Nonlinear Exploratory Data Analysis Applied to Seismic Signals

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 Strom...

Full description

Saved in:
Bibliographic Details
Published inLecture notes in computer science pp. 70 - 77
Main Authors Esposito, Antonietta M., Scarpetta, Silvia, Giudicepietro, Flora, Masiello, Stefano, Pugliese, Luca, Esposito, Anna
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Connectionism. Neural networks
Empty Triangle
Exact sciences and technology
Linear Predictive Code
Neighborhood Parameter
Prototype Vector
Seismic Signal
Projection method
Data analysis
Exploratory analysis
Self organization
Seismic analysis
Earthquakes
Seismic signal
Database
Neural network
Signal analysis
Principal component analysis
Online AccessGet full text

Cover

More Information
Summary: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.
ISBN:3540331832
9783540331834
ISSN:0302-9743
1611-3349
DOI:10.1007/11731177_11