This thesis presents an overview on seismic signals analysis and its related activities
to clustering. The real applications require the use of metrics, algorithms and data to
test hypothesis or to infer them. Hypocenter and focal mechanism of an earthquake
can be determined by the analysis of signals, named waveforms, related to the wave
field produced by earthquakes and recorded by a seismic network. Assuming that
waveform similarity implies the similarity of focal parameters, the analysis of those
signals characterized by very similar shapes can be used to give important details
about the physical phenomena which have generated an earthquake. Recent works
have shown the effectiveness of cross-correlation and/or cross-spectral dissimilarities
to identify clusters of seismic events. In this thesis we propose a new dissimilarity
measure between seismic signals whose reliability has been tested on real seismic data
by computing external and internal validation indices on the obtained clustering.
Results show its superior quality in terms of cluster homogeneity and computational
time with respect to the largely adopted cross correlation dissimilarity
