Integrated inversion of numerical geophysical models using artificial neural networks

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.