Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/8098
AuthorsDi Salvo, R.* 
Montalto, P.* 
Nunnari, G.* 
Neri, M.* 
Puglisi, G.* 
TitleMultivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003
Issue Date2013
Series/Report no./251(2013)
DOI10.1016/j.jvolgeores.2012.02.007
URIhttp://hdl.handle.net/2122/8098
Keywordsdata mining
features extraction
time series clustering
self organizing maps
Etna
summit and flank eruptions
Subject Classification04. Solid Earth::04.01. Earth Interior::04.01.99. General or miscellaneous 
04. Solid Earth::04.01. Earth Interior::04.01.02. Geological and geophysical evidences of deep processes 
04. Solid Earth::04.02. Exploration geophysics::04.02.99. General or miscellaneous 
04. Solid Earth::04.03. Geodesy::04.03.99. General or miscellaneous 
04. Solid Earth::04.06. Seismology::04.06.99. General or miscellaneous 
04. Solid Earth::04.07. Tectonophysics::04.07.99. General or miscellaneous 
04. Solid Earth::04.08. Volcanology::04.08.99. General or miscellaneous 
05. General::05.01. Computational geophysics::05.01.99. General or miscellaneous 
05. General::05.01. Computational geophysics::05.01.01. Data processing 
05. General::05.01. Computational geophysics::05.01.04. Statistical analysis 
AbstractTime series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information froma large collection of data. Finding useful similar trends inmultivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of researchwhere different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.
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