A spatio-temporal prediction method for coal burst based on the fusion of microseismic multidimensional information

It is difficult to cooperate temporal and spatial prediction of coal burst and spatio-temporal feature mining of massive microseismic data are insufficient. A spatio-temporal prediction method for coal burst is proposed based on the fusion of microseismic multidimensional information with the relevant theories and methods of deep learning. The process mainly includes three modules: microseismic spatio-temporal characteristic index, temporal prediction and spatial prediction. Microseismic spatio-temporal characteristic index method is based on principal component analysis and kernel density estimation, with which temporal prediction model of coal burst based on deep recurrent neural network is constructed, and spatial prediction method of coal burst based on the fusion of long and short time windows is proposed, thus realizing the spatio-temporal prediction of coal burst with spatio-temporal coordination. To evaluate the effectiveness of the method, engineering application tests are conducted on a hazardous working face in the Ordos mining area of the Inner Mongolia Autonomous Region. During the test, 13 large energy events (microseismic events with energy greater than 105 J) occur. In temporal prediction, the prediction results are 10 strong hazards and 3 medium hazards, and the false positive rate of the model is only 0. 133. In temporal prediction, the distribution region of large energy events corresponds to 6 strong hazards, 3 medium hazards and 4 weak hazards. The test results show that the method can meet the spatio-temporal requirements of engineering application, and the research results can provide a paradigm for spatio-temporal prediction of coal burst source. © 2024 China University of Mining and Technology. All rights reserved.