Improving reflection FWI reflectivity using LSRTM in curvelet-domain

We investigate the benefits of considering the effects of the Hessian matrix in the reflection FWI (RFWI) reflectivity, i.e., using least-squares RTM (LSRTM) to estimate the short-wavelength component of the model. In addition, a more efficient approach using single-iteration LSRTM, more specifically, using curvelet-domain Hessian filters is proposed. Using synthetic and field data sets, we show how this approach can improve the reflectivity model and, therefore, the synthetic reflection data, which ultimately benefits RFWI. Finally, we discuss some of the limitations of this approach and some of the challenges that are still not addressed by it.