Least-squares diffraction imaging using shaping regularization by anisotropic smoothing

Published as Geophysics, v. 85, S313-S325, (2020)

Least-squares diffraction imaging using shaping regularization by anisotropic smoothing

Dmitrii Merzlikin, Sergey Fomel, and Xinming Wu
Formerly Bureau of Economic Geology
John A. and Katherine G. Jackson School of Geosciences
The University of Texas at Austin
University Station, Box X
Austin, Texas 78713-8924, USA;
presently WesternGeco
Schlumberger
3750 Briarpark Drive
Houston, Texas 77042, USA
E-mail: dmitrii.merzlikin@utexas.edu (corresponding author) Bureau of Economic Geology
John A. and Katherine G. Jackson School of Geosciences
The University of Texas at Austin
University Station, Box X
Austin, Texas 78713-8924, USA
Formerly Bureau of Economic Geology
John A. and Katherine G. Jackson School of Geosciences
The University of Texas at Austin
University Station, Box X
Austin, Texas 78713-8924, USA;
presently School of Earth and Space Sciences
University of Science and Technology of China
Hefei, China

Abstract:

We use least-squares migration to emphasize edge diffractions. The inverted forward modeling operator is the chain of three operators: Kirchhoff modeling, azimuthal plane-wave destruction and path-summation integral filter. Azimuthal plane-wave destruction removes reflected energy without damaging edge diffraction signatures. Path-summation integral guides the inversion towards probable diffraction locations. We combine sparsity constraints and anisotropic smoothing in the form of shaping regularization to highlight edge diffractions. Anisotropic smoothing enforces continuity along edges. Sparsity constraints emphasize diffractions perpendicular to edges and have a denoising effect. Synthetic and field data examples illustrate the effectiveness of the proposed approach in denoising and highlighting edge diffractions, such as channel edges and faults.

Least-squares diffraction imaging using shaping regularization by anisotropic smoothing