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Understanding the timing of eruption end using a machine learning approach to classification of seismic time series.

Authors :
Manley, Grace F.
Pyle, David M.
Mather, Tamsin A.
Rodgers, Mel
Clifton, David A.
Stokell, Benjamin G.
Thompson, Glenn
Londoño, John Makario
Roman, Diana C.
Source :
Journal of Volcanology & Geothermal Research. Sep2020, Vol. 401, pN.PAG-N.PAG. 1p.
Publication Year :
2020

Abstract

The timing and processes that govern the end of volcanic eruptions are not yet fully understood, and there currently exists no systematic definition for the end of a volcanic eruption. Currently, end of eruption is established either by generic criteria (typically 90 days after the end of visual signals of eruption) or criteria specific to a given volcano. We explore the application of supervised machine learning classification methods: Support Vector Machine, Logistic Regression, Random Forest and Gaussian Process Classifiers and define a decisiveness index D to evaluate the consistency of the classifications obtained by these models. We apply these methods to seismic time series from two volcanoes chosen because they display contrasting styles of eruption: Telica (Nicaragua) and Nevado del Ruiz (Colombia). We find that, for both volcanic systems, the end-date we obtain by classification of seismic data is 2–4 months later than end-dates defined by the last occurrence of visual eruption (such as ash emission). This finding is in agreement with previous, general definitions of eruption end and is consistent across models. Our classifications have a higher correspondence of eruptive activity with visual activity than with database records of eruption start and end. We analyze the relative importance of the different features of seismic activity used in our models (e.g. peak event amplitude, daily event counts) and find little consistency between the two volcanic systems in terms of the most important features which determine whether activity is eruptive or non-eruptive. These initial results look promising and our approach may offer a robust tool to help determine when an eruption has ended in the absence of visual confirmation. • Machine learning classification applied to time series derived from seismic data. • End-dates obtained by classification are 2–4 months later than the last evidence of visual eruption. • These end-dates are in agreement with previous, non-physical definitions of eruption end. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03770273
Volume :
401
Database :
Academic Search Index
Journal :
Journal of Volcanology & Geothermal Research
Publication Type :
Academic Journal
Accession number :
144830017
Full Text :
https://doi.org/10.1016/j.jvolgeores.2020.106917