The efficient processing of large amounts of data collected by the microseismic monitoring system (MMS), especially the rapid identification of microseismic events in explosions and noise, is essential for mine disaster prevention. Currently, this work is primarily performed by skilled technicians, which results in severe workloads and inefficiency. In this paper, CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multi-channel microseismic signal waveforms. First, data collected by MMS was generated into 6-channel original waveforms based on events. After that, sample data sets of microseismic events, blasts, drillings, and noises were established through manual identification. These datasets were split into training sets and test sets according to a certain proportion, and transfer learning was performed on AlexNet, GoogLeNet, and ResNet50 pre-training network models, respectively. After training and tuning, optimal models were retained and compared with support vector machine classification. Results show that transfer learning models perform well on different test sets. Overall, GoogLeNet performed best, with a recognition accuracy of 99.8%. Finally, the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.

微震监测系统（MMS）采集的大量数据的高效处理，特别是爆炸和噪声中微震事件的快速识别，对于矿山防灾至关重要。目前，这项工作主要由熟练技术人员完成，工作量大、效率低。本文采用基于CNN的迁移学习结合计算机视觉技术，实现了多通道微震信号波形的自动识别和分类。首先，MMS采集的数据根据​​事件生成6通道原始波形。之后，通过人工识别建立了微震事件、爆炸、钻孔和噪声样本数据集。将这些数据集按照一定比例分为训练集和测试集，分别在AlexNet、GoogLeNet、ResNet50预训练网络模型上进行迁移学习。经过训练和调优后，保留最优模型并与支持向量机分类进行比较。结果表明，迁移学习模型在不同的测试集上表现良好。总体而言，GoogLeNet 表现最好，识别准确率达到 99.8%。最后讨论了训练集的数量以及不同类型样本数据的不平衡对分类模型的准确性和有效性可能产生的影响。