In order to accurately identify mine microseismic signals, this paper proposes a VGG4-CNN deep learning network model suitable for identifying mine microseismic signals. The model is written in Python language and built based on the PyTorch deep learning network architecture framework. Based on the time-domain characteristics of the microseismic signals of 9 types of events such as rock fracture, blasting operations, and background noise in the mine production process, VGG4-CNN has realized the supervised learning training and classification recognition application of 3 835 sets of mine microseismic signal data. The research results show that the recognition accuracy of the VGG4-CNN neural network constructed in this paper is as high as 94 %. This model does not require denoising of the original waveform signal and is more robust than other models. The implementation can be performed by a medium-level GPU to meet engineering requirements.
