Prestack migration deconvolution

Jianhua Yu

doi:10.1190/1.2187783

Return

Prestack migration deconvolution

2006-03-01

PRE

DOI：10.1190/1.2187783

Original

Original request for help

Save

Abstract

En 中文

Prestack migration in the time and depth domains is the premier tool for seismic imaging of complex structures. Unfortunately, an undersampled acquisition geometry, a limited recording aperture, and strong velocity contrasts lead to uneven illumination of the subsurface. This results in blurring the migrated image, sometimes referred to as acquisition footprint noise in the migrated image. To remedy this blurring partially, we introduce prestack migration deconvolution (MD). The MD filter is a layered-medium approximation to the inverse Hessian matrix that is applied locally to the migrated section. Both synthetic- and field-data results show noticeable improvements in reducing rnigration artifacts and increasing lateral spatial resolution by more than 10%. The computational expense for constructing the MD filter is related to the MD operator length. Its cost is about the same as that for migration, but opportunities exist for significantly reducing this cost. Results suggest that MD should be applied to migrated sections to optimize image quality.

Keywords:

INVERSION

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.