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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/3297

Authors: Esposito, A.*
Esposito, A. M.*
Giudicepietro, F.*
Marinaro, M.*
Scarpetta, S.*
Title: Models for Identifying Structures in the Data: A Performance Comparison
Editors: Apolloni, B.
Issue Date: 2007
Keywords: models for data structure
seismic events
clustering
classification
Abstract: This paper reports on the unsupervised analysis of seismic signals recorded in Italy, respectively on the Vesuvius volcano, located in Naples, and on the Stromboli volcano, located North of Eastern Sicily. The Vesuvius dataset is composed of earthquakes and false events like thunders, man-made quarry and undersea explosions. The Stromboli dataset consists of explosion-quakes, landslides and volcanic microtremor signals. The aim of this paper is to apply on these datasets three projection methods, the linear Principal Component Analysis (PCA), the Self-Organizing Map (SOM), and the Curvilinear Component Analysis (CCA), in order to compare their performance. Since these algorithms are well known to be able to exploit structures and organize data providing a clear framework for understanding and interpreting their relationships, this work examines the category of structural information that they can provide on our specific sets. Moreover, the paper suggests a breakthrough in the application area of the SOM, used here for clustering different seismic signals. The results show that, among the three above techniques, SOM better visualizes the complex set of high-dimensional data discovering their intrinsic structure and eventually appropriately clustering the different signal typologies under examination, discriminating the explosionquakes from the landslides and microtremor recorded at the Stromboli volcano, and the earthquakes from natural (thunders) and artificial (quarry blasts and undersea explosions) events recorded at the Vesuvius volcano.
Appears in Collections:Conference materials
05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks

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