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Volume 67, Issue 5
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

We present a method for fast estimation of finite offset common reflection surface parameters. Firstly, the derivatives with respect to offset are derived from the velocity guide. Secondly, we apply structure tensors to extract the derivatives with respect to midpoint from stacked common offset sections. Finally, the mixed derivative is estimated using a one‐parametric semblance search. The proposed method is compared to the global five‐parametric semblance search and the pragmatic sequential two‐parametric semblance search on one synthetic and one real data set. The experiments show that the proposed method is more robust against noise than the pragmatic search and have comparable robustness with the global search. The proposed method smoothes parameter estimates in a local window, and the window size is set to give the best trade‐off between detail and robustness. Since the proposed method is dependent on a velocity guide, the quality of the other parameter estimates may be influenced by any inaccuracies in the guide. The main advantage of the proposed method is the computational efficiency. When compared with a gridded implementation of the semblance search, the proposed method is 10 and 400 times faster than the pragmatic and global search. Alternative search strategies significantly reduce the computational cost of the global search. However, since more than 99% of the computational cost of the proposed method comes from the semblance search to estimate the mixed derivative, it is expected that such techniques also reduce the computational cost for the proposed method.

© 2019 European Association of Geoscientists & Engineers © 2019 European Association of Geoscientists & Engineers
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/content/journals/10.1111/1365-2478.12740
2019-03-05
2024-04-24

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References

  1. AsgedomE.G., GeliusL.J. and TygelM.2013. 2D common‐offset traveltime based diffraction enhancement and imaging. Geophysical Prospecting61, 1178–1193.
     [Google Scholar]
  2. BakkerP.2002. Image structure analysis for seismic interpretation. Phd thesis, Technische Universiteit Delft, The Netherlands.
     [Google Scholar]
  3. BarrosT., FerrariR., KrummenauerR. and LopesR.2015. Differential evolution‐based optimization procedure for automatic estimation of the common‐reflection surface traveltime parameters. Geophysics80, WD189–WD200.
     [Google Scholar]
  4. BigunJ. and GranlundG.H.1987. Optimal orientation detection of linear symmetry. Proceedings of the IEEE First International Conference on Computer Vision54, 433–438.
     [Google Scholar]
  5. CoimbraT.A., FaccipieriJ.H., GeliusL.‐J. and TygelM.2015. Enhancement of stacked sections using ZO CRS parameters. 14th International Congress of the Brazilian Geophysical Society and EXPOGEF, Rio de Janeiro, Brazil, pp. 1251–1255.
     [Google Scholar]
  6. FaccipieriJ.H.2016. Método CRS interativo com controle semiautomático de aberturas. PhD thesis, Universidade Estadual de Campinas, São Paulo, Brazil.
     [Google Scholar]
  7. FaccipieriJ.H., CoimbraT.A., GeliusL.‐J. and TygelM.2016. Stacking apertures and estimation strategies for reflection and diffraction enhancement. Geophysics81, V271–V282.
     [Google Scholar]
  8. GarabitoG., CruzJ.C.R. and SöllnerW.2017. Finite‐offset common reflection surface stack using global optimisation for parameter estimation: a land data example. Geophysical Prospecting65, 1123–1137.
     [Google Scholar]
  9. GarabitoG., StoffaP., and SöllnerW.2013. Global optimization of the common‐offset CRS‐attributes: synthetic and field data application. 13th International Congress of the Brazilian Geophysical Society and EXPOGEF, Rio de Janeiro, Brazil, pp. 1565–1568.
     [Google Scholar]
  10. GrechkaV. and TsvankinI.1998. 3‐D description of normal moveout in anisotropic inhomogeneous media. Geophysics, 1079–1092.
     [Google Scholar]
  11. Höcht, G., BazelaireE.D., MajerP. and HubralP.1999. Seismics and optics: hyperbolae and curvatures. Geophysical Prospecting42, 261–281.
     [Google Scholar]
  12. Höcht, G., RicarteP., BerglerS. and LandaE.2009. Operator‐oriented CRS interpolation. Geophysical Prospecting57, 957–979.
     [Google Scholar]
  13. HubralP.1962. Computing true amplitude reflections in a laterally inhomogeneous earth. Geophysics48, 1051–1062.
     [Google Scholar]
  14. MannJ., JägerR., MüllerT., HöchtG. and HubralP.1999. Common‐reflection‐surface stack: a real data example. Journal of Applied Geophysics42, 301–318.
     [Google Scholar]
  15. MayneW.H.1962. Common reflection point horizontal data stacking techniques. Geophysics27, 927–938.
     [Google Scholar]
  16. Minato, S., TsujiT., MatsuokaT., NishizakaN. and IkedaM.2012. Global optimisation by simulated annealing for common reflection surface stacking and its application to low‐fold marine data in southwest Japan. Exploration Geophysics43, 59–69.
     [Google Scholar]
  17. MüllerN.‐A.2003. The 3D common‐reflection‐surface stack theory and application. PhD thesis, Universität Karlsruhe, Germany.
     [Google Scholar]
  18. NeidellN.S. and TanerM.T.1971. Semblance and other coherency measures for multichannel data. Geophysics36, 482–497.
     [Google Scholar]
  19. SantosL., SchleicherJ., CostaJ. and NovaisA.2011. Fast estimation of common‐reflection‐surface parameters using local slopes. Geophysics76, U23–U34.
     [Google Scholar]
  20. SchleicherJ., TygelM. and HubralP.1993. Parabolic and hyperbolic paraxial two‐point traveltimes in 3D media. Geophysical Prospecting41(4), 495–513.
     [Google Scholar]
  21. van de WeijerJ., van VlietL.J., VerbeekP.W. and van GinkelM.2001. Curvature estimation in oriented patterns using curvilinear models applied to gradient vector fields. IEEE Transactions on Pattern Analysis and Machine Intelligence23, 1035–1042.
     [Google Scholar]
  22. WaldelandA.U., ZhaoH., FaccipieriJ.H., SolbergA.H.S. and GeliusL.J.2017. Fast and robust Common Reflection Surface (CRS) parameter estimation. Geophysics83, O1–O13.
     [Google Scholar]
  23. ZhangY., BerglerS. and HubralP.2001. Common reflection surface (CRS) stack for common offset. Geophysical Prospecting49, 709–718.
     [Google Scholar]
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Fast estimation of prestack common reflection surface parameters
Geophysical Prospecting 67, 1163 (2019); https://doi.org/10.1111/1365-2478.12740
/content/journals/10.1111/1365-2478.12740
/content/journals/10.1111/1365-2478.12740
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  • Article Type: Research Article
Keyword(s): CRS imaging; Moveout velocity; parameter estimation; Signal processing; Stacking

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