A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines

Abstract

The discrimination of micro-seismic events (events) and blasts is significant for monitoring and analyzing micro-seismicity in underground mines. To eliminate the negative effects of conventional discrimination methods, a waveform image discriminant method was proposed. Principal component analysis (PCA) was applied to extract the raw features of events and blasts through their waveform images that established by the recorded field data, and transform them into the new uncorrelated features. The amount of initial information retained in the derived features could be determined quantitatively by the contribution rate. The binary classification models were established by utilizing the support vector machine (SVM) algorithm and the PCA derived waveform image features. Results of four groups of cross validation show that the optimal values for the accuracy of events and blasts, total accuracy, and quality evaluation parameter MCC are 97.1%, 93.8%, 93.60%, and 0.8723, respectively. Moreover, the computation efficiency per accuracy (CEA) was introduced to quantitatively evaluate the effects of contribution rate on classification accuracy and computation efficiency. The optimal contribution rate was determined to be 0.90. The waveform image discriminant method can automatically classify events and blasts in underground mines, ensuring the efficient establishment of high-quality micro-seismic databases and providing adequate data for the subsequent seismicity analysis.