BLIND SOURCE SEPARATION: AN APPLICATION TO THE MT. MERAPI VOLCANO, INDONESIA

Abstract

Independent Component Analysis (ICA) is an emerging new technique in the blind identification of signals recorded in a variety of different fields. ICA tries to find the most statistically independent sources from an observable random vector, with the only restriction that all sources but at most one are non-Gaussian; no other a priori information on sources and mixing dynamic system are needed. The applications of these techniques to the analysis of volcanic time series are relatively few to date. In this paper we show that ICA is a suitable technique to separate a volcanic source component from ocean microseisms background noise in a seismic dataset recorded at the Mt. Merapi volcano, Indonesia. The encouraging results obtained with this methodology in the presented case study support their wider applicability in volcano seismology.