Near-surface fault detection by migrating back-scattered surface waves with and without velocity profiles

Abstract

We demonstrate that diffraction stack migration can be used to discover the distribution of near-surface faults. The methodology is based on the assumption that near-surface faults generate detectable back-scattered surface waves from impinging surface waves. We first isolate the back-scattered surface waves by muting or FK filtering, and then migrate them by diffraction migration using the surface wave velocity as the migration velocity. Instead of summing events along trial quasi-hyperbolas, surface wave migration sums events along trial quasi-linear trajectories that correspond to the moveout of back-scattered surface waves. We have also proposed a natural migration method that utilizes the intrinsic traveltime property of the direct and the back-scattered waves at faults. For the synthetic data sets and the land data collected in Aqaba, where surface wave velocity has unexpected perturbations, we migrate the back-scattered surface waves with both predicted velocity profiles and natural Green's function without velocity information. Because the latter approach avoids the need for an accurate velocity model in event summation, both the prestack and stacked migration images show competitive quality. Results with both synthetic data and field records validate the feasibility of this method. We believe applying this method to global or passive seismic data can open new opportunities in unveiling tectonic features.