基于深度学习方法的矿山微震信号分类识别研究

为了精准识别矿山微震信号,本文提出了一种适用于识别矿山微震信号的VGG4-CNN深度学习网络模型,该模型采用Python语言进行编写,基于PyTorch深度学习网络架构框架进行搭建。根据矿山生产过程中的岩石破裂、爆破作业、背景噪声等9类事件的微震信号的时域特征,VGG4-CNN深度学习网络模型实现了对3 835组矿山微震信号数据进行监督学习训练和分类识别应用。研究结果表明:本文构建的VGG4-CNN神经网络识别精度高达94 %,在采用该模型时不需要对原有波形信号进行去噪且鲁棒性强于现存其他模型,可在中等层次GPU上实现,满足工程需要。

Research on classification and identification of mine microseismic signals based on deep learning method

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.