Robust least squares RTM on the 3D Deimos ocean bottom node dataset

We apply least-squares reverse-time migration (LSRTM) to a three-dimensional ocean-bottom node dataset. When applying to field datasets, a simple data-fitting objective function in LSRTM is insufficient. The Deimos field has complex salt structure that obscures part of the image deep down. We use a target-oriented data-reweighting scheme to emphasize deeper parts of the image near the salt. To make the algorithm more robust, we utilize the subsurface-angle domain to filter some of the noise in the image space. With the above tools to improve the robustness of LSRTM, we found that LSRTM provide a better image than conventional RTM method with better relative amplitude balance, higher resolution, and improved illumination near the shadow zone. We also found that the image calculated using joing-LSRTM of primary and mirror signals is superior than conventional single-mode imaging.