Abstract:Purpose: In response to the difficulties in classifying microseismic blasting signals, heavy manual classification workload, and low classification accuracy in the application of microseismic monitoring technology, this article innovatively proposes the waveform automatic classification technology of support vector machine, aiming to solve the existing application problems of microseismic monitoring systems. Method: This article conducted time-domain and frequency-domain analysis on the microseismic signals and artificial blasting signals collected by the BSN microseismic monitoring system. Nine waveform features, including signal-to-noise ratio, P-wave arrival time, first peak, maximum peak, main frequency, and P-wave main frequency, were obtained. After analysis and comparison, five typical features were selected. Finally, SVM method was used to train the above typical features, and an automatic classification model for microseismic and blasting signals was established. The actual on-site application analysis was conducted in a metal mine in Guangxi. Result: The microseismic accuracy is 80.28%, and the blasting accuracy is 86.36%. Conclusion: The recognition and classification methods of the classification model studied in this article have achieved a classification accuracy of over 80%, indicating that the results can alleviate the labor intension of processing personnel and promote the development of fully automatic mining microseismic monitoring systems. Significance: The classification method research ideas provided in this article can effectively solve the difficulties in on-site application of microseismic monitoring systems, and the analysis methods can not only be applied in microseismic processing, but also further used in microseismic analysis, which is of great significance for achieving real-time monitoring services for mining microseismic.