Abstract

Microseismic (MS) monitoring technology has been widely used to monitor ground pressure disasters. However, the underground mining environment is complex and contains many types of noise sources. Furthermore, the traditional recognition method entails a complex process with low recognition accuracy for MS signals, so it is difficult to serve for the safe production of mines. Therefore, this study established a hybrid model combining the singular spectrum analysis (SSA) method, convolutional neural networks (CNN), and long short-term memory networks (LSTM). First, the principal components of monitoring signals were extracted with the SSA method, and then spatial and temporal features of monitoring signals were separately extracted with the CNN and LSTM. Based on actual field data collected from Xiadian Gold Mine, the hybrid model was compared with the CNN, LSTM, and back-propagation networks (BP), as well as commonly used recognition methods including the support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and linear discriminant analysis (LDA). The results show that the proposed hybrid model can accurately extract data features of monitoring signals and further improve MS signals' recognition performance. Furthermore, the recognition accuracy of mechanical signals in monitoring signals is particularly increased using the hybrid model, which avoids confusion with MS signals.

摘要

微震（MS）监测技术已广泛用于监测地压灾害。然而，地下采矿环境复杂，噪声源种类繁多。此外，传统的识别方法过程复杂，对MS信号的识别准确率低，难以为矿山的安全生产服务。因此，本研究建立了一种结合奇异谱分析（SSA）方法、卷积神经网络（CNN）和长短期记忆网络（LSTM）的混合模型。首先用SSA方法提取监测信号的主成分，然后用CNN和LSTM分别提取监测信号的时空特征。根据夏甸金矿现场采集的实际数据，K -最近邻 (KNN) 和线性判别分析 (LDA)。结果表明，所提出的混合模型能够准确提取监测信号的数据特征，进一步提高MS信号的识别性能。此外，使用混合模型特别提高了监测信号中机械信号的识别准确度，避免了与MS信号的混淆。