Nonlinear Exploratory Data Analysis Applied to Seismic Signals

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.