Neural networks and SAR interferometry for the characterization of seismic events

Satellite SAR Interferometry (InSAR) has been already proven to be effective in the analysis of seismic events. In fact, the surface displacement field obtained by InSAR application contains useful information to define the fault geometry (such as dip and strike angles, width, length), the extension of the rupture, the distribution of slip on the fault plain. However, the solution of the inverse problem, which means to recover the source parameters from the knowledge of InSAR surface displacement field, is rather complex. In this work we propose an inversion approach for the seismic source classification and the fault parameter quantitative retrieval based on neural networks. The network is trained by using a simulated data set generated by means of a forward model. The application of the methodology has been validated with a set of experimental data corresponding to different types of seismic events.