Abstract
This paper shows a preliminary study to perform a pattern recognition process for seismic events of the Llaima volcano, one of the most active volcanoes in South America. 1622 classified events registered from the Llaima volcano were considered in this study, taken from 2009 to 2011. The events were divided in four classes: TREMOR (TR), LONG-PERIOD (LP), VOLCANO-TECTONICS (VT) and OTHERS (OT). All of them correspond to specific activities. TR and LP events, are related to magmatic fluid through the ducts: continuous flux correspond to TR and discrete flux to LP. VT events occurs when excess of the magmatic pressure provides enough energy for rock failure. The group of OT contains events not related to the three first volcanic classes. Many features extracted from de amplitude, the frequency and the phase of the events were used to characterize the different classes. A classifier step based on Support Vector Machines was implemented to evaluate the contribution of each feature to the classification. The paper shows the results of this process and gives insights for future works.
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Curilem, M. et al. (2014). Feature Analysis for the Classification of Volcanic Seismic Events Using Support Vector Machines. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_15
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DOI: https://doi.org/10.1007/978-3-319-13650-9_15
Publisher Name: Springer, Cham
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