Research on prediction of high energy microseismic events in rock burst mines based on BP neural network

Abstract

In response to the frequent occurrence of high-energy microseismic events in coal mines in China, a back propagation neural network (BPNN) prediction model based on surface subsidence data has been proposed to provide a basis for safely and efficiently predicting coal mine disasters. Theoretical research on the relationship between surface displacement, mining disturbance, and high-energy microseismic event levels has demonstrated a significant correlation among these factors. When there is a sudden increase or decrease in surface displacement or mining disturbance, the advancing working face typically exhibits dynamic characteristics. Therefore, feature parameters relevant to predicting high-energy microseismic event levels were selected as input variables for the BPNN model. Raw data from 88 sets of microseismic events in the 301 working face of the third mining area of a certain coal mine in Inner Mongolia were extracted. First, outlier preprocessing was conducted to obtain a complete dataset, which is then divided into a training set and a testing set. The BPNN model was trained with the training set and subsequently tested to evaluate its predictive performance. Finally, by comparing several model evaluation metrics, it was found that the BPNN model outperforms other common models in predicting high-energy events. The overall prediction accuracy is 86.4%, and the root mean square error (RMSE) is 0.45, indicating that the BPNN-based prediction model for high-energy microseismic events in coal mines is feasible.