Publication Date:
2017-04-04
Description:
States of volcanic activity at Mt Etna develop in well-defined regimes with variable duration
from a few hours to several months. Changes in the regimes are usually concurrent with variations
of the characteristics of volcanic tremor, which is continuously recorded as background
seismic radiation. This strict relationship is useful for monitoring volcanic activity in any
moment and in whatever condition.We investigated the development of tremor features and its
relation to regimes of volcanic activity applying pattern classification techniques. We present
results from supervised and unsupervised classification methods applied to 425 patterns of
volcanic tremor recorded between 2001 July and August, when a volcano unrest occurred.
Support Vector Machine (SVM) and multilayer perceptron (MLP) were used as pattern
classifiers with supervised learning. For the SVM and MLP training, we considered four target
classes, that is, pre-eruptive, lava fountains, eruptive and post-eruptive. Using a leave one
out testing scheme, SVM reached a score of 94.8 per cent of patterns matching the actual
class membership, whereas MLP achieved 81.9 per cent of matching patterns. The excellent
results, in particular those obtained with SVM, confirmed the reproducibility of the a priori
classification.
Unsupervised classification was carried out using cluster analysis (CA) and self-organizing
maps (SOM). The clusters identified in unsupervised classification formed well-defined
regimes, which can be easily related to the four a priori classes aforementioned. Besides,
CA found a further cluster concurrent with the climax of eruptive activity. Applying a proper
colour-coding to the microclusters (the so-called best matching units) identified by SOM, it
was visually possible to follow the development of the characteristics of the tremor data with
time, highlighting transitional stages from a regime of volcanic activity to another one.
We conclude that supervised and unsupervised classification methods can be conveniently
implemented as complementary tools for an in-depth understanding of the relationships between
tremor data and volcanic phenomena.
Description:
Published
Description:
1132 - 1144
Description:
1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
Description:
1.5. TTC - Sorveglianza dell'attività eruttiva dei vulcani
Description:
JCR Journal
Description:
reserved
Keywords:
neural networks
;
fuzzy logic
;
persistance
;
memory
;
correlations
;
clustering
;
Volcano seismology
;
Statistical seismology
;
Volcano monitoring
;
04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology
;
04. Solid Earth::04.06. Seismology::04.06.09. Waves and wave analysis
;
04. Solid Earth::04.06. Seismology::04.06.10. Instruments and techniques
;
04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring
;
05. General::05.01. Computational geophysics::05.01.01. Data processing
;
05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks
;
05. General::05.01. Computational geophysics::05.01.04. Statistical analysis
;
05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
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