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# Model versus data normalization

Illumination problems caused by finite-recording aperture and lateral
velocity lensing can lead to amplitude fluctuations in migrated
images.
I calculate both model and data-space weighting functions that
compensate for these illumination problems in finite-frequency depth
migration results.
These weighting functions can either be applied directly with
migration to mitigate the effects of poor subsurface
illumination, or used as preconditioning operators in iterative
least-squares (*L*2) migrations.
Computational shortcuts allow the weighting functions to be computed
at about the cost of a single migration.
Results indicate that model-space normalization can significantly
reduce amplitude fluctuations due to illumination
problems.
However, for the examples presented here, data-space normalization
proved susceptible to coherent noise contamination.

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Stanford Exploration Project

5/27/2001