Improving microseismic event and quarry blast classification using Artificial Neural Networks based on Principal Component Analysis

The discrimination of microseismic events and quarry blasts has been examined in this paper. To do so, Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) have been used. The procedure proposed has been tested on 22 seismic parameters of 1600 events. In this work, the PCA has been used to transform the original dataset into a new dataset of uncorrelated variables. The new dataset generated has been used as input for ANN and compared to Logistic Regression (LR), Bayes and Fisher classifiers, which classify microseismic events and quarry blasts. The results have shown that PCA is effective for rating variables and reducing data dimension. Furthermore, the classification result based on PCA has been better than those based Ref. [22] and without PCA methods. Moreover, the ANN classifier has obtained the best classification result. The Matthew's Correlation Coefficient (MCC) results of the PCA, Ref. [22] and without PCA based methods have reached 89.00%, 73.68% and 82.04%, respectively, thus showing the reliability and potential of the PCA based method. •Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) are used to improve the classification of microseismic events and quarry blasts.•The classification results based on PCA are better than those based on [22] and without PCA methods.•The classification results using the ANN classifier are better than the Logistic Regression (LR), the Bayes and Fisher classifiers.