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| Titel |
FEM and ANN combined approach for predicting pressure source parameters at Etna volcano |
| VerfasserIn |
A. Stefano, G. Currenti, C. Negro, L. Fortuna, G. Nunnari |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 17, no. 3 ; Nr. 17, no. 3 (2010-05-20), S.273-282 |
| Datensatznummer |
250013684
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| Publikation (Nr.) |
 copernicus.org/npg-17-273-2010.pdf |
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| Zusammenfassung |
| A hybrid approach for forward and inverse geophysical modeling, based on
Artificial Neural Networks (ANN) and Finite Element Method (FEM), is proposed
in order to properly identify the parameters of volcanic pressure sources
from geophysical observations at ground surface. The neural network is
trained and tested with a set of patterns obtained by the solutions of
numerical models based on FEM. The geophysical changes caused by magmatic
pressure sources were computed developing a 3-D FEM model with the aim to
include the effects of topography and medium heterogeneities at Etna volcano.
ANNs are used to interpolate the complex non linear relation between
geophysical observations and source parameters both for forward and inverse
modeling. The results show that the combination of neural networks and FEM is
a powerful tool for a straightforward and accurate estimation of source
parameters in volcanic regions. |
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