Publication Date:
2017-04-04
Description:
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.
Description:
Published
Description:
273-282
Description:
3.6. Fisica del vulcanismo
Description:
JCR Journal
Description:
open
Keywords:
FEM, ANN, Etna volcano
;
04. Solid Earth::04.08. Volcanology::04.08.99. General or miscellaneous
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
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