Sismo - volcano classification in real time based on Empirical Mode Decomposition (EMD) and Machine Learning

Abstract:

This research proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral and cepstral domains, extracted from the EMD of the signals, as well as a set of pre-processing and instrument correction techniques. Due to the fact that three component seismometers are currently being installed in seismic networks worldwide, the proposed approach uses three-component sensors to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis (PCA) method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine (SVM) providing the best results. The presented investigation was tested with a large database generated by "Instituto Geofisico del Peru" (IGP) that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%, the analysis of these results leads to establishing indicators for volcanic hazard monitoring.