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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/8098

Authors: Di Salvo, R.*
Montalto, P.*
Nunnari, G.*
Neri, M.*
Puglisi, G.*
Title: Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003
Title of journal: Journal of Volcanology and Geothermal Research
Series/Report no.: /251(2013)
Publisher: Elsevier B.V.
Issue Date: 2013
DOI: 10.1016/j.jvolgeores.2012.02.007
URL: http://www.sciencedirect.com/science/article/pii/S0377027312000443
Keywords: data mining
features extraction
time series clustering
self organizing maps
Etna
summit and flank eruptions
Abstract: Time 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.
Appears in Collections:Papers Published / Papers in press
04.01.99. General or miscellaneous
04.01.02. Geological and geophysical evidences of deep processes
04.02.99. General or miscellaneous
04.03.99. General or miscellaneous
04.06.99. General or miscellaneous
04.07.99. General or miscellaneous
04.08.99. General or miscellaneous
05.01.99. General or miscellaneous
05.01.01. Data processing
05.01.04. Statistical analysis

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