Approximate Least Squares RTM via matching filters and regularized inversion

Least-squares Reverse Time Migration (LS-RTM) provides amplitude recovery, high resolution and artifacts attenuation, at the cost of one migration/demigration pair for each iteration. Hence, the so called single-iteration approaches represent a cost-effective approximation of LS-RTM. We propose a single-iteration LS-RTM, where the approximate Hessian operator is estimated as a matching filter. Based on the consideration that subsurface reflectivity is sparse, the estimated filter is given as input to a sparse inversion. The proposed method is then illustrated through synthetic and field data examples