1887

Abstract

Summary

Conventional deconvolution methods based on Robinson’s convolutional model have been playing an important role in improving the temporal resolution of seismic data for years. However, the application of these methods to real data is not always desirable due to some assumptions on the seismic wavelet and the seismogram, especially the noise-free assumption. In order to address the shortcomings of conventional deconvolution methods, the noise-free assumptio, we develop a multi-trace statistical broadband wavelet deconvolution based on the surface-consistent deconvolution method. In our proposed method, we maintained the standard assumptions that the source wavelet is minimum phase and the reflectivity is statistically white. However, we extended the Robinson’s convolutional model to include the noise component and use a Ricker-like wavelet which is the integral of the Ricker wavelet to be the desired output wavelet. Synthetic and real data examples are provided to show the effectiveness of the proposed deconvolution method.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201701086
2017-06-12
2020-01-19
Loading full text...

Full text loading...

References

  1. Gary, F. M, Michael, P. L, and DavidC. H.
    [2011] Gabor deconvolution: Estimating reflectivity by nonstationary deconvolution of seismic data. Geophysics, 76(3), P.W15–W30.
    [Google Scholar]
  2. Milton, J. P., Björn, U. and Michelangelo, G. S.
    [2013] Dynamic estimation of reflectivity by minimum-delay seismic trace decomposition. Geophysics, 78(3), P.V109–V117.
    [Google Scholar]
  3. Robinson, E. A. and Treitel.S.
    [1980] Geophysical signal analysis: Prentice-Hall Book Co.
    [Google Scholar]
  4. Taner, M. T. and Coburn, K.
    [1981] Surface-consistent deconvolution:Presented at 51st Ann. Soc. Explor. Geophys. Mtg.
    [Google Scholar]
  5. Yilmaz, Ö.
    [1987] Seismic data processing: SEG, Tulsa.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201701086
Loading
/content/papers/10.3997/2214-4609.201701086
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