1887
Volume 43 Number 8
  • E-ISSN: 1365-2478

Abstract

Abstract

It is often very useful to be able to smooth velocity fields estimated from exploration seismic data. For example seismic migration is most successful when accurate but also smooth migration velocity fields are used. Smoothing in one, two and three dimensions is examined using North Sea velocity data.

A number of ways for carrying out this smoothing are examined, and the technique of locally weighted regression (LOESS) emerges as most satisfactory. In this method each smoothed value is formed using a local regression on a neighbourhood of points downweighted according to their distance from the point of interest. In addition the method incorporates ‘blending’ which saves computations by using function and derivative information, and ‘weighting and robustness’ which allows the smooth to be biased towards reliable points, or away from unreliable ones.

A number of other important factors are also considered: namely, the effect of changing the scales of axes, or of thinning the velocity field, prior to smoothing, as well as the problem of smoothing on to irregular subsurfaces.

Loading

Article metrics loading...

/content/journals/10.1111/j.1365-2478.1995.tb00296.x
2006-04-28
2024-04-26
Loading full text...

Full text loading...

References

  1. ClevelandW.S.1979. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association74, 829–836.
    [Google Scholar]
  2. ClevelandW.S., DevlinS.J. and GrosseE.1988. Regression by local fitting: methods, properties and computational algorithms. Journal of Economenics37, 87–114.
    [Google Scholar]
  3. ClevelandW.S. and GrosseE.1991. Computational methods for local regression. Statistics and Computing1, 47–62.
    [Google Scholar]
  4. YilmazO.1987. Seismic Data Processing. Society of Exploration Geophysicists, Tulsa, USA .
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/j.1365-2478.1995.tb00296.x
Loading
  • Article Type: Research Article

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