Resolving Small Scale Lateral Velocity Anomalies without FWI or Stochastic Approaches

Small-scale heterogeneities create distortions in the wavefield that are usually resolved by Full Waveform Inversion (FWI). When FWI fails (for lack of diving waves or low frequencies), recent works promote the use of targeted stochastic approaches maximizing the stack power. We instead revisit here ray-based tomography and propose an innovative approach for a high definition tomography which can recover small-scale anomalies without any a priori information on their localization. While previously proposed tomographic approaches aimed at vertical resolution and structural conformity, our new approach focuses on enhancing lateral heterogeneities. The first key component is an accurate RMO picking with structural dip information honoring the variation of dip with offset. Second, is an efficient nonlinear slope tomography employing small grids and a minimum level of constraints. Our approach is demonstrated on a 2D synthetic dataset with small-scale lateral variations and confirmed on a 3D streamer dataset from the Barents Sea.