Detection and classification of seismic events with progressive multi channel correlation and hidden Markov models
The progressive multi channel correlation (PMCC) detector and a statistical time series classifier based on hidden Markov models (HMMs) are associated in order to detect and classify seismic events at an array station. We classify the signal patterns that have triggered PMCC detections thanks to HMMs. The signal is transformed into sequences of feature vectors, which are used to train HMMs. The features include outputs from the PMCC detector describing the seismic wave propagation. Our method is applied to the automatic classification of regional seismic event, teleseismic event, and noise. The experimental data set comprises records from the Songino array station in Mongolia. Results on test events show an average classification performance of 84% HighlightsWe propose a method to automatically detect and classify seismic events.We associate array processing and time series statistical modelling.Wave propagation features from array processing are used for model training.Our method is applied to discriminate regional and teleseismic events in Mongolia.
Comput. Geosci.