Decision Tree Model for Rockburst Prediction Based on Microseismic Monitoring

Rockburst is an extremely complex dynamic instability phenomenon for rock underground excavation. It difficult to predict and evaluate the rank level of rockburst in practice. Microseismic monitoring technology has been adopted obtain microseismic events microcrack mass rockburst. The possibility can be reflected by data. In this study, a decision tree was used extract knowledge from predictive model built based on data using algorithm. results were compared with real relationship between feature investigated developed model. show that predictable provides feasible promising approach