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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/7274

Authors: Esposito, A. M.*
Title: Clustering of Hybrid Events at Stromboli Volcano (Italy)
Editors: Apolloni, B.; Department of Computer Science University of Milano, Italy
Bassis, S.; Department of Computer Science University of Milano, Italy
Esposito, A.; Dep. of Psychology, Second University of Naples, Caserta, Italy
Morabito, C.F.; Università Mediterranea di Reggio Calabria
Issue Date: 3-Jun-2011
Keywords: Hybrid events, clustering, SOM, Hierarchical Clustering, neural
Abstract: The last effusive eruption on February 27, 2007 at Stromboli volcano was characterized by the occurrence of a particular typology of seismic events named “hybrids”. During March about 4000 of these signals were recorded, and three main swarms happened: the first one on days 6-8, with more than 1200 events; the second one on day 20, with more than 400 events; and the third one on day 22, with about 600 events. The study of these events and specifically their location is the main purpose of this work because it not only characterizes a particular aspect of the 2007 effusive eruption but at the same time can improve the understanding of the eruptive processes of the volcano. Thus, in order to locate them it was first necessary to group the signals according to their waveform similarity and then apply relative location techniques on individual families. To perform the clustering an unsupervised SOM neural network was used. This technique is capable of working without any “a-priori” information about data distribution and structure. Its results have revealed differences in the families of events recorded during and between the swarms, underlying from a volcanological point different locations or source mechanisms of the involved structures. Moreover, they have shown to be consistent compared to those obtained by applying the Hierarchical Clustering technique. However, in contrast to the latter, the SOM clustering does not critically depend on its parameters and allows for an easier result visualization and interpretation.
Appears in Collections:Conference materials
05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks

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