Paper Summary
Iterative least-squares migration is an expensive process that requires several passes of migration and demigration. In this paper we focus on how an optimized initial reflectivity model combined with a deconvolution imaging condition can ensure faster convergence. We also incorporate the visco-acoustic effects in the de-blurring process to improve image corrections across and below Q-anomalies. This workflow is demonstrated on a dualazimuth North Sea dataset, where the aim is to improve the understanding of the Frosk and Boyla fields, which are characterized by complex and steeply dipping sand systems and areas of weak reflectivity.