1887

Abstract

Summary

Swell noise attenuation is the first step in the processing sequence of marine seismic data. Often this filtering requires a good amount of testing to achieve optimal results, particularly when swell noise characteristics vary considerably along the survey. In the context of on-board processing this filtering is the bottleneck of the production sequence. It would be very useful both technically and economically if there exists a solution that automate this process. This paper proposes a data driven method for swell noise attenuation. It is based on improving the detection of swell noise by deriving its characteristics from the data. The filtering parameters are automatically tailored to suit the spatial and temporal frequency spread of swell noise in each filtered gather. When compared to a conventional method, it gives similar results but with much less testing effort. It is highly data adaptive and can be used to attenuate gathers with different noise level using the same minimal parameterisation.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.20141442
2014-06-16
2020-07-09
Loading full text...

Full text loading...

References

  1. Canales, L
    (1984), “Random noise reduction”, 54th SEG Annual International Meeting, 525–527.
    [Google Scholar]
  2. Bekara, M. and Van der BaanM
    (2010), “High-amplitude noise detection by the expectation-maximization algorithm with application to swell-noise attenuation”, Geophysics, 75, no. 3, V39–V49.
    [Google Scholar]
  3. Bekara, M.
    ,(2004) “A model selection criterion for small data record using the symmetric Kullback divergence”, PhD thesis, University of Paris XI, France.
    [Google Scholar]
  4. Shonewille, M; Vigner, A and RyderA.
    , (2008), “Advances in swell-noise attenuation”, First break vol26, pp.103–108.
    [Google Scholar]
  5. Spanos, A., and Bekara, M
    ,(2012) “Using Statistical Techniques to Improve the QC Process of Swell Noise Filtering”, 75th EAGE Conference & Exhibition incorporating.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20141442
Loading
/content/papers/10.3997/2214-4609.20141442
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