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

Abstract

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 none-eruption epochs is impossible. We then apply nonsupervised algorithms to the whole set of data obtaining a confirmation of the findings of supervised nets. This difficulty in recognizing patterns characteristic of prelateral 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. Copyright 2007 by the American Geophysical Union.