A quantitative pre-warning for coal burst hazardous zones in a deep coal mine based on the spatio-temporal forecast of microseismic events
文献类型：期刊论文
| 作者 | Chen, Jie1; Zhu, Chao1; Du, Junsheng1; Pu, Yuanyuan1; Pan, Pengzhi4; Bai, Jianbiao3; Qi, Qingxin2 |
| 刊名 | PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
 |
| 出版日期 | 2022-03-01 |
| 卷号 | 159期号:-页码:1105 |
| ISSN号 | 0957-5820 |
| 关键词 | Coal burst Deep learning Microseismic event Intelligent pre-warning platform |
| 英文摘要 | The quantitative prediction for a coal burst is challenging since the coal burst mechanism is extremely complex with a verity of influencing factors involved. This study proposes a data-driven strategy to dynamically determine the coal burst hazardous zones in a deep coal mine based on quantitative predictions for microseismic events. A deep learning model, MSNet, comprising a convolutional module, a recurrent module, a skip-recurrent module, and an autoregressive module is built to predict the time, location, and energy for imminent microseismic events. More than ten thousand microseismic events from a workface were collected to form the database for the MSNet model training and testing. The results indicated that the MSNet can predict the event location accurately but that it predicts event timing less accurately. The MSNet demonstrated the worst prediction accuracy for event energy. Furthermore, this study analyzed the possible causes of the model's prediction errors and provided ways for enhancing the model's performance. Finally, a coal burst intelligent pre-warning platform was developed, which has been successfully used in coal mines at present. This study realized the quantitative forecast for coal burst hazardous areas on a preliminary basis while laying a foundation for coal burst timing risk prediction.(c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved. |
| 学科主题 | Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000781511300004 |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35266]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China 2.State Key Laboratory of Coal Mining and Clean Utilization, China Coal Research Institute, China Mine Safety Technology Branch, Beijing 100013, China 3.State Key Laboratory of Coal Resources ＆ Safe Mining, China University of Mining ＆ Technology, Xuzhou 221116, China 4.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China |
| 推荐引用方式 GB/T 7714 | Chen, Jie,Zhu, Chao,Du, Junsheng,et al. A quantitative pre-warning for coal burst hazardous zones in a deep coal mine based on the spatio-temporal forecast of microseismic events[J]. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION,2022,159(-):1105. |
| APA | Chen, Jie.,Zhu, Chao.,Du, Junsheng.,Pu, Yuanyuan.,Pan, Pengzhi.,...&Qi, Qingxin.(2022).A quantitative pre-warning for coal burst hazardous zones in a deep coal mine based on the spatio-temporal forecast of microseismic events.PROCESS SAFETY AND ENVIRONMENTAL PROTECTION,159(-),1105. |
| MLA | Chen, Jie,et al."A quantitative pre-warning for coal burst hazardous zones in a deep coal mine based on the spatio-temporal forecast of microseismic events".PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 159.-(2022):1105. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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