The automated classification of seismic volcanic sig- nals has been faced with several different pattern recognition approaches. Among them, hidden Markov models (HMMs) have been advocated as a cost-effective option having the advantages of a straightforward Bayesian interpretation and the capacity of dealing with seismic sequences of different lengths. In the volcano seismology scenario, HMM-based classification schemes were only based on a standard and purely generative scheme, i.e., the Bayes rule: training an HMM per class and classifying an incoming seismic signal according to the class whose model shows the highest likelihood. In this paper, a novel HMM-based classification approach for pretriggered seismic volcanic signals is proposed. The main idea is to enrich the classical HMM scheme with a discriminative step that is able to recover from situations when the classical Bayes classification rule is not sufficient. More in detail, a generative embedding scheme is used, which employs the models to map the signals into a vector space, which is called generative embedding space. In such a space, any discriminative vector-based classifier can be applied. A thorough set of experiments, which is carried out on pretriggered signals recorded at Galeras Volcano in Colombia, shows that the proposed approach typically outper- forms standard HMM-based classification schemes, also in some cross-station cases.

Classification of Seismic Volcanic Signals Using Hidden Markov Models-based Generative Embeddings

BICEGO, Manuele;
2013-01-01

Abstract

The automated classification of seismic volcanic sig- nals has been faced with several different pattern recognition approaches. Among them, hidden Markov models (HMMs) have been advocated as a cost-effective option having the advantages of a straightforward Bayesian interpretation and the capacity of dealing with seismic sequences of different lengths. In the volcano seismology scenario, HMM-based classification schemes were only based on a standard and purely generative scheme, i.e., the Bayes rule: training an HMM per class and classifying an incoming seismic signal according to the class whose model shows the highest likelihood. In this paper, a novel HMM-based classification approach for pretriggered seismic volcanic signals is proposed. The main idea is to enrich the classical HMM scheme with a discriminative step that is able to recover from situations when the classical Bayes classification rule is not sufficient. More in detail, a generative embedding scheme is used, which employs the models to map the signals into a vector space, which is called generative embedding space. In such a space, any discriminative vector-based classifier can be applied. A thorough set of experiments, which is carried out on pretriggered signals recorded at Galeras Volcano in Colombia, shows that the proposed approach typically outper- forms standard HMM-based classification schemes, also in some cross-station cases.
2013
volcanic signals; pattern recognition; hidden markov models
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/470952
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