Least-squares migration — Data domain versus image domain using point spread functions

Abstract

Conventional amplitude inversion assumes that the migrated image preserves relative-amplitude information. However, illumination effects caused by complex geologic settings, undersampled acquisition geometry, and limited recording aperture pose a challenge to even the most advanced imaging algorithms. In addition, standard depth-migration images can suffer from lack of resolution caused by wavelet stretch, attenuation, and suboptimal deghosting. Least-squares migration (LSM) can mitigate many of these problems and produce better resolved migration images suitable for AVO inversion. However, whether formulated in the data domain or the image domain, LSM is an inversion algorithm and is sensitive to inaccuracies in the source wavelet, velocity model, data preprocessing, and the propagator used. Practical considerations to mitigate these problems under nonideal conditions and cost-reduction strategies differ between the data- and image-domain formulations. The relative merits of each approach are evaluated by using example inversions for complex synthetic models, including free-surface ghost and attenuation effects. When a data-domain implementation of LSM is considered necessary, the image-domain implementation should be considered at the same time, especially when targeting localized reservoir targets under complex overburdens. Application of image-domain least-squares migration on a Gulf of Mexico field data set produces significant improvements in resolution and event continuity in the subsalt target region.