Construction and application of foundational models for intelligent processing of microseismic events in mines
Under the background of intelligent mine construction, intelligent microseismic signal processing serves as the cornerstone for precise early warning of mine dynamic disasters. The complex geological conditions and operational environments in mining areas result in microseismic signals characterized...
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| Format: | Article |
| Language: | Chinese |
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Editorial Office of Journal of China Coal Society
2025-06-01
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| Series: | Meitan xuebao |
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| Online Access: | http://www.mtxb.com.cn/article/doi/10.13225/j.cnki.jccs.2025.0294 |
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| author | Anye CAO Maotao LI Xu YANG Yao YANG Sen LI Yaoqi LIU Changbin WANG |
| author_facet | Anye CAO Maotao LI Xu YANG Yao YANG Sen LI Yaoqi LIU Changbin WANG |
| author_sort | Anye CAO |
| collection | DOAJ |
| description | Under the background of intelligent mine construction, intelligent microseismic signal processing serves as the cornerstone for precise early warning of mine dynamic disasters. The complex geological conditions and operational environments in mining areas result in microseismic signals characterized by strong noise interference, high dimensionality, and pronounced nonlinearity. Conventional automated processing methods suffer from low accuracy and heavy reliance on manual intervention for source parameter calculation, failing to meet the requirements of intelligent disaster warning. To address these challenges, this study developed a Transformer-based foundational model for intelligent microseismic processing by integrating big data analytics and deep learning theories. A comprehensive dataset containing over 300 000 microseismic waveforms was established, incorporating three key innovations: multi-scale convolutional modules for multi-dimensional feature extraction, an adaptive feature fusion strategy for noise-resistant signal representation, and a feature-aggregated multi-head attention mechanism for temporal sequence modeling. The model’s multi-task decoder simultaneously achieves intelligent event detection, P/S-wave arrival picking, and first-motion polarity determination. Experimental results demonstrate exceptional performance with 95.4% event detection accuracy, 96.6% of P-wave arrivals and 65.5% of S-wave arrivals exhibiting errors within 50 ms, and 93.38% accuracy in polarity determination, satisfying real-time processing requirements. Field application at a rockburst-prone coal face in Gansu Province confirmed the model’s engineering effectiveness, enabling fully automated processing from event detection to source localization (errors <50 ms) and mechanism inversion. This technological breakthrough establishes a robust framework for intelligent monitoring and precise early warning of mine dynamic disasters, effectively overcoming the limitations of traditional methods in complex geological environments. |
| format | Article |
| id | doaj-art-68968bcedc4943e88a1a8a157ff3cd11 |
| institution | Matheson Library |
| issn | 0253-9993 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Office of Journal of China Coal Society |
| record_format | Article |
| series | Meitan xuebao |
| spelling | doaj-art-68968bcedc4943e88a1a8a157ff3cd112025-07-16T00:44:41ZzhoEditorial Office of Journal of China Coal SocietyMeitan xuebao0253-99932025-06-015062823283610.13225/j.cnki.jccs.2025.02942025-0294Construction and application of foundational models for intelligent processing of microseismic events in minesAnye CAO0Maotao LI1Xu YANG2Yao YANG3Sen LI4Yaoqi LIU5Changbin WANG6School of Geology and Mining Engineering, Xinjiang University, Urumqi 830046, ChinaSchool of Computer Science and Technology, China University of Mining & Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining & Technology, Xuzhou 221116, ChinaSchool of Mines, China University of Mining & Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining & Technology, Xuzhou 221116, ChinaSchool of Mines, China University of Mining & Technology, Xuzhou 221116, ChinaState Key Laboratory of Fine Exploration and Intelligent Development of Coal Resources, China University of Mining & Technology, Xuzhou 221116, ChinaUnder the background of intelligent mine construction, intelligent microseismic signal processing serves as the cornerstone for precise early warning of mine dynamic disasters. The complex geological conditions and operational environments in mining areas result in microseismic signals characterized by strong noise interference, high dimensionality, and pronounced nonlinearity. Conventional automated processing methods suffer from low accuracy and heavy reliance on manual intervention for source parameter calculation, failing to meet the requirements of intelligent disaster warning. To address these challenges, this study developed a Transformer-based foundational model for intelligent microseismic processing by integrating big data analytics and deep learning theories. A comprehensive dataset containing over 300 000 microseismic waveforms was established, incorporating three key innovations: multi-scale convolutional modules for multi-dimensional feature extraction, an adaptive feature fusion strategy for noise-resistant signal representation, and a feature-aggregated multi-head attention mechanism for temporal sequence modeling. The model’s multi-task decoder simultaneously achieves intelligent event detection, P/S-wave arrival picking, and first-motion polarity determination. Experimental results demonstrate exceptional performance with 95.4% event detection accuracy, 96.6% of P-wave arrivals and 65.5% of S-wave arrivals exhibiting errors within 50 ms, and 93.38% accuracy in polarity determination, satisfying real-time processing requirements. Field application at a rockburst-prone coal face in Gansu Province confirmed the model’s engineering effectiveness, enabling fully automated processing from event detection to source localization (errors <50 ms) and mechanism inversion. This technological breakthrough establishes a robust framework for intelligent monitoring and precise early warning of mine dynamic disasters, effectively overcoming the limitations of traditional methods in complex geological environments.http://www.mtxb.com.cn/article/doi/10.13225/j.cnki.jccs.2025.0294intelligent minemicroseismicbasic modelevent detectionphase pickingdeep learning |
| spellingShingle | Anye CAO Maotao LI Xu YANG Yao YANG Sen LI Yaoqi LIU Changbin WANG Construction and application of foundational models for intelligent processing of microseismic events in mines Meitan xuebao intelligent mine microseismic basic model event detection phase picking deep learning |
| title | Construction and application of foundational models for intelligent processing of microseismic events in mines |
| title_full | Construction and application of foundational models for intelligent processing of microseismic events in mines |
| title_fullStr | Construction and application of foundational models for intelligent processing of microseismic events in mines |
| title_full_unstemmed | Construction and application of foundational models for intelligent processing of microseismic events in mines |
| title_short | Construction and application of foundational models for intelligent processing of microseismic events in mines |
| title_sort | construction and application of foundational models for intelligent processing of microseismic events in mines |
| topic | intelligent mine microseismic basic model event detection phase picking deep learning |
| url | http://www.mtxb.com.cn/article/doi/10.13225/j.cnki.jccs.2025.0294 |
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