基于WPD-SVD的矿山微震信号特征提取及分类方法

为减少人工识别矿山微震事件的工作量，提出了基于小波包分解(WPD)和奇异值分解(SVD)提取微震信号特征的方法。首先对爆破震动、岩体破裂、机械干扰和电干扰等4种信号进行4层小波包分解，再利用奇异值分解计算第4层节点上小波包系数构成矩阵得到奇异值。以奇异值为特征值，建立16维特征向量，利用支持向量机(SVM)对400组矿山现场微震信号进行了训练和分类。研究结果表明：与爆破震动、岩体破裂和电干扰信号相比，机械干扰信号的奇异值的差异性最大；SVM的分类正确率达到94.5%，取得了理想的分类效果。

Feature Extraction and Classification of Mine Microseismic Signal Based on WPD-SVD

In order to reduce the work load of artificial recognition of mine microseismic events, a method for feature extraction was proposed based on the wavelet packet decomposition (WPD) and singular value decomposition (SVD). Firstly, blasting vibration, rock fracture, mechanical interference and electrical interference signals were decomposed into 4 layers based on WPD, then the singular value was obtained by calculating the wavelet packet coefficients on the fourth layer through the use of SVD.With the singular value as the eigenvalue, a 16-dimensional eigenvector was established.The support vector machine (SVM) was adopted to train and classify the microseismic signals in 400 sets of mines. The results showed that compared with blasting vibration, rock fracture and electrical interference, the singular value of mechanical interference signal was the most significant. The classification accuracy of SVM reached 94.5%, which achieved the ideal classification effect.