Publication Date:
2017-04-04
Description:
This paper reports on the unsupervised analysis of seismic signals recorded by four
stations situated on the Vesuvius area in Naples, Italy. The dataset under examination is composed
of earthquakes and false events like thunders, quarry blasts and man-made undersea explosions.
The goal is to use these specific data for comparing the performance of three projection methods
that are well known to be able to exploit structures and organizes data, providing a framework for
understanding and interpreting the relationships between data items, and suggesting simple
descriptions of these relationships. The three unsupervised techniques under examination are:
Principal Component Analysis (PCA), which is linear, Self-Organizing Map (SOM) and
Curvilinear Component Analysis (CCA), which are nonlinear. The results show that, among the
above techniques, SOM can better visualize the complex set of high-dimensional data allowing to
discover their intrinsic clusters structure and eventually discriminate the earthquakes from the
false events either natural (thunder) or artificial (quarry blast and undersea explosions).
Description:
Unpublished
Description:
PARIS
Description:
open
Keywords:
seismic signals
;
unsupervised clustering techniques
;
05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
Conference paper
Format:
435161 bytes
Format:
application/pdf
Permalink
