Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/12914
Authors: Spampinato, Salvatore* 
Langer, Horst* 
Messina, Alfio* 
Falsaperla, Susanna* 
Title: Short-term detection of volcanic unrest at Mt. Etna by means of a multi-station warning system
Issue Date: 24-Apr-2019
Series/Report no.: /9 (2019)
DOI: 10.1038/s41598-019-42930-3
URI: http://hdl.handle.net/2122/12914
Keywords: Etna, Volcanic tremor
Volcano Monitoring, Pattern recognition
Self organizing map, Fuzzy clustering
Subject Classification04.06. Seismology
04.08. Volcanology 
05.01. Computational geophysics 
Abstract: Early-warning assessment of a volcanic unrest requires that accurate information from monitoring is continuously gathered before volcanic activity starts. Seismic data are an optimal source of such information, overcoming safety problems due to dangerous conditions for field surveys or cloud cover that may hinder visibility. We designed a multi-station warning system based on the classification of patterns of the background seismic radiation, so-called volcanic tremor, by using Self-Organizing Maps (SOM) and fuzzy clustering. The classifier automatically detects patterns that are typical footprints of volcanic unrest. The issuance of the SOM colors on DEM allows their geographical visualization according to the stations of detection; this spatial location makes it possible to infer areas potentially impacted by eruptive phenomena. Tested at Mt. Etna (Italy), the classifier forecasted in hindsight patterns associated with fast-rising magma (typical of lava fountains) as well as a relatively long lead time of the outburst (lava flows from eruptive fractures). Receiver Operating Characteristics (ROC) curves gave an Area Under the Curve (AUC) ∼0.8 indicative of a good detection accuracy that cannot be achieved from a mere random choice.
Appears in Collections:Article published / in press

Files in This Item:
File Description SizeFormat 
2019_SR_Spampinato_et_al.pdf2.96 MBAdobe PDFView/Open
Show full item record

Google ScholarTM

Check

Altmetric