Dynamic Bayesian Networks for Real-Time Classification of Seismic Signals

Authors:
Carsten Riggelsen;Matthias Ohrnberger;Frank Scherbaum
Affiliations:
University of Potsdam, Institute of Geosciences, Karl-Liebknecht-Str. 24/25, 14476 Golm, Potsdam, Germany;University of Potsdam, Institute of Geosciences, Karl-Liebknecht-Str. 24/25, 14476 Golm, Potsdam, Germany;University of Potsdam, Institute of Geosciences, Karl-Liebknecht-Str. 24/25, 14476 Golm, Potsdam, Germany
Venue:
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2007

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Abstract

We present a novel method for automatic classification of seismological data streams, focusing on the detection of earthquake signals. We consider the approach as being a first step towards a generic method that provides for classifying a broad range of seismic patterns by modeling the interrelationships between essential features of seismograms in a graphical model. Through a continuous Wavelet transform the features are extracted, yielding a time-frequency-amplitude decomposition. The extracted features obey certain Markov properties, which allows us to form a joint distribution in terms of a Dynamic Bayesian Network. We performed experiments using real seismic data recorded at different stations in the European Broadband Network, for which we achieve an average classification accuracy of 95%.