1887

Abstract

Summary

Machine learning is becoming an attractive tool in various fields of earth sciences. During seismic data processing, velocity auto-picking can reduce time consumed on processing large volumes of seismic data and increase the number of velocity semblances which will be picked in a 3D seismic survey. In this paper, a new velocity auto-picking method on the base of unsupervised machine learning was proposed, a guide velocity as a constraint to pre-processing the velocity semblance was introduced and K-means clustering had been used to accomplish the velocity auto-picking. Real data processing shows that the proposed method is accurate and stable. The proposed method in the future will help geophysicist potentially reduce time spent on velocity semblance picking and obtain the relatively accurate time-velocity pairs effectively.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201800919
2018-06-11
2020-04-06
Loading full text...

Full text loading...

References

  1. Hall, B.
    (2016), Facies classification using machine learning, The Leading Edge, (October), 906–909, http://doi:10.1190/tle35100906.1.
    [Google Scholar]
  2. Smith, K. J., and S.Geoservices
    (2017), Machine learning assisted velocity auto-picking, 73rd Annual International Meeting, SEG, Expanded Abstracts, 5686–5690.
    [Google Scholar]
  3. Ratcliffe, A., and G.Roberts
    , 2003, Robust, automatic, continuous velocity picking: 87rd Annual International Meeting, SEG, Expanded Abstracts, 2080–2083, https://doi.org/10.1190/L1817743.
    [Google Scholar]
  4. Araya-Polo, M., T.Dahlke, C.Frogner, C.Zhang, T.Poggio, and D.Hohl
    , 2017, Automated fault detection with seismic processing: The Leading Edge, 36, 208–214, https://doi.org/10.1190/tle36030208.1.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201800919
Loading
/content/papers/10.3997/2214-4609.201800919
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