Intelligent prediction of time series and grade of rock burst in steeply inclined ultra-thick coal seam excavation roadway

CUI Feng; ZONG Cheng; LAI Xingping; HE Shifeng; ZHANG Suilin; JIA Chong

doi:10.13225/j.cnki.jccs.2023.1762

Abstract：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 in-clined 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 se-lects 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 mi-croseismic 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 con-structed PSR-LSTM model is specified as 1 day,the prediction accuracy can reach 0.913 5,and the F1 value can reach 0.911 6.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 in-clined ultra-thick coal seam.