MS monitoring data, our results demonstrated promising efficacy in predicting the TEL of MS events, with the cosine similarity (C) above 0.90 and the mean relative error (MRE) below 0.08. This is critical to improving the safety and operational efficiency of deep coal-energy mining. |
| format | Article |
| id | doaj-art-b30383df43894167991db2fc0792d307 |
| institution | Kabale University |
| issn | 1674-7755 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj-art-b30383df43894167991db2fc0792d3072025-01-17T04:49:10ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552025-01-01171233244Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approachYue Song0Enyuan Wang1Hengze Yang2Dong Chen3Baolin Li4Yangyang Di5School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, ChinaSchool of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, China; State Key Laboratory of Coal Mine Disaster Prevention and Control, China University of Mining and Technology, Xuzhou, 221116, China; Corresponding author. School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, China.School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, China; Corresponding author.State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining Technology, Xuzhou, 221116, ChinaSchool of Environment and Safety Engineering, North University of China, Taiyuan, 030051, ChinaSchool of Materials Engineering, Changshu Institute of Technology, Suzhou, 215506, ChinaDeep coal-energy mining frequently results in microseismic (MS) events, which may be a precursor to the risk of rockbursts and pose risks to human safety and infrastructure. Therefore, quantitatively predicting the time, energy, and location (TEL) of future MS events is crucial for understanding and preventing potential catastrophic events. In this study, we introduced the application of spatiotemporal graph convolutional networks (STGCN) to predict the TEL of MS events induced by deep coal-energy mining. Notably, this was the first application of graph convolution networks (GCNs) in the spatiotemporal prediction of MS events. The adjacency matrices of the sensor networks were determined based on the distance between MS sensors, the sensor network graphs we constructed, and GCN was employed to extract the spatiotemporal details of the graphs. The model is simple and versatile. By testing the model with on-site MS monitoring data, our results demonstrated promising efficacy in predicting the TEL of MS events, with the cosine similarity (C) above 0.90 and the mean relative error (MRE) below 0.08. This is critical to improving the safety and operational efficiency of deep coal-energy mining.http://www.sciencedirect.com/science/article/pii/S1674775524002518RockburstMicroseismic systemMonitoring and early warningArtificial intelligence |
| spellingShingle | Yue Song Enyuan Wang Hengze Yang Dong Chen Baolin Li Yangyang Di Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approach Journal of Rock Mechanics and Geotechnical Engineering Rockburst Microseismic system Monitoring and early warning Artificial intelligence |
| title | Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approach |
| title_full | Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approach |
| title_fullStr | Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approach |
| title_full_unstemmed | Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approach |
| title_short | Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approach |
| title_sort | prediction of time energy location of microseismic events induced by deep coal energy mining deep learning approach |
| topic | Rockburst Microseismic system Monitoring and early warning Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S1674775524002518 |
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