High‐density moveout parameter fields V and η. Part one: Simultaneous automatic picking

Siliqi, R.; Meur, D. Le; Gamar, Fatiha; Smith, Lianna J.D.; Touré, J.P.; Herrmann, P.

doi:10.1190/1.1817745

Cited by 27 publications

(6 citation statements)

References 6 publications

(6 reference statements)

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“…The problem of automatic picking of velocities from semblance scans has been considered by many authors (Adler and Brandwood, 1999;Siliqi et al, 2003;Arnaud et al, 2004). The approach taken in this paper is inspired by the suggestion of Harlan (2001) to look at velocity picking as a variational problem.…”

Section: Appendix B: Automatic Velocity Picking From Semblance Scansmentioning

confidence: 99%

Velocity analysis using AB semblance

Fomel

2009

Geophysical Prospecting

135

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I derive and analyze an explicit formula for a generalized semblance attribute, which is suitable for velocity analysis of prestack seismic gathers with distinct amplitude trends. While the conventional semblance can be interpreted as squared correlation with a constant, the AB semblance is defined as a correlation with a trend. This measure is particularly attractive for analyzing class II AVO anomalies and converted waves. Analytical derivations and numerical experiments show that the resolution of the AB semblance is approximately twice lower than that of the conventional semblance. However, this does not prevent it from being an effective attribute. I use synthetic and field data examples to demonstrate the improvements in velocity analysis from AB semblance.

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Section: Appendix B: Automatic Velocity Picking From Semblance Scansmentioning

confidence: 99%

Velocity analysis using AB semblance

Fomel

2009

Geophysical Prospecting

135

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“…Van der Baan and Kendall (2002) also invert the model in the τ-p domain and conclude that there exists a family of kinematically equivalent models that exhibit identical moveout curves. Siliqi et al (2003) obtain dense model parameters by simultaneously picking velocity and anellipticity. Abbad et al (2009) propose two-step automatic nonhyperbolic velocity analysis using a normalized bootstrapped differential semblance (BDS).…”

Section: Introductionmentioning

confidence: 99%

Horizon-based semiautomated nonhyperbolic velocity analysis

Zhang

Zhao

et al. 2014

GEOPHYSICS

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With higher capacity recording systems, long-offset surveys are becoming common in seismic exploration plays. Long offsets provide leverage against multiples, have greater sensitivity to anisotropy, and are key to accurate inversion for shear impedance and density. There are two main issues associated with preserving the data fidelity contained in the large offsets: (1) nonhyperbolic velocity analysis and (2) mitigating the migration/NMO stretch. Current nonhyperbolic velocity analysis workflows first estimate moveout velocity V nmo based on the offset-limited gathers, then pick an effective anellipticity η eff using the full-offset gathers. Unfortunately, estimating V nmo at small aperture may be inaccurate, with picking errors in V nmo introducing errors in the subsequent analysis of effective anellipticity. We have developed an automated algorithm to simultaneously estimate the nonhyperbolic parameters. Instead of directly seeking an effective stacking model, the algorithm finds an interval model that gives the most powerful stack. The searching procedure for the best interval model was conducted using a direct, global optimization algorithm called differential evolutionary. Next, we applied an antistretch workflow to minimize stretch at a far offset after obtaining the optimal effective model. The automated velocity analysis and antistretch workflow were tested on the data volume acquired over the Fort Worth Basin, USA. The results provided noticeable improvement on the prestack gathers and on the stacked data volume.

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“…Furthermore, picking parameters from a high‐dimensional semblance volume also poses a challenge (Adler and Brandwood, ; Siliqi et al . ; Arnaud et al . ; Tao et al .…”

Section: Introductionmentioning

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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High‐density moveout parameter fields V and η. Part one: Simultaneous automatic picking

Cited by 27 publications

References 6 publications

Velocity analysis using AB semblance

Velocity analysis using AB semblance

Horizon-based semiautomated nonhyperbolic velocity analysis

A fast algorithm for 3D azimuthally anisotropic velocity scan

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