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Illumination compensation: Model space weighting vs. regularized inversion

Marie L. Clapp

marie@sep.Stanford.edu

ABSTRACT

In areas of complex geology, finite surveys and large velocity contrasts result in images full of artifacts and amplitude variations due to illumination problems. Cheap methods such as model space weighting and expensive methods such as regularized least-squares inversion are among the schemes that have been developed to deal with these issues. Model space weighting operators can be obtained by applying a forward modeling and an adjoint migration operator to a user-specified reference model, then applied a posteriori to an image. Regularized least-squares inversion applied in an iterative scheme requires the selection of an imaging operator and a regularization operator that will compensate for the illumination problems during the processing itself. Applying each of these methods to the Sigsbee2A dataset, a complex synthetic, shows that model space weighting a posteriori can help to equalize amplitudes, but will strengthen artifacts within the image. Regularized least-squares inversion will equalize amplitudes and reduce artifacts, but can be quite expensive.



 
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Stanford Exploration Project
7/8/2003