Coal burst spatio-temporal prediction method based on bidirectional long short-term memory network
Xu Yang1
Yapeng Liu1
Anye Cao2
Yaoqi Liu2
Changbin Wang3
Weiwei Zhao2
Qiang Niu1
1.School of Computer Science and Technology,China University of Mining and Technology,Daxue Road,Xuzhou 221116,Jiangsu,China;Engineering Research Center of Mine Digitization of Ministry of Education,China University of Mining and Technology,Daxue Road,Xuzhou 221116,Jiangsu,China2.School of Mines,China University of Mining and Technology,Daxue Road,Xuzhou 221116,Jiangsu,China3.State Key Laboratory of Coal Resources and Safe Mining,China University of Mining and Technology,Daxue Road,Xuzhou 221116,Jiangsu,China
摘要：The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster prediction.To address the issue of insuf-ficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction,this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory(Bi-LSTM)network.The method involves three main modules,including microseismic spatio-temporal characteristic indicators construction,temporal prediction model,and spatial prediction model.To validate the effectiveness of the proposed method,engineering applica-tion tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia,focusing on 13 high-energy microseismic events with energy levels greater than 105 J.In terms of temporal prediction,the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions,and there is no false alarm detected throughout the entire testing period.Moreover,compared to the traditional threshold-based coal burst temporal prediction method,the accuracy of the proposed method is increased by 38.5％.In terms of spatial prediction,the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions,3 medium hazard predictions,and 4 weak hazard predictions.
机标关键词：predictionmemorynetworkburstmethodlongbasedbidirectional
论文发表日期：2025-02-28
在线出版日期：2025-10-15（本平台首次上网日期，不代表文献的发表时间）
页数：18( 230-247 )
英文信息