Recognition of Rock Micro-Fracture Signal Based on Deep Convolution Neural Network Inception Algorithm
Rockburst is a common geological disaster in mines, tunnels, deep underground engineering, and during excavation, mining, and construction. Rockburst frequently occurs as the depth of burial increases, and its early warning technology is in urgent need of further development. At present, the most ef...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9447036/ |
| _version_ | 1818600922005110784 |
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| author | Guili Peng Xianguo Tuo Tong Shen Jing Lu |
| author_facet | Guili Peng Xianguo Tuo Tong Shen Jing Lu |
| author_sort | Guili Peng |
| collection | DOAJ |
| description | Rockburst is a common geological disaster in mines, tunnels, deep underground engineering, and during excavation, mining, and construction. Rockburst frequently occurs as the depth of burial increases, and its early warning technology is in urgent need of further development. At present, the most effective monitoring and analysis method of rockburst is microseismic technology, which detects a large number of rock micro-fracture signals through geophones. The identification of microseismic monitoring data is an essential part of microseismic data processing. It is necessary to identify effective microseismic signals from considerable monitoring data for subsequent early warning. Aiming at the identification of rock micro-fracture signals, this thesis proposes a microseismic data identification method based on the Deep Convolution Neural Network Inception (DCNN-Inception) algorithm. The algorithm uses an existing Convolutional Neural Network (CNN) model, adding Inception structure in the middle of the model to form a DCNN-Inception model. A data set was established depending on the actual measured data of Baihetan Hydropower Station, and CNN and DCNN-Inception were employed to identify effective microseismic signals. The results demonstrate that the DCNN-Inception algorithm is better than CNN in recognition accuracy and can effectively identify effective microseismic signals. It provides an essential foundation for the identification of microseismic abnormal signals of rock microfracture and the early warning of rock rupture precursors and is of practical significance for the study of rockburst warning technology. |
| first_indexed | 2024-12-16T12:43:11Z |
| format | Article |
| id | doaj.art-34f45ac785b54bd189bc58615c9f30a3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T12:43:11Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-34f45ac785b54bd189bc58615c9f30a32022-12-21T22:31:22ZengIEEEIEEE Access2169-35362021-01-019893908939910.1109/ACCESS.2021.30866309447036Recognition of Rock Micro-Fracture Signal Based on Deep Convolution Neural Network Inception AlgorithmGuili Peng0https://orcid.org/0000-0001-5181-8126Xianguo Tuo1Tong Shen2Jing Lu3https://orcid.org/0000-0002-4789-5476School of Control and Mechanical, Tianjin Chengjian University, Tianjin, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, ChinaRockburst is a common geological disaster in mines, tunnels, deep underground engineering, and during excavation, mining, and construction. Rockburst frequently occurs as the depth of burial increases, and its early warning technology is in urgent need of further development. At present, the most effective monitoring and analysis method of rockburst is microseismic technology, which detects a large number of rock micro-fracture signals through geophones. The identification of microseismic monitoring data is an essential part of microseismic data processing. It is necessary to identify effective microseismic signals from considerable monitoring data for subsequent early warning. Aiming at the identification of rock micro-fracture signals, this thesis proposes a microseismic data identification method based on the Deep Convolution Neural Network Inception (DCNN-Inception) algorithm. The algorithm uses an existing Convolutional Neural Network (CNN) model, adding Inception structure in the middle of the model to form a DCNN-Inception model. A data set was established depending on the actual measured data of Baihetan Hydropower Station, and CNN and DCNN-Inception were employed to identify effective microseismic signals. The results demonstrate that the DCNN-Inception algorithm is better than CNN in recognition accuracy and can effectively identify effective microseismic signals. It provides an essential foundation for the identification of microseismic abnormal signals of rock microfracture and the early warning of rock rupture precursors and is of practical significance for the study of rockburst warning technology.https://ieeexplore.ieee.org/document/9447036/Rock micro-fracture signalsdeep convolution neural networkinception structuremicrosiesmic signal recognitionaccuracy and loss rate |
| spellingShingle | Guili Peng Xianguo Tuo Tong Shen Jing Lu Recognition of Rock Micro-Fracture Signal Based on Deep Convolution Neural Network Inception Algorithm IEEE Access Rock micro-fracture signals deep convolution neural network inception structure microsiesmic signal recognition accuracy and loss rate |
| title | Recognition of Rock Micro-Fracture Signal Based on Deep Convolution Neural Network Inception Algorithm |
| title_full | Recognition of Rock Micro-Fracture Signal Based on Deep Convolution Neural Network Inception Algorithm |
| title_fullStr | Recognition of Rock Micro-Fracture Signal Based on Deep Convolution Neural Network Inception Algorithm |
| title_full_unstemmed | Recognition of Rock Micro-Fracture Signal Based on Deep Convolution Neural Network Inception Algorithm |
| title_short | Recognition of Rock Micro-Fracture Signal Based on Deep Convolution Neural Network Inception Algorithm |
| title_sort | recognition of rock micro fracture signal based on deep convolution neural network inception algorithm |
| topic | Rock micro-fracture signals deep convolution neural network inception structure microsiesmic signal recognition accuracy and loss rate |
| url | https://ieeexplore.ieee.org/document/9447036/ |
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