Microseismic Event Recognition and Transfer Learning Based on Convolutional Neural Network and Attention Mechanisms

Abstract

Microseismic monitoring technology is widely used in tunnel and coal mine safety production. For signals generated by ultra-weak microseismic events, traditional sensors encounter limitations in terms of detection sensitivity. Given the complex engineering environment, automatic multi-classification of microseismic data is highly required. In this study, we use acceleration sensors to collect signals and combine the improved Visual Geometry Group with a convolutional block attention module to obtain a new network structure, termed CNN_BAM, for automatic classification and identification of microseismic events. We use the dataset collected from the Hanjiang-to-Weihe River Diversion Project to train and validate the network model. Results show that the CNN_BAM model exhibits good feature extraction ability, achieving a recognition accuracy of 99.29%, surpassing all its counterparts. The stability and accuracy of the classification algorithm improve remarkably. In addition, through fine-tuning and migration to the Pan II Mine Project, the network demonstrates reliable generalization performance. This outcome reflects its adaptability across different projects and promising application prospects.