Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10
Borehole hydraulic fracturing in coal mines can effectively prevent coal rock dynamic disasters. Accurately recognizing weak microseismic events is an essential prerequisite for the micro-seismic monitoring of hydraulic fracturing in coal seams. This study proposes a recognition method for weak micr...
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| Format: | Article |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/1/80 |
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| author | Yunpeng Zhang Nan Li Lihong Sun Jincheng Qiu Xiaokai Huang Yan Li |
| author_facet | Yunpeng Zhang Nan Li Lihong Sun Jincheng Qiu Xiaokai Huang Yan Li |
| author_sort | Yunpeng Zhang |
| collection | DOAJ |
| description | Borehole hydraulic fracturing in coal mines can effectively prevent coal rock dynamic disasters. Accurately recognizing weak microseismic events is an essential prerequisite for the micro-seismic monitoring of hydraulic fracturing in coal seams. This study proposes a recognition method for weak microseismic waveforms based on ResNet-10 to accurately recognize microseismic events generated by borehole hydraulic fracturing in coal mines. To begin with, the background noise and microseismic signals undergo pre-processing through noise reduction and filtering techniques. The preprocessed data are then fed into the ResNet-10 model, and the model parameters are continuously adjusted while the training and test data are updated. The training process stops when the model accuracy rate and loss function value are greater than 99.9% and less than 0.02 for five consecutive times. The model with the highest accuracy rate is then selected to detect the microseismic waveform. The recognition results of ResNet-10 are compared with the threshold value, STA/LTA, and expert recognition results. Finally, the study analyzes flow signal, blasting, and microseismic waveforms. The recognition accuracy rate and recall rate of ResNet-10 are much higher than those of threshold value and STA/LTA, and better than that of the experts. The results of the study show that ResNet-10 can accurately recognize weak microseismic events that are difficult for the threshold value, STA/LTA, and experts to recognize. When water flow signal occurs, it often corresponds to the penetration of hydraulic cracks and the seepage of water. The waveform recognition results demonstrate that the ResNet-10 method has great potential in recognizing weak microseismic waveforms generated by borehole hydraulic fracturing in coal seams. |
| first_indexed | 2024-03-08T15:12:45Z |
| format | Article |
| id | doaj.art-cdb5a7d343d44b65a9e3d2bcb9cd8e25 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T15:12:45Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cdb5a7d343d44b65a9e3d2bcb9cd8e252024-01-10T14:50:50ZengMDPI AGApplied Sciences2076-34172023-12-011418010.3390/app14010080Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10Yunpeng Zhang0Nan Li1Lihong Sun2Jincheng Qiu3Xiaokai Huang4Yan Li5State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, ChinaState Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaBorehole hydraulic fracturing in coal mines can effectively prevent coal rock dynamic disasters. Accurately recognizing weak microseismic events is an essential prerequisite for the micro-seismic monitoring of hydraulic fracturing in coal seams. This study proposes a recognition method for weak microseismic waveforms based on ResNet-10 to accurately recognize microseismic events generated by borehole hydraulic fracturing in coal mines. To begin with, the background noise and microseismic signals undergo pre-processing through noise reduction and filtering techniques. The preprocessed data are then fed into the ResNet-10 model, and the model parameters are continuously adjusted while the training and test data are updated. The training process stops when the model accuracy rate and loss function value are greater than 99.9% and less than 0.02 for five consecutive times. The model with the highest accuracy rate is then selected to detect the microseismic waveform. The recognition results of ResNet-10 are compared with the threshold value, STA/LTA, and expert recognition results. Finally, the study analyzes flow signal, blasting, and microseismic waveforms. The recognition accuracy rate and recall rate of ResNet-10 are much higher than those of threshold value and STA/LTA, and better than that of the experts. The results of the study show that ResNet-10 can accurately recognize weak microseismic events that are difficult for the threshold value, STA/LTA, and experts to recognize. When water flow signal occurs, it often corresponds to the penetration of hydraulic cracks and the seepage of water. The waveform recognition results demonstrate that the ResNet-10 method has great potential in recognizing weak microseismic waveforms generated by borehole hydraulic fracturing in coal seams.https://www.mdpi.com/2076-3417/14/1/80coal seamborehole hydraulic fracturingweak microseismic eventsartificial intelligenceResNet-10water flow signal |
| spellingShingle | Yunpeng Zhang Nan Li Lihong Sun Jincheng Qiu Xiaokai Huang Yan Li Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10 Applied Sciences coal seam borehole hydraulic fracturing weak microseismic events artificial intelligence ResNet-10 water flow signal |
| title | Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10 |
| title_full | Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10 |
| title_fullStr | Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10 |
| title_full_unstemmed | Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10 |
| title_short | Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10 |
| title_sort | recognition of weak microseismic events induced by borehole hydraulic fracturing in coal seam based on resnet 10 |
| topic | coal seam borehole hydraulic fracturing weak microseismic events artificial intelligence ResNet-10 water flow signal |
| url | https://www.mdpi.com/2076-3417/14/1/80 |
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