Intelligent prediction of time series and grade of rock burst in steeply inclined ultra-thick coal seam excavation roadway
Realizing the intelligent warning of rock burst in coal mine is of great significance to ensure the safety of mine operation. Based on the intelligent classification prediction of rock burst occurrence time series in roadway of steeply inclined ultra-thick coal seam in a mine in Xinjiang, the spatio...
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| Format: | Article |
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Editorial Office of Journal of China Coal Society
2025-02-01
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| Series: | Meitan xuebao |
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| Online Access: | http://www.mtxb.com.cn/article/doi/10.13225/j.cnki.jccs.2023.1762 |
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| author | Feng CUI Cheng ZONG Xingping LAI Shifeng HE Suilin ZHANG Chong JIA |
| author_facet | Feng CUI Cheng ZONG Xingping LAI Shifeng HE Suilin ZHANG Chong JIA |
| author_sort | Feng CUI |
| collection | DOAJ |
| description | Realizing the intelligent warning of rock burst in coal mine is of great significance to ensure the safety of mine operation. Based on the intelligent classification prediction of rock burst occurrence time series in roadway of steeply inclined ultra-thick coal seam in a mine in Xinjiang, the spatio-temporal evolution of each microseismic information index during roadway excavation was analyzed. The Random Forest optimized by Genetic Algorithm (GA) was used. RF selects a number of indicators with high performance in predicting the development trend of impact. Based on the Phase Space Reconstruction (PSR) technology, the data is mapped to the high-dimensional space for reconstruction. LSTM is trained to learn the characteristics of high dimensional data, and a prediction model of steeply inclined ultra-thick coal seam rock burst (PSR-LSTM) based on deep learning and multiple chaotic time series is constructed. The results show that each microseismic information index is sensitive to shock warning and has significant correlation with each other. Six microseismic information indexes with high performance in predicting the development trend of shock are selected. The time series of multiple indicators has chaotic characteristics. After phase space reconstruction, LSTM learning and training can effectively enhance the data utilization rate and prediction accuracy of the model. When the prediction time of the constructed PSR-LSTM model is specified as 1 day, the prediction accuracy can reach 0.9135, and the F1 value can reach 0.9116. All of them are better than the unreconstructed LSTM model. The model can well predict the time series trend and danger level of the rock burst in the excavation roadway of steeply inclined ultra-thick coal seam. The research method can provide reference for the intelligent prediction and early warning of rock burst in the excavation roadway of steeply inclined ultra-thick coal seam. |
| format | Article |
| id | doaj-art-3ff51235382445e5b8fcc4e6e661e581 |
| institution | OA Journals |
| issn | 0253-9993 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Editorial Office of Journal of China Coal Society |
| record_format | Article |
| series | Meitan xuebao |
| spelling | doaj-art-3ff51235382445e5b8fcc4e6e661e5812025-08-20T02:26:03ZzhoEditorial Office of Journal of China Coal SocietyMeitan xuebao0253-99932025-02-0150284586110.13225/j.cnki.jccs.2023.17622023-1762Intelligent prediction of time series and grade of rock burst in steeply inclined ultra-thick coal seam excavation roadwayFeng CUI0Cheng ZONG1Xingping LAI2Shifeng HE3Suilin ZHANG4Chong JIA5College of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaRealizing the intelligent warning of rock burst in coal mine is of great significance to ensure the safety of mine operation. Based on the intelligent classification prediction of rock burst occurrence time series in roadway of steeply inclined ultra-thick coal seam in a mine in Xinjiang, the spatio-temporal evolution of each microseismic information index during roadway excavation was analyzed. The Random Forest optimized by Genetic Algorithm (GA) was used. RF selects a number of indicators with high performance in predicting the development trend of impact. Based on the Phase Space Reconstruction (PSR) technology, the data is mapped to the high-dimensional space for reconstruction. LSTM is trained to learn the characteristics of high dimensional data, and a prediction model of steeply inclined ultra-thick coal seam rock burst (PSR-LSTM) based on deep learning and multiple chaotic time series is constructed. The results show that each microseismic information index is sensitive to shock warning and has significant correlation with each other. Six microseismic information indexes with high performance in predicting the development trend of shock are selected. The time series of multiple indicators has chaotic characteristics. After phase space reconstruction, LSTM learning and training can effectively enhance the data utilization rate and prediction accuracy of the model. When the prediction time of the constructed PSR-LSTM model is specified as 1 day, the prediction accuracy can reach 0.9135, and the F1 value can reach 0.9116. All of them are better than the unreconstructed LSTM model. The model can well predict the time series trend and danger level of the rock burst in the excavation roadway of steeply inclined ultra-thick coal seam. The research method can provide reference for the intelligent prediction and early warning of rock burst in the excavation roadway of steeply inclined ultra-thick coal seam.http://www.mtxb.com.cn/article/doi/10.13225/j.cnki.jccs.2023.1762steeply inclined ultra-thick coal seamexcavation roadwayrock burstmultivariate chaotic time seriesphase space reconstructiondeep learning |
| spellingShingle | Feng CUI Cheng ZONG Xingping LAI Shifeng HE Suilin ZHANG Chong JIA Intelligent prediction of time series and grade of rock burst in steeply inclined ultra-thick coal seam excavation roadway Meitan xuebao steeply inclined ultra-thick coal seam excavation roadway rock burst multivariate chaotic time series phase space reconstruction deep learning |
| title | Intelligent prediction of time series and grade of rock burst in steeply inclined ultra-thick coal seam excavation roadway |
| title_full | Intelligent prediction of time series and grade of rock burst in steeply inclined ultra-thick coal seam excavation roadway |
| title_fullStr | Intelligent prediction of time series and grade of rock burst in steeply inclined ultra-thick coal seam excavation roadway |
| title_full_unstemmed | Intelligent prediction of time series and grade of rock burst in steeply inclined ultra-thick coal seam excavation roadway |
| title_short | Intelligent prediction of time series and grade of rock burst in steeply inclined ultra-thick coal seam excavation roadway |
| title_sort | intelligent prediction of time series and grade of rock burst in steeply inclined ultra thick coal seam excavation roadway |
| topic | steeply inclined ultra-thick coal seam excavation roadway rock burst multivariate chaotic time series phase space reconstruction deep learning |
| url | http://www.mtxb.com.cn/article/doi/10.13225/j.cnki.jccs.2023.1762 |
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