Event recognition technology and short-term rockburst early warning model based on microseismic monitoring and ensemble learning

Rockbursts are a significant geological hazard in deep underground engineering, and accurate short-term risk prediction can mitigate safety risks to personnel and equipment. However, challenges remain in the intelligent processing of microseismic(MS) data and effective rockburst prediction. This study focuses on the Dahongshan Copper Mine, utilizing MS monitoring, the synthetic minority oversampling technique and edited nearest neighbours (SMOTE-ENN), and ensemble learning techniques. We developed an intelligent MS event recognition model and a short-term rockburst risk assessment model, enhancing the automation and efficiency of MS event recognition. Additionally, a short-term rockburst risk assessment system (program) was implemented in the Python programming environment, enabling one-click evaluation and warning of short-term rockburst risks (24 h). The research results indicate that the MS event recognition model achieved an F1 score of 0.9819 and an area under the curve (AUC) value of 0.9989, with improvements of up to 49.63% and 11.24% compared to the original dataset. The rockburst early warning model V-soft achieves peak accuracy and F1 scores of 0.9394 and 0.9173, respectively, demonstrating performance improvements of 6.59-15.68% in accuracy and 14.11-27.21% in F1 score compared to conventional machine learning algorithms and ensemble classifiers. This highlights its superior discriminative capability and robustness in predicting high-intensity rockburst events. Applying the rockburst risk assessment system to the mine enables one-click intelligent rockburst early warning.