1887
Volume 67, Issue 3
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

The objective of moveout parameter inversion is to derive sets of parameter models that can be used for moveout correction and stacking at each common midpoint location to increase the signal‐to‐noise ratio of the data and to provide insights into the kinematic characteristics of the data amongst other things. In this paper, we introduce a data‐driven user‐constrained optimization scheme that utilizes manual picks at a point on each reflector within a common midpoint gather to constrain the search space in which an optimization procedure can search for the optimal parameter sets at each reflection. The picks are used to create boundary curves which can be derived approximately via an optimization technique or analytically via the derivation of an analytical bounds function. In this paper, we derive analytical forms of bounds functions for four different moveout cases. These are normal moveout, non‐hyperbolic moveout, azimuthally dependent normal moveout and azimuthally dependent non‐hyperbolic moveout. The optimization procedure utilized here to search for the optimal moveout parameters is the particle swarm optimization technique. However, any metaheuristic optimization procedure could be modified to account for the constraints introduced in this paper. The technique is tested on two‐layer synthetic models based on three of the four moveout cases discussed in this paper. It is also applied to an elastic forward modelled synthetic model called the HESS model, and finally to real 2D land data from Alaska. The resultant stacks show a marked improvement in the signal‐to‐noise ratio compared to the raw stacks. The results for the normal moveout, non‐hyperbolic moveout and azimuthally dependent normal moveout tests suggest that the method is viable for said models. Results demonstrate that our method offers potential as an alternative to conventional parameter picking and inversion schemes, particularly for some cases where the number of parameters in the moveout approximation is 2 or greater.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12758
2019-03-13
2024-03-28
Loading full text...

Full text loading...

References

  1. AbbadB., UrsinB. and RappinD.2009. Automatic nonhyperbolic velocity analysis. Geophysics74, U1–U12.
    [Google Scholar]
  2. AlDajaniA. and TsvankinI.1998. Nonhyperbolic reflection moveout for horizontal transverse isotropy. Geophysics63, 1738–1753.
    [Google Scholar]
  3. AlDajaniA.F., TsvankinI. and ToksozN.1998. Nonhyperbolic reflection moveout for azimuthally anisotropic media. SEG Technical Program, Expanded Abstracts, pp. 1479–1482. Society of Exploration Geophysicist.
  4. AlkhalifahT.2000. The offset‐midpoint traveltime pyramid in transversely isotropic media. Geophysics65, 1316–1325.
    [Google Scholar]
  5. AlkhalifahT. and TsvankinI.1995. Velocity analysis for transversely isotropic media. Geophysics60, 1550–1566.
    [Google Scholar]
  6. AlmarzougA.M. and AhmedF.Y.2012. Automatic seismic velocity picking. SEG Technical Program, Expanded Abstracts, pp. 1–5. Society of Exploration Geophysicists.
  7. BliasE.2009. Long‐offset NMO approximations for a layered VTI model: model study. SEG Technical Program, Expanded Abstracts, pp. 3745–3749. Society of Exploration Geophysicists.
  8. BurnettW. and FomelS.2008. 3D velocity‐independent elliptically anisotropic moveout correction. SEG Technical Program, Expanded Abstracts, pp. 2952–2956. Society of Exploration Geophysicists.
  9. BurnettW. and FomelS.2009. Moveout analysis by time‐warping. SEG Technical Program, Expanded Abstracts, pp. 3710–3714. Society of Exploration Geophysicists.
  10. CarneiroT.C., MeloS.P., CarvalhoP.C. and de S. BragaA.P.2016. Particle swarm optimization method for estimation of Weibull parameters: a case study for the Brazilian northeast region. Renewable Energy86, 751–759.
    [Google Scholar]
  11. ChenY., LiuT. and ChenX.2015. Velocity analysis using similarity‐weighted semblance. Geophysics80, A75–A82.
    [Google Scholar]
  12. ClaerboutJ.F.1992. Earth Soundings Analysis: Processing Versus Inversion. Blackwell Scientific Publications.
    [Google Scholar]
  13. FarraV., PenkI. and JlekP.2016. Weak‐anisotropy moveout approximations for P‐waves in homogeneous layers of monoclinic or higher anisotropy symmetries. Geophysics81, C17–C37.
    [Google Scholar]
  14. FomelS.2009. Velocity analysis using AB semblance. Geophysical Prospecting57, 311–321.
    [Google Scholar]
  15. FomelS. and StovasA.2010. Generalized nonhyperbolic moveout approximation. Geophysics75, U9–U18.
    [Google Scholar]
  16. FortiniC., MaggiD., LipariV. and FerlaM.2013. Particle swarm optimization for seismic velocity analysis. SEG Technical Program, Expanded Abstracts, pp. 4864–4868. Society of Exploration Geophysicists.
  17. GadeS. and HerlufsenH.1987. Windows to FFT analysis (part 1): technical report. Brüel and Kjær, Copenhagen.
  18. GrechkaV. and TsvankinI.1998. 3‐D description of normal moveout in anisotropic inhomogeneous media. Geophysics63, 1079–1092.
    [Google Scholar]
  19. GrechkaV. and TsvankinI.1999. 3‐D moveout inversion in azimuthally anisotropic media with lateral velocity variation: theory and a case study. Geophysics64, 1202–1218.
    [Google Scholar]
  20. HuangK.‐Y. and YangJ.‐R.2015. Seismic velocity picking using hopfield neural network. SEG Technical Program, Expanded Abstracts, pp. 5317–5321. Society of Exploration Geophysicists.
  21. JennerE., WilliamsM. and DavisT.2005. A new method for azimuthal velocity analysis and application to a 3D survey, Weyburn field, Saskatchewan, Canada. SEG Technical Program, Expanded Abstracts, pp. 102–105. Society of Exploration Geophysicists.
  22. KennedyJ. and EberhartR.C.1995. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948. IEEE Service Center, Piscataway, NJ.
    [Google Scholar]
  23. KorenZ. and RavveI.2017. Fourth‐order normal moveout velocity in elastic layered orthorhombic media part 2: offset‐azimuth domain. Geophysics82, C113–C132.
    [Google Scholar]
  24. NeidellN.S. and TanerM.T. 1971. Semblance and other coherency measures for multichannel data. Geophysics36, 482–497.
    [Google Scholar]
  25. PatroS.G.K. and SahuK.K.2015. Normalization: a preprocessing stage . CoRR abs/1503.06462.
  26. PechA. and TsvankinI.2004. Quartic moveout coefficient for a dipping azimuthally anisotropic layer. Geophysics69, 699–707.
    [Google Scholar]
  27. PrataD.M., SchwaabM., LimaE.L. and PintoJ.C.2009. Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: application for an industrial polypropylene reactor. Chemical Engineering Science64, 3953–3967.
    [Google Scholar]
  28. RavveI. and KorenZ.2017a. Fourth‐order normal moveout velocity in elastic layered orthorhombic media part 1: slowness‐azimuth domain. Geophysics82, C91–C111.
    [Google Scholar]
  29. RavveI. and KorenZ.2017b. Traveltime approximation in vertical transversely isotropic layered media. Geophysical Prospecting65, 1559–1581.
    [Google Scholar]
  30. SedekM., GrossL. and TysonS.2017. Automatic NMO correction and full common depth point NMO velocity field estimation in anisotropic media. Pure and Applied Geophysics174, 305–325.
    [Google Scholar]
  31. SheriffR.2002. Encyclopedic Dictionary of Applied Geophysics, 4th edn. Society of Exploration Geophysicists.
    [Google Scholar]
  32. SörensenK. and GloverF.W.2013. Metaheuristics. In: Encyclopedia of Operations Research and Management Science, pp. 960–970. Springer.
    [Google Scholar]
  33. TarantolaA.1984. Inversion of seismic reflection data in the acoustic approximation. Geophysics49, 1259–1266.
    [Google Scholar]
  34. ThomsenL.1986. Weak elastic anisotropy. Geophysics51, 1954–1966.
    [Google Scholar]
  35. TsvankinI.1997. Anisotropic parameters and P‐wave velocity for orthorhombic media. Geophysics62, 1292–1309.
    [Google Scholar]
  36. TsvankinI. and GrechkaV.2011. Seismology of Azimuthally Anisotropic Media and Seismic Fracture Characterization. Society of Exploration Geophysicists.
    [Google Scholar]
  37. TsvankinI. and ThomsenL.1994. Nonhyperbolic reflection moveout in anisotropic media. Geophysics59, 1290–1304.
    [Google Scholar]
  38. VasconcelosI. and TsvankinI.2006. Non‐hyperbolic moveout inversion of wide‐azimuth P‐wave data for orthorhombic media. Geophysical Prospecting54, 535–552.
    [Google Scholar]
  39. WangJ., ZhangR., YanY., DongX. and LiJ.M.2017. Locating hazardous gas leaks in the atmosphere via modified genetic, MCMC and particle swarm optimization algorithms. Atmospheric Environment157, 27–37.
    [Google Scholar]
  40. WilsonH. and GrossL.2016. Hybridised weighted boot‐strap differential semblance. 25th International Geophysical Conference and Exhibition, ASEG Extended Abstracts, pp. 557–563. Australian Society of Exploration Geophysicists.
  41. WilsonH. and GrossL.2017. Amplitude variation with offset‐friendly bootstrapped differential semblance. Geophysics82, V297–V309.
    [Google Scholar]
  42. XuX., TsvankinI. and PechA.2005. Geometrical spreading of P‐waves in horizontally layered, azimuthally anisotropic media. Geophysics70, D43–D53.
    [Google Scholar]
  43. YangX.‐S. and HeX.2016. Nature‐inspired optimization algorithms in engineering: overview and applications. In: Nature‐Inspired Computation in Engineering, pp. 1–20. Springer International Publishing.
    [Google Scholar]
  44. YilmazÖ.2001. Seismic Data Analysis, 2nd edn. Society of Exploration Geophysicists.
    [Google Scholar]
  45. ZhangF. and UrenN.2005. Approximate explicit ray velocity functions and travel times for P‐waves in TI media. SEG Technical Program, Expanded Abstracts, pp. 106–109. Society of Exploration Geophysicists.
  46. ZhangW., JinY., LiX. and ZhangX.2011. A simple way for parameter selection of standard particle swarm optimization. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 436–443. Springer, Berlin, Heidelberg.
    [Google Scholar]
  47. ZielinskiK. and LaurR.2007. Stopping criteria for a constrained single‐objective particle swarm optimization algorithm. Informatica31, 51–59.
    [Google Scholar]
  48. ZielinskiK., WeitkemperP., LaurR. and KammeyerK.‐D., K. ZielinskiR. L.2006. Examination of stopping criteria for differential evolution based on a power allocation problem. Proceedings of the 10th International Conference on Optimization of Electrical and Electronic Equipment, Braşov, Romania, pp. 149–156. Springer.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12758
Loading
/content/journals/10.1111/1365-2478.12758
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Anisotropy; CMP; Moveout; Optimization; Parameter estimation; Traveltimes; Velocity analysis

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error