Publication Date:
2017-04-04
Description:
The computing techniques currently available for the seismic monitoring
allow advanced analysis. However, the correct event classification remains
a critical aspect for the reliability of real time automatic analysis.
Among the existing methods, neural networks may be considered efficient
tools for detection and discrimination, and may be integrated into
intelligent systems for the automatic classification of seismic events. In
this work we apply an unsupervised technique for analysis and classification
of seismic signals recorded in the Mt. Vesuvius area in order to improve
the automatic event detection. The examined dataset contains
about 1500 records divided into four typologies of events: earthquakes,
landslides, artificial explosions, and “other” (any other signals not included
in the previous classes). First, the Linear Predictive Coding (LPC)
and a waveform parametrization have been applied to achieve a significant
and compact data encoding. Then, the clustering is obtained using
a Self-Organizing Map (SOM) neural network which does not require an
a-priori classification of the seismic signals, groups those with similar
structures, providing a simple framework for understanding the relationships
between them. The resulting SOM map is separated into different
areas, each one containing the events of a defined type. This means
that the SOM discriminates well the four classes of seismic signals.
Moreover, the system will classify a new input pattern depending on its
position on the SOM map. The proposed approach can be an efficient instrument
for the real time automatic analysis of seismic data, especially
in the case of possible volcanic unrest.
Description:
Published
Description:
S0446
Description:
5V. Sorveglianza vulcanica ed emergenze
Description:
JCR Journal
Description:
open
Keywords:
Mt. Vesuvius
;
Neural analysis
;
04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
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