1887

Abstract

Summary

The common-reflection surface (CRS) method is a sophisticated alternative to the traditional common-midpoint (CMP) stacking, as its traveltime approximation is more accurate than the normal moveout. This in turn requires more parameters for the moveout description, implying in the increase of the computational burden. In fact, the great challenge to make this method widely used in seismic data analysis has being the trade-off between the accuracy of the estimation of the traveltime parameters and the corresponding computational complexity. To cope with this problem, in this work we estimate the CRS parameters using the Differential Evolution (DE) global optimization algorithm. As we aim at critical data sets, from which no reliable prior information can be easily extracted, we propose to apply this algorithm in a fully automatic global search without any guide. The results for a 2D real data set from Brazil indicates that the global strategy yields good results, both in terms of image quality as in the quality of the parameters, especially the stacking velocity estimates, while keeping the computational costs relatively low.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.20140991
2014-06-16
2020-05-31
Loading full text...

Full text loading...

References

  1. P.Hubral, G.Höcht and R.Jäger
    . (1998). An introduction to the common reflection surface stack. 60th EAGE Conference & Exhibition.
    [Google Scholar]
  2. W. H.Mayne
    . (1962). Common reflection point horizontal data stacking techniques. Geophysics. 27:927–938.
    [Google Scholar]
  3. T.Herteweck, J.Schleicher and J.Mann
    . (2007). Data stacking beyond CMP. The Leading Edge.70(7):818–827.
    [Google Scholar]
  4. E.Duveneck
    . (2007). 3D Tomographic velocity model estimation with kinematic wavefield attributes. Geophysical Prospecting. 52(6):535–545.
    [Google Scholar]
  5. N.Neidell and M.Taner
    . (1971). Semblance and other coherency measures for multichannel data. Geophysics. 36(3):482–497.
    [Google Scholar]
  6. R.Jäger, J.Mann, G.Höcht and P.Hubral
    . (2001). Common-reflection-surface stack: image and attributes. Geophysics. 66:97–109.
    [Google Scholar]
  7. G.Garabito, P.Stoffa, L.Lucena and J.C. R.Cruz
    . (2012). Part I-CRS stack: Global optimization of the 2D CRS-attributes. Journal of Applied Geophysics. 85(0):92–101.
    [Google Scholar]
  8. L.M.K.Carmo and G.Garabito
    . (2003). Métodos de otimização global aplicados na busca dos parâmetros SRC-2D. SBGf 8th International Congress of the Brazilian Geophysical Society.
    [Google Scholar]
  9. M.Taner and F.Koehler
    . (1969). Velocity spectra digital computer derivation and applications of velocity functions. Geophysics. 34(6):859–881.
    [Google Scholar]
  10. R.Storn and K.Price
    . (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization. 11(4):341–359.
    [Google Scholar]
  11. T.Barros, R.Ferrari, R.Krummenauer, R.Lopes and M.Tygel
    . (2013). The Impact of the Parameter Estimation Strategy in the CRS Method. SBGf 13th International Congress of the Brazilian Geophysical Society.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20140991
Loading
/content/papers/10.3997/2214-4609.20140991
Loading

Data & Media loading...

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