Classification of volcano-seismic events using waveforms in the method of k-means clustering and dynamic time warping

Abstract

In most volcano observatories classification of seismic events is made as a routine work to get some information on eruptive activities. Automating the classification with artificial intelligence (AI) techniques is a current issue but many AI techniques are not directly applicable to waveforms because they consist of individually different data lengths. This study develops a simple method in which the distance between waveforms with different lengths is evaluated by dynamic time warping and waveforms are classified by k-means clustering. In this method each of the classified groups is specified by a prototype, one of the waveforms that may represent the group best and good classifications are selected considering high generality and good fitting to the members. The seismic events of Sakurajima volcano are classified in this method and waveforms, spectra, time sequences and source locations are compared among groups for some good classifications. It is revealed that one of the groups consisting of tremor-like waveforms has an intimate connection with a magma ascent event beneath the active crater of the volcano.