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Abstract

Summary

The problem of sparsely collected seismic data is one of the main issues in reflection seismology, because most advanced data processing techniques require a dense and regular seismic data grid. We present a geostatistical seismic data interpolation technique based on sequential stochastic simulations with local structural anisotropies. This technique, contrary to conventional existing data-driven seismic interpolation approaches based on sparsity, prediction filters, or rank-reduction, predicts the value of seismic amplitudes at non-sampled locations by exploiting the statistics of the recorded amplitudes, which are used as experimental data for the geostatistical interpolation in the original data domain. Local mean and variance are computed on-the-fly to define intervals of the global conditional distribution function, from where amplitude values are stochastically simulated. The parameters to define subsets of experimental data from which mean and variance are calculated are given by local variogram models, which in turn are obtained from a local dip and azimuth estimation in the t-x-y domain. The geostatistical seismic data interpolation technique is applied to synthetic and real 2D and 3D datasets in both postand pre-stack domains. Besides being computationally cheaper than other methods, because the interpolation is carried out directly in the original data domain, the proposed technique provides a local quantitative analysis of the reliability of the interpolated seismic samples, which can be exploited in following processing steps.

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/content/papers/10.3997/2214-4609.201902177
2019-09-02
2024-04-25

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References

  1. Azevedo, L. and Soares, A. [2017] Geostatistical methods for reservoir geophysics. Springer International Publishing, Advances in Oil and Gas Exploration & Production
     [Google Scholar]
  2. Bahorich, M. S. and FarmerS. L. [1995] 3-D seismic coherency for faults and stratigraphic features: The Leading Edge, 1053–1058
     [Google Scholar]
  3. Deutsch, C., and Journel, A.G. [1998] GSLIB: Geostatistical Software Library and Users' Guide. Oxford University Press.
     [Google Scholar]
  4. Dubrule, O. [2003] Geostatistics for seismic data integration in earth models. Tulsa, OK: SEG/EAGE Distinguished Instructor Short Course Number 6.
     [Google Scholar]
  5. Goovaerts, P., [1997] Geostatistics for natural resources evaluation. New York: Oxford University Press.
     [Google Scholar]
  6. Horta, A, Caeiro, M., Nunes, R., and Soares, A., [2009] Simulation of continuous variables at meander structures: application to contaminated sediments of a lagoon. AtkinsonP, LloydC (eds) geoENV VII—Geostatistics for environmental applications. Quantitative geology and geostatistics. Springer, the Netherlands, pp 161–172
     [Google Scholar]
  7. Jia, Y. and Ma, J. [2017] What can machine learning do for seismic data processing? An interpolation application: Geophysics, 82, no. 3 P. V163-V177 doi: 10.1190/GEO2016‑0300.1
     https://doi.org/10.1190/GEO2016-0300.1 [Google Scholar]
  8. Kreimer, N., StantonA., and Sacchi, M. D. [2013] Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction: Geophysics, 78, no. 6, V273-V284, doi: 10.1190/geo2013‑0022.1
     https://doi.org/10.1190/geo2013-0022.1 [Google Scholar]
  9. Naghizadeh, M and Sacchi, M. D. [2010], Hierarchical scale curvelet interpolation of aliased seismic data. 80th Annual International Meeting, SEG, Expanded Abstracts, 3656–3661.
     [Google Scholar]
  10. SoaresA. [1990] Geostatistical Estimation of Orebody Geometry: Morphological Kriging. Mathematical Geology, 22, N 7, 787–802.
     [Google Scholar]
  11. Soares, A. [2001] Direct Sequential Simulation and Co-simulation: Mathematical Geology, 33(8), 911–926.
     [Google Scholar]
  12. SpitzS. [1991] Seismic trace interpolation in the F-X domain. Geophysics, 56, 785–794, doi: 10.1190/1.1443096.
     https://doi.org/10.1190/1.1443096. [Google Scholar]
  13. Xu, W., [1996] Conditional curvilinear stochastic simulation using pixel- based algorithms: Mathematical Geology, v. 28, no. 7, p. 937–949.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201902177
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Geostatistical Interpolation of Non-stationary Seismic Data
Conference Proceedings 2019, 1 (2019); https://doi.org/10.3997/2214-4609.201902177
/content/papers/10.3997/2214-4609.201902177
/content/papers/10.3997/2214-4609.201902177
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