Automatic detection of volcano-seismic events by modeling state and event duration in hidden Markov models

Automatic detection of volcano-seismic events by modeling state and event duration in hidden... In this paper we propose an automatic volcano event detection system based on Hidden Markov Model (HMM) with state and event duration models. Since different volcanic events have different durations, therefore the state and whole event durations learnt from the training data are enforced on the corresponding state and event duration models within the HMM. Seismic signals from the Llaima volcano are used to train the system. Two types of events are employed in this study, Long Period (LP) and Volcano-Tectonic (VT). Experiments show that the standard HMMs can detect the volcano events with high accuracy but generates false positives. The results presented in this paper show that the incorporation of duration modeling can lead to reductions in false positive rate in event detection as high as 31% with a true positive accuracy equal to 94%. Further evaluation of the false positives indicate that the false alarms generated by the system were mostly potential events based on the signal-to-noise ratio criteria recommended by a volcano expert. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Volcanology and Geothermal Research Elsevier

Automatic detection of volcano-seismic events by modeling state and event duration in hidden Markov models

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier B.V.
ISSN
0377-0273
eISSN
1872-6097
D.O.I.
10.1016/j.jvolgeores.2016.05.015
Publisher site
See Article on Publisher Site

Abstract

In this paper we propose an automatic volcano event detection system based on Hidden Markov Model (HMM) with state and event duration models. Since different volcanic events have different durations, therefore the state and whole event durations learnt from the training data are enforced on the corresponding state and event duration models within the HMM. Seismic signals from the Llaima volcano are used to train the system. Two types of events are employed in this study, Long Period (LP) and Volcano-Tectonic (VT). Experiments show that the standard HMMs can detect the volcano events with high accuracy but generates false positives. The results presented in this paper show that the incorporation of duration modeling can lead to reductions in false positive rate in event detection as high as 31% with a true positive accuracy equal to 94%. Further evaluation of the false positives indicate that the false alarms generated by the system were mostly potential events based on the signal-to-noise ratio criteria recommended by a volcano expert.

Journal

Journal of Volcanology and Geothermal ResearchElsevier

Published: Sep 15, 2016

References

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