A new neural networks scheme for automatic seismic source analysis from DInSAR data

Satellite SAR Interferometry (InSAR) has already proved its effectiveness in the analysis of seismic events. In fact, measuring the surface displacement field generated by an earthquake can be useful to define fault parameters regarding the geometry (such as dip and strike angles, width, length), the extension of the rupture and the distribution of slip on the fault plain. However, to retrieve the source parameters from InSAR measurements is rather complex since the inversion problem is ill-posed. In this work we propose an inversion approach for retrieving the fault parameters based on neural networks, trained by simulated data sets generated by means of the Okada forward model. The developed work-flow implements a pre-processing step, aiming to reducing the data dimensionality, in order to improve the performance of the neural network inversion. The methodology has been validated by using experimental data sets obtained using different wavelength and representative of different kind of seismic source mechanisms.