Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application
文献类型：期刊论文
| 作者 | Chen, Bing-Rui1,2; Wang, Xu1,2; Zhu, Xinhao1,2; Wang, Qing1,2; Xie, Houlin1,2 |
| 刊名 | JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 16期号:3页码:761-777 |
| 关键词 | Rock mass failure Microseismic event P-wave arrival S-wave arrival Deep learning |
| ISSN号 | 1674-7755 |
| DOI | 10.1016/j.jrmge.2023.07.003 |
| 英文摘要 | Accurately picking P- and S -wave arrivals of microseismic (MS) signals in real-time directly in fluences the early warning of rock mass failure. A common contradiction between accuracy and computation exists in the current arrival picking methods. Thus, a real-time arrival picking method of MS signals is constructed based on a convolutional -recurrent neural network (CRNN). This method fully utilizes the advantages of convolutional layers and gated recurrent units (GRU) in extracting short- and long-term features, in order to create a precise and lightweight arrival picking structure. Then, the synthetic signals with field noises are used to evaluate the hyperparameters of the CRNN model and obtain an optimal CRNN model. The actual operation on various devices indicates that compared with the U -Net method, the CRNN method achieves faster arrival picking with less performance consumption. An application of large underground caverns in the Yebatan hydropower station (YBT) project shows that compared with the short-term average/long-term average (STA/LTA), Akaike information criterion (AIC) and U -Net methods, the CRNN method has the highest accuracy within four sampling points, which is 87.44% for P -wave and 91.29% for S -wave, respectively. The sum of mean absolute errors ( MAE SUM ) of the CRNN method is 4.22 sampling points, which is lower than that of the other methods. Among the four methods, the MS sources location calculated based on the CRNN method shows the best consistency with the actual failure, which occurs at the junction of the shaft and the second gallery. Thus, the proposed method can pick up P- and S -arrival accurately and rapidly, providing a reference for rock failure analysis and evaluation in engineering applications. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). |
| 资助项目 | National Natural Science Foundation of China[42077263] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001239651700001 |
| 出版者 | SCIENCE PRESS |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/41641]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Chen, Bing-Rui; Wang, Xu |
| 作者单位 | 1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Bing-Rui,Wang, Xu,Zhu, Xinhao,et al. Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application[J]. JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING,2024,16(3):761-777. |
| APA | Chen, Bing-Rui,Wang, Xu,Zhu, Xinhao,Wang, Qing,&Xie, Houlin.(2024).Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application.JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING,16(3),761-777. |
| MLA | Chen, Bing-Rui,et al."Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application".JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING 16.3(2024):761-777. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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