Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform

Abstract

Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring. The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic events for providing accurate information of rockmass. The accurate identification of microseismic events and blasts determines the timeliness and accuracy of early warning of microseismic monitoring technology. An image identification model based on Convolutional Neural Network (CNN) is established in this paper for the seismic waveforms of microseismic events and blasts. Firstly, the training set, test set, and validation set are collected, which are composed of 5250, 1500, and 750 seismic waveforms of microseismic events and blasts, respectively. The classified data sets are preprocessed and input into the constructed CNN in CPU mode for training. Results show that the accuracies of microseismic events and blasts are 99.46% and 99.33% in the test set, respectively. The accuracies of microseismic events and blasts are 100% and 98.13% in the validation set, respectively. The proposed method gives superior performance when compared with existed methods. The accuracies of models using logistic regression and artificial neural network (ANN) based on the same data set are 54.43% and 67.9% in the test set, respectively. Then, the ROC curves of the three models are obtained and compared, which show that the CNN gives an absolute advantage in this classification model when the original seismic waveform are used in training the model. It not only decreases the influence of individual differences in experience, but also removes the errors induced by source and waveform parameters. It is proved that the established discriminant method improves the efficiency and accuracy of microseismic data processing for monitoring rock instability and seismicity.