Machine Learning Based Comparative Analysis for the Classification of Earthquake Signals

This research aims at classifying earthquake signals from seismic noises caused due to anthropogenic activities. We aim at designing a seismic classifier for classifying true earthquake signals so as to reduce the false alarms thereby avoiding excessive data logging due to cultural noise. Based on theoretical and experimental consideration, a set of time and frequency domain features are extracted and used as features to train the supervised classifier network, viz., k-nearest neighbor (k-NN), maximum likelihood (ML), artificial neural network (ANN), and support vector machine (SVM). Two datasets were used in this research work K-NET (Kyoshin Network), Japan and strong motion seismic data recorded at CSIR-CSIO, Chandigarh using BASALT accelerograph of Kinemetrics Inc. Comparative analysis of the classifiers shows that SVM outperforms the other methods with an accuracy of 99.60%.