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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/2276
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dc.contributor.authorallEsposito, A. M.; Dipartimento di Fisica, Università di Salerno, INFN, and INFM Salernoen
dc.contributor.authorallScarpetta, S.; Dipartimento di Fisica, Università di Salerno, INFN, and INFM Salernoen
dc.contributor.authorallGiudicepietro, F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italiaen
dc.contributor.authorallMasiello, M.; Dipartimento di Fisica, Università di Salerno, INFN, and INFM Salernoen
dc.contributor.authorallPugliese, L.; IIASS, via Pellegrino 19, Vietri sul Mare (SA), Italyen
dc.contributor.authorallEsposito, A.; Seconda Università di Napoli, and INFM Salerno, Italyen
dc.date.accessioned2007-07-03T08:55:12Zen
dc.date.available2007-07-03T08:55:12Zen
dc.date.issued2006en
dc.identifier.urihttp://hdl.handle.net/2122/2276en
dc.description.abstractThis 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.en
dc.format.extent371813 bytesen
dc.format.mimetypeapplication/pdfen
dc.language.isoEnglishen
dc.publisher.nameSpringer-Verlagen
dc.relation.ispartofWIRN/NAIS 2005, LNCS 3931,en
dc.subjectNONEen
dc.titleNonlinear Exploratory Data Analysis Applied to Seismic Signalsen
dc.typearticleen
dc.description.statusPublisheden
dc.type.QualityControlPeer-revieweden
dc.description.pagenumber70-77en
dc.subject.INGV05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networksen
dc.relation.referencesDemartines, P., Herault, J.: Curvilinear Component Analysis: A Self Organizing Neural Network for Nonlinear Mapping of Data Sets. IEEE Trans. on Neural Networks, Vol. 8 (1997) 48-154 Herault, J., Guerin-Dugue, A., Villemain, P.: Searching for the Embedded Manifolds in Highdimensional Data, Problems and Unsolved Questions. Proceedings of ECANN Bruge (2002) Jollife. I.T.:Principal Component Analysis. Springer Verlag, New York (1986) Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J.: SOM_PAK Program Package. Report A31. Helsinki University, Finland (1996) Kohonen, T.: Self-Organizing Maps. Series in Information Sciences, Vol. 30. Springer, Heidelberg. Second ed (1997) Lee, J.A., Lendasse, A., Donckers, N., Verleysen, M.: A Robust Nonlinear Projection Method. Proceedings of ESANN’2000 D-Facto pubbl. (2000) 13-20 Makhoul, J.: Linear prediction: a Tutorial Review. IEEE Vol. 63 (1975). 561-580. Esposito, A. M., Giudicepietro, F., Scarpetta, S., D’Auria, L., Marinaro, M., Martini .M.: Automatic Discrimination of Landslides Seismic Signals at Stromboli Volcano using Neural Network (submitted) Masiello, S., Esposito, A. M., Scarpetta, S., Giudicepietro, F., Esposito, A., Marinaro, M.: Application of Self Organized Maps and Curvilinear Components Analysis to the Discrimination of Vesuvius Seismic Signals. To appear in the Proceedings of the Workshop on Self Organizing Map (WSOM), Paris, 5-8 September (2005)en
dc.description.fulltextreserveden
dc.contributor.authorEsposito, A. M.en
dc.contributor.authorScarpetta, S.en
dc.contributor.authorGiudicepietro, F.en
dc.contributor.authorMasiello, M.en
dc.contributor.authorPugliese, L.en
dc.contributor.authorEsposito, A.en
dc.contributor.departmentDipartimento di Fisica, Università di Salerno, INFN, and INFM Salernoen
dc.contributor.departmentDipartimento di Fisica, Università di Salerno, INFN, and INFM Salernoen
dc.contributor.departmentIstituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italiaen
dc.contributor.departmentDipartimento di Fisica, Università di Salerno, INFN, and INFM Salernoen
dc.contributor.departmentIIASS, via Pellegrino 19, Vietri sul Mare (SA), Italyen
dc.contributor.departmentSeconda Università di Napoli, and INFM Salerno, Italyen
item.openairetypearticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextrestricted-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
crisitem.author.deptIstituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione OV, Napoli, Italia-
crisitem.author.deptIstituto Nazionale per la Fisica della Materia Sezione di Salerno and Istituto Nazionale di Fisica Nucleare Gruppo Collegato di Salerno, Italy-
crisitem.author.deptIstituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione OV, Napoli, Italia-
crisitem.author.deptDipartimento di Fisica, Università di Salerno, INFN, and INFM Salerno. Italy-
crisitem.author.deptIIASS, via Pellegrino 19, Vietri sul Mare (SA), Italy-
crisitem.author.deptIstituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione OV, Napoli, Italia-
crisitem.author.orcid0000-0003-2192-3720-
crisitem.author.orcid0000-0001-6198-8655-
crisitem.author.orcid0000-0003-2192-3720-
crisitem.author.parentorgIstituto Nazionale di Geofisica e Vulcanologia-
crisitem.author.parentorgIstituto Nazionale di Geofisica e Vulcanologia-
crisitem.author.parentorgIstituto Nazionale di Geofisica e Vulcanologia-
crisitem.classification.parent05. General-
crisitem.department.parentorgIstituto Nazionale di Geofisica e Vulcanologia-
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