基于微震特征参数的秦岭隧洞岩爆实时预测模型OA北大核心CSTPCD

Rockburst is one of the main disasters in the construction of deep earth engineering,and microseismic monitoring is the main method of short-term prediction for rockburst.In order to solve the problem that the short-term prediction of rockburst mainly depends on experience,a database was established consisting of the microseis-mic monitoring and the rockburst record events at Qinling Tunnel of the water diversion project from Hanjiang River to Weihe River.Based on all characteristic parameters during a period,e.g.,energy,position,and magnitude of the micro seismic event,a real-time prediction model for rockburst was established using the convolutional neural network.Besides,the influence of work surface position on rockburst was also considered.According to the train-ing,validation and test results,the model structure,selection of lookback time,prediction time and training ep-och were optimized.The model could reasonably describe the influence of the distribution feature of microseismic and the construction progress on the rockburst probability.Based on the test results,the model can predict the probability of rockburst in the next 48 hours continuously.The accuracy of the prediction is more than 80％,which provides an effective technical way for real-time prediction of rockburst.