A robust workflow for detecting azimuthal anisotropy

Davidson, Mark R.; Swan, Herbert W.; Sil, Samik; Howell, Jack; Olson, Robert; Zhou, Chengshang

doi:10.1190/1.3627732

Cited by 1 publication

(1 citation statement)

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“…Davidson et al . () proposed a stable way of detecting azimuthal anisotropy using orthogonal parameterization of the moveout function, which is based on an equivalent reformulation of equation :

t = \sqrt{τ^{2} + W_{avg} (x^{2} + y^{2}) + W_{prefixcos} (x^{2} - y^{2}) + 2 W_{prefixsin} x y} .

The cosine‐ and sine‐dependent slowness values

W_{cos}

and

W_{sin}

are usually much smaller than the averaged slowness

W_{avg}

. Therefore, a possible workflow for anisotropic velocity analysis and NMO correction can proceed in three steps.…”

Section: Theorymentioning

confidence: 99%

A fast algorithm for 3D azimuthally anisotropic velocity scan

Fomel

Ying

2014

Geophysical Prospecting

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The conventional velocity scan can be computationally expensive for large‐scale seismic data sets, particularly when the presence of anisotropy requires multiparameter scanning. We introduce a fast algorithm for 3D azimuthally anisotropic velocity scan by generalizing the previously proposed 2D butterfly algorithm for hyperbolic Radon transforms. To compute semblance in a two‐parameter residual moveout domain, the numerical complexity of our algorithm is roughly O(N3logN) as opposed to O(N5) of the straightforward velocity scan, with N being the representative of the number of points in a particular dimension of either data space or parameter space. Synthetic and field data examples demonstrate the superior efficiency of the proposed algorithm.

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