Solutions for Deep Water Imaging

Imaging in deep water environments poses a specific set of challenges, both in the data preconditioning and the imaging.

A prerequisite for any successful imaging project is the estimation of an accurate velocity field for the subsurface. For large scale industrial projects, this invariably implies the use of automatic
pickers to assist in the velocity model building. A corollary of this assertion, is that the data going
into the migration (and hence the autopicker) is free of noise and multiples, so that the autopicker can make reliable picks.

Consequently, best practise in imaging is inexorably linked to optimal pre-processing (the ‘image-driven’ concept).

In this paper, we will review several examples in current practise for addressing many of these
issues involved in optimal imaging, concentrating our attention on multiple suppression, scattered noise attenuation, iterative velocity model building and depth imaging.