Reflection FWI for both Reflectivity and Background Velocity

Abstract

The application of full-waveform inversion to pure reflection data in the absence of a highly accurate starting velocity model is difficult. We demonstrate a means of achieving this successfully by interleaving least-squares RTM with a version of FWI in which the tomographic gradient that is required to update the background macro-model is separated from the reflectivity gradient using the Born approximation during forward modelling. This provides a good update to the macro-model. This approach is then followed by conventional reflection FWI to obtain a final high-fidelity high-resolution result from a poor starting model using only reflection data.