We used least-squares inverse theory with the AMO operator as the regularization term. This method satisfactorily solved for interpolation of a 3D irregular data set.
We implemented a similar approach for regularizing the PS section of the OBC data set. For this problem, an iterative procedure is needed due to the dependence of the AMO operator on the value.
In order to obtain better results in the future, we recommend the use of a higher NMO approximation to obtain coherence among the traces to be stacked on the PS section. Additionally, formulating the estimation problem in a least-squares sense should allow a better constraint for its calculation, creating better PS regularized sections. This is an ongoing project.