A contribution to deep learning approaches for automatic classification of volcano-seismic events: deep gaussian processes

Abstract

Automatic classification of volcano-seismic events is a key problem in volcanology. Due to its complexity, Deep Learning (DL) techniques have become the tool of choice for this problem, outperforming classical classifiers. The main drawback of this approach, when applied to the classification of volcanoseismic events, is its tendency to overfit because of the small-size available databases. In this work, we propose and analyze the use of Gaussian Processes (GPs) and Deep Gaussian Processes (DGPs), their hierarchical extension, for volcano-seismic event classification. We empirically prove the adequacy of the proposed modelling with an insightful and exhaustive comparison with state-of-the-art DL-based methods on a seismic database recorded at “Volc´an de Fuego”, in Colima (Mexico). The hierarchical structure of DGPs and the reduced number of parameters to be automatically estimated become essential to achieve an excellent performance even on small databases, capturing well the complex patterns of seismic signals for all classes and in particular for those which have been hardly observed.