Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/6590
AuthorsDi Stefano, A.* 
Currenti, G.* 
Del Negro, C.* 
Fortuna, L.* 
Nunnari, G.* 
TitleFEM and ANN combined approach for predicting pressure source
Issue Date2010
Series/Report no./17 (2010)
DOI10.5194/npg-17-273-2010
URIhttp://hdl.handle.net/2122/6590
KeywordsFEM, ANN, Etna volcano
Subject Classification04. Solid Earth::04.08. Volcanology::04.08.99. General or miscellaneous 
AbstractA 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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