Strata pressure behavior law and regional-level intelligent early warning for high-intensity mining in extended working faces of medium-thick coal seams in Yuheng mining area
High-intensity mining and extended working faces have significantly modified strata pressure manifestation patterns and triggered spatiotemporal evolution of mining-induced pressure. Achieving intelligent prediction of roof pressure is of great significance for ensuring the safe production of mines....
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Editorial Office of Journal of China Coal Society
2025-06-01
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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.2025.0292 |
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| author | Feng CUI Cheng ZONG Xingping LAI Tao XIE Yunhu BAI Yi ZHANG Chong JIA Hao WANG Zhong LUO Hao LIU |
| author_facet | Feng CUI Cheng ZONG Xingping LAI Tao XIE Yunhu BAI Yi ZHANG Chong JIA Hao WANG Zhong LUO Hao LIU |
| author_sort | Feng CUI |
| collection | DOAJ |
| description | High-intensity mining and extended working faces have significantly modified strata pressure manifestation patterns and triggered spatiotemporal evolution of mining-induced pressure. Achieving intelligent prediction of roof pressure is of great significance for ensuring the safe production of mines. Based on the evolution trend and classification prediction of mine pressure in the extended working face of the medium-thick coal seam under high-intensity mining in Yuandatan Coal Mine, Yuheng Mining Area, Shaanxi Province, the distribution characteristics of the support resistance in the extended working face and the strata pressure behavior law were analyzed. The mine pressure data of the working face was dynamically mapped to the spatial grid cells with topological relationships. The spatio-temporal correlation characteristics of the support in the working face were extracted by using the unsupervised clustering algorithm, forming a spatio-temporal linkage support resistance analysis method. A Transformer (Patch Time Series Transformer, PatchTST) mine pressure prediction model based on the patch mechanism was constructed. Multiple prediction models were horizontally compared and tested based on the field measured data to verify the accuracy of PatchTST and its applicability to the prediction of long mine pressure sequences. Finally, the engineering application performance test and prediction error analysis were carried out. The results show that the pressure distribution in the strike direction of the 11207 extended working face of Yuandatan Coal Mine presents an “M”-shaped feature of “double peaks-valley oscillation”. With the advancement and time progression, the “M”-shaped pressure field generally shows a periodic evolution law of “formation-stabilization-recurrence”. After the mine pressure data is dynamically mapped and clustered in the spatial grid units, the accumulation area of the working face pressure can be accurately identified, and the automatic solution of the pressure intensity classification can be achieved. The PatchTST model has the best prediction accuracy when the look-back window is 240 and the prediction step is 3, with the evaluation indicators MSE and MAE being 0.095 and 0.240, respectively. Compared with multiple models based on the attention mechanism, the PatchTST model can achieve the lowest prediction error. The engineering application performance test shows that the method accurately identifies the relatively strong pressure observed on site, and the error analysis also indicates that the prediction accuracy of the model is high, with an accuracy rate of up to 92.8%. The research can provide reference and guidance for the strata pressure behavior law in extended working faces and the intelligent prediction and early warning of working face pressure. |
| format | Article |
| id | doaj-art-16f5a90bdfec4a4f9bb9525fd9abc198 |
| institution | DOAJ |
| issn | 0253-9993 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Office of Journal of China Coal Society |
| record_format | Article |
| series | Meitan xuebao |
| spelling | doaj-art-16f5a90bdfec4a4f9bb9525fd9abc1982025-08-20T03:13:10ZzhoEditorial Office of Journal of China Coal SocietyMeitan xuebao0253-99932025-06-015062866288010.13225/j.cnki.jccs.2025.02922025-0292Strata pressure behavior law and regional-level intelligent early warning for high-intensity mining in extended working faces of medium-thick coal seams in Yuheng mining areaFeng CUI0Cheng ZONG1Xingping LAI2Tao XIE3Yunhu BAI4Yi ZHANG5Chong JIA6Hao WANG7Zhong LUO8Hao LIU9College of Energy and Mining Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy and Mining Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy and Mining Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaShaanxi Zhongneng Coalfield Co., Ltd., Yulin 719000, ChinaShaanxi Zhongneng Coalfield Co., Ltd., Yulin 719000, ChinaShaanxi Zhongneng Coalfield Co., Ltd., Yulin 719000, ChinaCollege of Energy and Mining Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy and Mining Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy and Mining Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy and Mining Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaHigh-intensity mining and extended working faces have significantly modified strata pressure manifestation patterns and triggered spatiotemporal evolution of mining-induced pressure. Achieving intelligent prediction of roof pressure is of great significance for ensuring the safe production of mines. Based on the evolution trend and classification prediction of mine pressure in the extended working face of the medium-thick coal seam under high-intensity mining in Yuandatan Coal Mine, Yuheng Mining Area, Shaanxi Province, the distribution characteristics of the support resistance in the extended working face and the strata pressure behavior law were analyzed. The mine pressure data of the working face was dynamically mapped to the spatial grid cells with topological relationships. The spatio-temporal correlation characteristics of the support in the working face were extracted by using the unsupervised clustering algorithm, forming a spatio-temporal linkage support resistance analysis method. A Transformer (Patch Time Series Transformer, PatchTST) mine pressure prediction model based on the patch mechanism was constructed. Multiple prediction models were horizontally compared and tested based on the field measured data to verify the accuracy of PatchTST and its applicability to the prediction of long mine pressure sequences. Finally, the engineering application performance test and prediction error analysis were carried out. The results show that the pressure distribution in the strike direction of the 11207 extended working face of Yuandatan Coal Mine presents an “M”-shaped feature of “double peaks-valley oscillation”. With the advancement and time progression, the “M”-shaped pressure field generally shows a periodic evolution law of “formation-stabilization-recurrence”. After the mine pressure data is dynamically mapped and clustered in the spatial grid units, the accumulation area of the working face pressure can be accurately identified, and the automatic solution of the pressure intensity classification can be achieved. The PatchTST model has the best prediction accuracy when the look-back window is 240 and the prediction step is 3, with the evaluation indicators MSE and MAE being 0.095 and 0.240, respectively. Compared with multiple models based on the attention mechanism, the PatchTST model can achieve the lowest prediction error. The engineering application performance test shows that the method accurately identifies the relatively strong pressure observed on site, and the error analysis also indicates that the prediction accuracy of the model is high, with an accuracy rate of up to 92.8%. The research can provide reference and guidance for the strata pressure behavior law in extended working faces and the intelligent prediction and early warning of working face pressure.http://www.mtxb.com.cn/article/doi/10.13225/j.cnki.jccs.2025.0292extended mining facemine strata pressure behaviorspatiotemporal characteristicsincoming pressure classificationload forecastingpatchtst |
| spellingShingle | Feng CUI Cheng ZONG Xingping LAI Tao XIE Yunhu BAI Yi ZHANG Chong JIA Hao WANG Zhong LUO Hao LIU Strata pressure behavior law and regional-level intelligent early warning for high-intensity mining in extended working faces of medium-thick coal seams in Yuheng mining area Meitan xuebao extended mining face mine strata pressure behavior spatiotemporal characteristics incoming pressure classification load forecasting patchtst |
| title | Strata pressure behavior law and regional-level intelligent early warning for high-intensity mining in extended working faces of medium-thick coal seams in Yuheng mining area |
| title_full | Strata pressure behavior law and regional-level intelligent early warning for high-intensity mining in extended working faces of medium-thick coal seams in Yuheng mining area |
| title_fullStr | Strata pressure behavior law and regional-level intelligent early warning for high-intensity mining in extended working faces of medium-thick coal seams in Yuheng mining area |
| title_full_unstemmed | Strata pressure behavior law and regional-level intelligent early warning for high-intensity mining in extended working faces of medium-thick coal seams in Yuheng mining area |
| title_short | Strata pressure behavior law and regional-level intelligent early warning for high-intensity mining in extended working faces of medium-thick coal seams in Yuheng mining area |
| title_sort | strata pressure behavior law and regional level intelligent early warning for high intensity mining in extended working faces of medium thick coal seams in yuheng mining area |
| topic | extended mining face mine strata pressure behavior spatiotemporal characteristics incoming pressure classification load forecasting patchtst |
| url | http://www.mtxb.com.cn/article/doi/10.13225/j.cnki.jccs.2025.0292 |
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