Machine Learning–Enhanced Microseismic Analysis for Evaluating Rock Crack Trajectory
Microseismic (MS) monitoring is an effective method for tracking the development of rock fractures. However, the utilization of existing data is severely limited by current visualization techniques. In this study, the evolution characteristics of MS parameters during the rock fracture process were i...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2024/6845665 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832543597970325504 |
|---|---|
| author | Xinglong Feng Zeng Chen Zhengrong Li Qingtian Zeng Jing Wang Ping Wang |
| author_facet | Xinglong Feng Zeng Chen Zhengrong Li Qingtian Zeng Jing Wang Ping Wang |
| author_sort | Xinglong Feng |
| collection | DOAJ |
| description | Microseismic (MS) monitoring is an effective method for tracking the development of rock fractures. However, the utilization of existing data is severely limited by current visualization techniques. In this study, the evolution characteristics of MS parameters during the rock fracture process were investigated using MS localization techniques and spatial clustering methods. Machine learning methods were applied to cluster acoustic emission events, enabling the automatic identification of hazardous regions and the construction of crack propagation trajectories. The applicability of clustering methods was assessed using MS monitoring data collected during an in situ rock fracture process. The research results show that the spatial clustering method can effectively delineate hazardous areas, and the changes in these regions reflect the progressive development of rock mass fractures. Regions with a higher proportion of high-energy MS events pose greater risks. Additionally, the propagation trajectory of MS events can effectively characterize the concentration and development direction of fractures. This study provides valuable insights into the characterization of crack evolution and seepage channels. |
| format | Article |
| id | doaj-art-c7eb23df75ee466096f1d009db4d92b5 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-c7eb23df75ee466096f1d009db4d92b52025-02-03T11:38:00ZengWileyShock and Vibration1875-92032024-01-01202410.1155/2024/6845665Machine Learning–Enhanced Microseismic Analysis for Evaluating Rock Crack TrajectoryXinglong Feng0Zeng Chen1Zhengrong Li2Qingtian Zeng3Jing Wang4Ping Wang5Yunnan Diqing Nonferrous Metals Co., LtdBGRIMM Technology GroupYunnan Diqing Nonferrous Metals Co., LtdYunnan Diqing Nonferrous Metals Co., LtdYunnan Diqing Nonferrous Metals Co., LtdBGRIMM Technology GroupMicroseismic (MS) monitoring is an effective method for tracking the development of rock fractures. However, the utilization of existing data is severely limited by current visualization techniques. In this study, the evolution characteristics of MS parameters during the rock fracture process were investigated using MS localization techniques and spatial clustering methods. Machine learning methods were applied to cluster acoustic emission events, enabling the automatic identification of hazardous regions and the construction of crack propagation trajectories. The applicability of clustering methods was assessed using MS monitoring data collected during an in situ rock fracture process. The research results show that the spatial clustering method can effectively delineate hazardous areas, and the changes in these regions reflect the progressive development of rock mass fractures. Regions with a higher proportion of high-energy MS events pose greater risks. Additionally, the propagation trajectory of MS events can effectively characterize the concentration and development direction of fractures. This study provides valuable insights into the characterization of crack evolution and seepage channels.http://dx.doi.org/10.1155/2024/6845665 |
| spellingShingle | Xinglong Feng Zeng Chen Zhengrong Li Qingtian Zeng Jing Wang Ping Wang Machine Learning–Enhanced Microseismic Analysis for Evaluating Rock Crack Trajectory Shock and Vibration |
| title | Machine Learning–Enhanced Microseismic Analysis for Evaluating Rock Crack Trajectory |
| title_full | Machine Learning–Enhanced Microseismic Analysis for Evaluating Rock Crack Trajectory |
| title_fullStr | Machine Learning–Enhanced Microseismic Analysis for Evaluating Rock Crack Trajectory |
| title_full_unstemmed | Machine Learning–Enhanced Microseismic Analysis for Evaluating Rock Crack Trajectory |
| title_short | Machine Learning–Enhanced Microseismic Analysis for Evaluating Rock Crack Trajectory |
| title_sort | machine learning enhanced microseismic analysis for evaluating rock crack trajectory |
| url | http://dx.doi.org/10.1155/2024/6845665 |
| work_keys_str_mv | AT xinglongfeng machinelearningenhancedmicroseismicanalysisforevaluatingrockcracktrajectory AT zengchen machinelearningenhancedmicroseismicanalysisforevaluatingrockcracktrajectory AT zhengrongli machinelearningenhancedmicroseismicanalysisforevaluatingrockcracktrajectory AT qingtianzeng machinelearningenhancedmicroseismicanalysisforevaluatingrockcracktrajectory AT jingwang machinelearningenhancedmicroseismicanalysisforevaluatingrockcracktrajectory AT pingwang machinelearningenhancedmicroseismicanalysisforevaluatingrockcracktrajectory |