Improving feature extraction in the automatic classification of seismic events. Application to Colima and Arenal volcanoes

Abstract

Monitoring of precursory seismicity in volcanoes is the most reliable and widely used technique in volcano monitoring. Since a visual inspection by human operators is a tedious task in a non-stop monitoring process, Hidden Markov Models have been previously proposed to automatically classify the different types of volcano-seismic events. Mel Frequency Cepstral Coefficients were successfully used as feature vector in this continuous classification system. In this paper seven novel features to be included in the MFCC feature vector are proposed. A very elementary GMM-based classifier has been implemented in order to assess the efficiency of the proposed parameters. Results using hundreds of events recorded from stations situated at Colima (Mexico) and Arenal (Costa Rica) volcanoes show that the proposed features improve the recognition accuracy and therefore they may be relevant in continuous volcano-seismic event automatic classification.