Early Warning Systems for Large Earthquakes: Classification of Near-source and Far-source Stations by using the Bayesian Model Class Selection

Abstract

To estimate the fault dimension of an earthquake in real time, we present a methodology to classify seismic records into near-source or far-source records. This study analyzes peak ground motions and finds the function which best classifies near-source and far-source records based on these parameters. We perform: Bayesian methods to find the coefficients of the linear discriminant function; and Bayesian model class selection to find the best combination of the peak ground motion parameters. Bayesian model class selection shows that the combination of vertical acceleration and horizontal velocity produces the best performance for the classification. The linear discriminant function produced classifies near-source and far-source data and it gives the probability for a station to be near-source, based on the ground motion measurements. This discriminant function is useful to estimate the fault rupture dimension in real time, especially for large earthquakes

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