Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM

Abstract

The recognition and classification of the multi-channel microseismic waveform are important for mine hazard prediction. It is widely used to design the corresponding waveform feature for recognition and classification of the microseismic waveform by hand. The process of designing features manually is arduous and the results of recognition and classification are not ideal. In this paper, we propose a method combining Deep Convolutional Neural Networks (DCNN) with Support Vector Machine (SVM) for identifying the microseismic waveform automatically. We constructed a DCNN structure to train the optimal weight model named the DCNN-Model. The DCNN-Model is used as a tool for extracting features from multi-channel waveforms. After combining the extracted features, we used SVM to classify multi-channel waveforms. We compared the outputs of other classifiers, such as Random Forest and k-Nearest Neighbor (KNN). To extend the dataset of DCNN training and extract the essential characteristics of waveform images more accurately, we pre-process the raw data by means of filtering and de-nosing. The experiment shows that the recognition and classification method is of practical value, and the accuracy rate can reach as high as 98.18%.