Enhanced FWI Cycle-Skipping Mitigation by Constrained Correlation-Based Traveltime Inversion

Summary A traveltime based FWI algorithm can estimate a kinematically accurate macro model in presence of cycle-skipped data. Such method aims at minimizing the traveltime misfit between the observed and modelled data, in a way that also maximizes their temporal cross-correlation function. Obtaining a reliable traveltime misfit estimate is paramount to the success of traveltime based FWI algorithms in the presence of cycle-skipped data. We propose a constrained correlation-based traveltime inversion to improve the quality of the traveltime misfit estimate. We use synthetic and field data examples to validate our method, which show evidence of cycle skipping mitigation and demonstrate the retrieval of kinematically accurate macro models suitable for subsequent iterations of high-resolution L2-norm FWI.