Introduction

Multicomponent ocean bottom cable (OBC) technology reestablishes the use and importance of converted wave (PS) data, yet opens the door for a series of new and existing problems with PS data. Irregular acquisition geometries are a serious impediment for accurate subsurface imaging. Irregularly sampled data affects the image with amplitude artifacts and phase distortions. Irregular geometry problems are more evident in cases in which the amplitude information is one of the main goals of study. For PS data, this problem is crucial since most of the PS processing focuses on the estimation of rock properties from seismic amplitudes.

The application of inverse theory satisfactorily regularizes acquisition geometries of 3D prestack seismic data (, , , , , , , ). For PP data, there are two distinct approaches to apply: 1) data regularization before migration and 2) irregular geometries correction during migration. Biondi and Vlad combine the advantages of the previous two approaches. Their methodology regularizes the data geometry before migration, filling in the acquisition gaps with a partial migration operator. The operator exploits the intrinsic correlation between prestack seismic traces. The partial migration operator used is Azimuth Moveout.

The recent development of a converted wave Azimuth Moveout (PSAMO) operator () that preserves amplitudes and is fast, enables the extension of Biondi and Vlad's methodology for converted waves data. Therefore, a complete and accurate geometry regularization is now possible for OBC seismic data.

This paper extends already existing methodologies for PP regularization in order to handle PS data. Due to the asymmetry of ray trajectories in PS data, there are more elements to consider in order to solve for irregular geometry problems. Our method for PS data regularization uses a PS Azimuth Moveout operator (PS-AMO) () in order to preserve the resolution of dipping events and correct for the lateral shift of the common conversion point.

Our methodology depends on the ratio between the P and the S velocities ($\gamma$). It also depends on the continuity of the events in the common midpoint gathers. These situations make our regularization an iterative procedure that stops where the difference between the previous and the actual $\gamma$ sections is relatively small.

We will present a summary of Biondi and Vlad's methodology for solving the irregular geometry problem using a preconditioned-regularized least-squares scheme. We present and discuss how this method can be extended to handle PS data and implement this method on a portion of a real 3D OBC data set.