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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/6596

Authors: Di Stefano, A.*
Currenti, G.*
Del Negro, C.*
Fortuna, L.*
Nunnari, G.*
Title: Integrated inversion of numerical geophysical models using artificial neural networks
Title of journal: World Scientific series on nonlinear science, series B
Series/Report no.: /15 (2010)
Issue Date: 2010
Keywords: Identification, Modeling, Numerical methods.
Abstract: A 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.
Appears in Collections:04.08.99. General or miscellaneous
Papers Published / Papers in press

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