Publikationsdatum:
2017-04-04
Beschreibung:
This paper reports on the unsupervised analysis of seismic signals
recorded in Italy, respectively on the Vesuvius volcano, located in Naples, and
on the Stromboli volcano, located North of Eastern Sicily. The Vesuvius dataset
is composed of earthquakes and false events like thunders, man-made quarry
and undersea explosions. The Stromboli dataset consists of explosion-quakes,
landslides and volcanic microtremor signals. The aim of this paper is to apply
on these datasets three projection methods, the linear Principal Component
Analysis (PCA), the Self-Organizing Map (SOM), and the Curvilinear
Component Analysis (CCA), in order to compare their performance. Since
these algorithms are well known to be able to exploit structures and organize
data providing a clear framework for understanding and interpreting their
relationships, this work examines the category of structural information that
they can provide on our specific sets. Moreover, the paper suggests a
breakthrough in the application area of the SOM, used here for clustering
different seismic signals. The results show that, among the three above
techniques, SOM better visualizes the complex set of high-dimensional data
discovering their intrinsic structure and eventually appropriately clustering the
different signal typologies under examination, discriminating the explosionquakes
from the landslides and microtremor recorded at the Stromboli volcano,
and the earthquakes from natural (thunders) and artificial (quarry blasts and
undersea explosions) events recorded at the Vesuvius volcano.
Beschreibung:
Published
Beschreibung:
Vietri sul Mare
Beschreibung:
1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
Beschreibung:
open
Schlagwort(e):
models for data structure
;
seismic events
;
clustering
;
classification
;
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)
Materialart:
Conference paper
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