Automatic Classification of seismic signals of the Chilean Llaima Volcano using Cartesian Genetic Programming based Artificial Neural Network

Abstract

Volcanoes are ruptures in the earth's crust unleashing the dormant forces lying buried deep beneath the crust. The present work is an endeavour towards the quest of automatic volcanic event classification. We propose a volcanic event classification system based on Cartesian Genetic Programming based Artificial Neural Network (CGPANN). CGPANN is a technique for generation of ANN networks without any constraints on network size and topology. Two types of volcanic events for the Chilean Llaima volcano, long period (LP), related to pressure in the volcanic ducts occurring at discrete periods, and volcano tectonic (VT), that is due to the rock fracture, are classified in the present work. The system shows over 80% correct classification for unseen events. The current work also attempts to explore the networks generated and features selected in order to gain an insight into the underlying processes.