Nonlinear exploratory data analysis applied to seismic signals

Authors:
Antonietta M. Esposito;Silvia Scarpetta;Flora Giudicepietro;Stefano Masiello;Luca Pugliese;Anna Esposito
Affiliations:
Dipartimento di Fisica, Università di Salerno, INFN, and INFM, Salerno;Dipartimento di Fisica, Università di Salerno, INFN, and INFM, Salerno;Osservatorio Vesuviano, INGV, Napoli, Italy;Dipartimento di Fisica, Università di Salerno, INFN, and INFM, Salerno;IIASS, Vietri sul Mare (SA), Italy;Seconda Università di Napoli, and INFM Salerno, Italy
Venue:
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Year:
2005

Citing 2
Cited 1

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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.