Automated Detection of Microseismic Arrival Based on Convolutional Neural Networks
It is difficult to accurately and efficiently detect seismic wave signals at the time of arrival for automatic positioning from microseismic waves. A U-net model to detect the arrival time of seismic waves is constructed based on the convolutional neural network (CNN) theory. The original data for 1...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/8000477 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832551047310082048 |
|---|---|
| author | Weijian Liu Haoyuan Chang Yang Xiao Shuisheng Yu Chuanbo Huang Yuntian Yao |
| author_facet | Weijian Liu Haoyuan Chang Yang Xiao Shuisheng Yu Chuanbo Huang Yuntian Yao |
| author_sort | Weijian Liu |
| collection | DOAJ |
| description | It is difficult to accurately and efficiently detect seismic wave signals at the time of arrival for automatic positioning from microseismic waves. A U-net model to detect the arrival time of seismic waves is constructed based on the convolutional neural network (CNN) theory. The original data for 1555 segments and synthetic data of 7764 segments were detected using Akaike’s information criterion (AIC) algorithm, the time window energy eigenvalue algorithm, and the U-net model. During uniaxial compression of the test block, acoustic emission equipment is used to collect the vibration wave generated by the rupture of the test block. Source imaging images are drawn using the Origin software, the arrival time error is counted, and the advantages and disadvantages of the three arrival time methods are discussed. Similarities between the source image and the actual fracture image are observed. There is a high similarity between the source imaging map and the physical trajectory map when the U-net model is used. Thus, it is feasible to use the U-net model to detect the arrival time of seismic waves. Its accuracy is greater than that of the time window energy eigenvalue algorithm but lower than that of the AIC algorithm for high signal-to-noise ratios. After reducing the signal-to-noise ratio, the stability and accuracy of the U-net model to detect the arrival time have improved over the other two algorithms. |
| format | Article |
| id | doaj-art-5a91015d8a7542e597e47683aa2cf02f |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-5a91015d8a7542e597e47683aa2cf02f2025-02-03T06:05:01ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/8000477Automated Detection of Microseismic Arrival Based on Convolutional Neural NetworksWeijian Liu0Haoyuan Chang1Yang Xiao2Shuisheng Yu3Chuanbo Huang4Yuntian Yao5Architectural and Civil Engineering InstituteArchitectural and Civil Engineering InstituteArchitectural and Civil Engineering InstituteArchitectural and Civil Engineering InstituteZhenghong Hengtai (Xinmi) Coal Industry Inc.Suzhou Ruisi Breakthrough Science & Technologies Inc.It is difficult to accurately and efficiently detect seismic wave signals at the time of arrival for automatic positioning from microseismic waves. A U-net model to detect the arrival time of seismic waves is constructed based on the convolutional neural network (CNN) theory. The original data for 1555 segments and synthetic data of 7764 segments were detected using Akaike’s information criterion (AIC) algorithm, the time window energy eigenvalue algorithm, and the U-net model. During uniaxial compression of the test block, acoustic emission equipment is used to collect the vibration wave generated by the rupture of the test block. Source imaging images are drawn using the Origin software, the arrival time error is counted, and the advantages and disadvantages of the three arrival time methods are discussed. Similarities between the source image and the actual fracture image are observed. There is a high similarity between the source imaging map and the physical trajectory map when the U-net model is used. Thus, it is feasible to use the U-net model to detect the arrival time of seismic waves. Its accuracy is greater than that of the time window energy eigenvalue algorithm but lower than that of the AIC algorithm for high signal-to-noise ratios. After reducing the signal-to-noise ratio, the stability and accuracy of the U-net model to detect the arrival time have improved over the other two algorithms.http://dx.doi.org/10.1155/2022/8000477 |
| spellingShingle | Weijian Liu Haoyuan Chang Yang Xiao Shuisheng Yu Chuanbo Huang Yuntian Yao Automated Detection of Microseismic Arrival Based on Convolutional Neural Networks Shock and Vibration |
| title | Automated Detection of Microseismic Arrival Based on Convolutional Neural Networks |
| title_full | Automated Detection of Microseismic Arrival Based on Convolutional Neural Networks |
| title_fullStr | Automated Detection of Microseismic Arrival Based on Convolutional Neural Networks |
| title_full_unstemmed | Automated Detection of Microseismic Arrival Based on Convolutional Neural Networks |
| title_short | Automated Detection of Microseismic Arrival Based on Convolutional Neural Networks |
| title_sort | automated detection of microseismic arrival based on convolutional neural networks |
| url | http://dx.doi.org/10.1155/2022/8000477 |
| work_keys_str_mv | AT weijianliu automateddetectionofmicroseismicarrivalbasedonconvolutionalneuralnetworks AT haoyuanchang automateddetectionofmicroseismicarrivalbasedonconvolutionalneuralnetworks AT yangxiao automateddetectionofmicroseismicarrivalbasedonconvolutionalneuralnetworks AT shuishengyu automateddetectionofmicroseismicarrivalbasedonconvolutionalneuralnetworks AT chuanbohuang automateddetectionofmicroseismicarrivalbasedonconvolutionalneuralnetworks AT yuntianyao automateddetectionofmicroseismicarrivalbasedonconvolutionalneuralnetworks |