Introduction

In Chapter  and I introduced the concept of tau tomography in steering filter preconditioning. I showed how, on a simple synthetic, using tau tomography preconditioned with steering filters provided superior velocity estimation compared to conventional methods. In Chapter  I applied the methodology on a 2-D line taken from a 3-D North Sea dataset. The velocity estimated by my tomography methodology proved superior to both the conventional layer- and grid-based approaches.

In this chapter, I apply the same methodology, tau tomography with steering filter preconditioning, to estimate a 3-D velocity function for the North Sea dataset introduced in Chapter . I begin by introducing the dataset and explaining the preprocessing. I then show the result of applying Common Azimuth Migration (CAM) () to the dataset using a layer-base velocity estimate. I pick several reflectors representing major structural boundaries. I perform semblance analysis using these reflectors. I calculate the dips along the picked reflectors and, using the methodology described in Appendix , I construct a 3-D steering filter. I invert for a new velocity using the 3-D tau tomography operator described in Appendix . I conclude by remigrating the dataset with the updated velocity model. The updated migration shows more reflector coherency and overall better focusing.