An image-focusing semblance functional for velocity analysis

An image-focusing semblance functional for velocity analysis

Biondo Biondi

Abstract:

Analyzing the focusing and defocusing of migrated images provides valuable velocity information that can supplement the velocity information routinely extracted from migrated common-image gathers. However, whereas qualitative focusing analysis is readily performed on ensemble of images generated by prestack residual migration, quantitative focusing analysis remains a challenge. I use two simple synthetic-data examples to show that the maximization of a minimum-entropy norm, a commonly-used measure of image focusing, yields accurate estimates for diffracted events, but it can be misleading in the presence of continuous but curved reflectors.

I propose to measure image focusing by computing coherency across structural dips, in addition to coherency across aperture/azimuth angles. Images can be efficiently decomposed according to structural dips during residual migration. I introduce a semblance functional to measure image coherency simultaneously across the aperture/azimuth angles and the dip angles. Using 2D synthetic data examples, I show that the simultaneous evaluation of semblance across aperture-angles and dips can be effective in quantitatively measuring image focusing and also avoiding the biases induced by reflectors' curvature.

An image-focusing semblance functional for velocity analysis