Hybrid deep learning-based identification of microseismic events in TBM tunnelling
文献类型：期刊论文
| 作者 | Yin, Xin4; Liu, Quansheng2; Lei, Jinshan1; Pan, Yucong2; Huang, Xing3; Lei, Yiming2 |
| 刊名 | MEASUREMENT
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 238页码:20 |
| 关键词 | TBM tunnelling Microseismic monitoring Microseismic identification Deep learning Multi-algorithm fusion |
| ISSN号 | 0263-2241 |
| DOI | 10.1016/j.measurement.2024.115381 |
| 英文摘要 | For TBM's safe and efficient tunnelling, the microseismic monitoring technique has been widely applied in rockburst warning. However, useful microseismic events are often mixed with noisy events, which seriously affects warning accuracy. To this end, this study proposes several hybrid deep learning-based microseismic identification approaches in TBM tunnelling, where recurrent neural networks directly treat monitored microseismic events as time series and avoid complex feature pre-extraction, and grey wolf optimization and attention mechanism are embedded to optimize model hyper-parameters and recognize value information. Additionally, the learning curve and early-stopping strategy are also integrated to reduce overfitting and underfitting risks. Results in a real TBM-excavated water conveyance tunnel indicate that the bidirectional long short-term memory network model combined with grey wolf optimization and attention mechanism achieves the greatest identification performance among discussed models and provides an effective support for intelligent identification of microseismic events under TBM tunnelling environments. |
| 资助项目 | National Natural Science Founda-tion of China[41941018] ; National Natural Science Founda-tion of China[42177140] ; Key Research and Development Project of Hubei Province[2021BCA133] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001281268900001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42137]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Lei, Jinshan |
| 作者单位 | 1.Cent South Univ, Sch Civil Engn, Changsha 410075, Peoples R China 2.Wuhan Univ, Sch Civil Engn, Wuhan 430072, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 4.City Univ Hong Kong, Dept Architecture & Civil Engn, Kowloon, Tat Chee Ave, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Xin,Liu, Quansheng,Lei, Jinshan,et al. Hybrid deep learning-based identification of microseismic events in TBM tunnelling[J]. MEASUREMENT,2024,238:20. |
| APA | Yin, Xin,Liu, Quansheng,Lei, Jinshan,Pan, Yucong,Huang, Xing,&Lei, Yiming.(2024).Hybrid deep learning-based identification of microseismic events in TBM tunnelling.MEASUREMENT,238,20. |
| MLA | Yin, Xin,et al."Hybrid deep learning-based identification of microseismic events in TBM tunnelling".MEASUREMENT 238(2024):20. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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