Analysis of the recharging of the volcanic feeder at Mt. Etna using pattern classification of volcanic tremor data and comparison with recent seismic tomography

摘要

ABSTRACT FINAL ID: V41H-06 TITLE: Analysis of the recharging of the volcanic feeder at Mt. Etna using pattern classification of volcanic tremor data and comparison with recent seismic tomography SESSION TYPE: Oral SESSION TITLE: V41H. Surveillance of Volcanic Unrest: New Developments in Multidisciplinary Monitoring Methods I (Video On-Demand) Susanna M R Falsaperla1, Graziella Barberi1, Ornella Cocina1 INSTITUTIONS (ALL): 1. Sez Catania, INGV, Catania, Italy. KKAnalysis is a method of pattern classification based on Self Organizing Maps and Fuzzy Cluster Analysis successfully applied to volcanic tremor data recorded at Mt. Etna [Langer et al., J. Volcan. Geoth. Res., doi:10.1016/j.jvolgeores.2010.11.019, 2010]. The classifier can detect anomalies in the seismic signal long before changes in volcanic tremor amplitude and spectral content become evident, and is particularly useful in highlighting impending paroxysmal eruptive activity, such as lava fountains and intense effusive activity. In this study we propose an application to volcanic tremor data recorded from November 1 2005 to January 31 2006, when strong changes in amplitude and frequency content were detected without any visible activity of the volcano was reported by volcanologists and alpine guides. The classifier detects patterns that we interpret as evidence of recharging of the volcanic feeder at depth. We discuss our results considering stations of the permanent network of Mt. Etna, which is run by INGV, comparing their characteristics resulting from pattern classification. To corroborate our results we also take into account VT seismicity and a recently published seismic tomography, which allows us to look at discontinuities and possible zone of magma transfer at depth.