Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/6596
AuthorsDi Stefano, A.* 
Currenti, G.* 
Del Negro, C.* 
Fortuna, L.* 
Nunnari, G.* 
TitleIntegrated inversion of numerical geophysical models using artificial neural networks
Issue Date2010
Series/Report no./15 (2010)
URIhttp://hdl.handle.net/2122/6596
KeywordsIdentification, Modeling, Numerical methods.
Subject Classification04. Solid Earth::04.08. Volcanology::04.08.99. General or miscellaneous 
AbstractA uni ed modelling procedure is proposed to jointly interpret the variations observed in geophysical data and to properly take into account the relation- ship between the intrusive processes and the geophysical variations expected at the ground surface. We focus on the joint inversion of geophysical data by a procedure based on Arti cial Neural Network (ANN) for the estimation of the volcanic source parameters. As forward model, we develop a 3D numerical model based on Finite Element Method (FEM) for computing ground deforma- tion, magnetic and gravity changes caused by magmatic overpressure sources, with the aim to consider a more realistic description of Etna volcano, including the e ects of topography and medium heterogeneities.
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