Summary and Future Work

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 $\gamma$ 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 $\gamma$ 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.