Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/2276
Authors: Esposito, A. M.* 
Scarpetta, S.* 
Giudicepietro, F.* 
Masiello, M.* 
Pugliese, L.* 
Esposito, A.* 
Title: Nonlinear Exploratory Data Analysis Applied to Seismic Signals
Journal: WIRN/NAIS 2005, LNCS 3931, 
Publisher: Springer-Verlag
Issue Date: 2006
Keywords: NONE
Subject Classification05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks 
Abstract: This paper compares three unsupervised projection methods: Principal Component Analysis (PCA), which is linear, Self-Organizing Map (SOM) and Curvilinear Component Analysis (CCA), which are both nonlinear. Performance comparison of the three methods is made on a set of seismic data recorded on Stromboli that includes three classes of signals: explosion-quakes, landslides, and microtremors. The unsupervised analysis of the signals is able to discover the nature of the seismic events. Our analysis shows that the SOM algorithm discriminates better than CCA and PCA on the data under examination.
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