Classification of pre-eruption and non-pre-eruption epochs at Mount Etna volcano by means of artificial neural networks

Details Classification of pre-eruption and non-pre-eruption epochs at Mount Etna volcano by means of artificial neural networks Classification of pre-eruption and non-pre-eruption epochs at Mount Etna volcano by means of artificial neural networks

May 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007georl..3410311c&link_type=abstract

Geophysical Research Letters, Volume 34, Issue 10, CiteID L10311

Statistics

Computation

Computational Geophysics: Neural Networks, Fuzzy Logic, Machine Learning, Mathematical Geophysics: Time Series Analysis (1872, 4277, 4475), Seismology: Volcano Seismology (8419), Volcanology: Volcanic Hazards And Risks, Geographic Location: Europe

Scientific paper

We apply artificial neural networks to the classification of pre-eruption time epochs of Mount Etna volcano on the basis of variables depending on tectonics and on the volcano `recharging system'. We consider time-epochs from 7 to 30 days and train the supervised nets, with the aim of recognizing the time epochs preceding summit eruptions, lateral eruptions and not preceding any eruption. Tested on a number of independent data sets, these patterns are found to be efficient (75 +/- 10% success) in recognizing pre-summit eruption epochs, while distinguishing pre-lateral from non-pre-eruption epochs is impossible. We then apply non-supervised algorithms to the whole set of data obtaining a confirmation of the findings of supervised nets. This difficulty in recognizing patterns characteristic of pre-lateral eruption epochs is at odds with all previous work and seems to depend on the small size of the eruptive series, which makes unstable the results of any multivariate analysis.

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