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Introduction

Imaging the subsurface in areas of poor illumination is an increasingly important goal. Limited survey sizes and lensing caused by large velocity contrasts result in unwanted amplitude variations and artifacts in our images. Although much recent work as been done to improve imaging in these areas with least-squares inversion techniques Kuehl and Sacchi (2001); Prucha et al. (2000, 2001); Prucha and Biondi (2002a,b), these methods are computationally quite expensive. A less expensive method to equalize amplitudes is simple weighting in the model space.

Model space weighting operators can be applied to an image after it has been produced by an imaging operator. One familiar operator used to help bring up amplitudes in seismic images is automatic gain control (AGC). However, AGC does not make use of the information we have about the subsurface and, therefore, the illumination. Since most imaging operators use a velocity model, why not use that velocity model to help design a smarter weighting operator that will do a better job of compensating for illumination? Rickett (2001) suggested using the velocity model and a downward continuation operator to approximate the Hessian, giving us an appropriate weighting operator.

In this paper, I will first review the method proposed by Rickett (2001) for the construction of a model space weighting operator. I will then apply this weighting operator to a complex synthetic model with clear illumination problems. Finally, I will compare the model space weighted results with the more expensive regularized inversion methods used by Prucha and Biondi (2002b).


next up previous print clean
Next: Methodology Up: M. Clapp: Illumination compensation: Previous: M. Clapp: Illumination compensation:
Stanford Exploration Project
7/8/2003