Journal Published Online: 22 December 2016
Volume 45, Issue 1

Classification of Seismic Events and Quarry Blasts Using Singular Value Decomposition and Support Vector Machine



Investigating two types of seismic signals often misclassified in practice, seismic events and quarry blasts, two novel approaches using singular value decomposition (SVD) to extract features of the main wavelet coefficients (WCs) and product function components (PFs) and the support vector machine (SVM) to classify them are proposed. This research collected and preprocessed 200 seismic events and 200 quarry blasts from the Yongshaba mine, China. Discrete wavelet transform (DWT) and local mean decomposition (LMD) were used to decompose the signals into several WCs and PFs, respectively, and the correlation coefficient and variance contribution ratio were used to select the main WCs and PFs. Finally, the singular value features of the selected six WCs and PFs, which can discriminate between seismic events and quarry blasts, were extracted, and the features were input to backpropagation (BP) neural network, Bayes, SVM, and logistic regression (LR) classifiers. The results show that SVD can effectively extract signal features, and that the SVM classifier offers better classification results than the BP neural network, Bayes, and LR classifiers. In addition, the LMD-SVD-SVM-based method is better than the DWT-SVD-SVM-based method in accuracy, specificity, and sensitivity, with values of 96.0 %, 97.0 %, and 95.0 %, and 95.5 %, 97.0 %, and 94.0 %, respectively. Therefore, DWT and LMD based on SVD and SVM techniques provide useful approaches to seismic event and quarry-blast classification.

Author Information

Li, X.
School of Resources and Safety Engineering, Central South Univ., Changsha, CN
Shang, X.
School of Resources and Safety Engineering, Central South Univ., Changsha, CN
Peng, K.
State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing Univ., Chongqing 400044, CN College of Resources and Environmental Science, Chongqing Univ., Chongqing, CN
Pages: 12
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Stock #: JTE20160136
ISSN: 0090-3973
DOI: 10.1520/JTE20160136