1887

Abstract

Summary

In Ocean Bottom Multicomponent acquisitions, the particle motion recordings can be heavily contaminated with shear-wave noise. The presence of this shear-wave noise, especially on the Z-component, hinders the preprocessing steps required to achieve an accurate calibration of the pressure and Z-component for up/down wave separation, also referred to as PZ summation. Failure to effectively attenuate this noise may reduce the quality of the separation and consequently the quality of the processing products that depend on it (up/down deconvolution and imaging). We implement a simple and flexible approach that operates in the Radon domain and uses clustering techniques borrowed from unsupervised machine learning to identify the coefficients that are likely noise and those that are likely signal. We use a set of local structural and similarity attributes to define the space of features for clustering and test the methodology on a real data example.

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/content/papers/10.3997/2214-4609.201901347
2019-06-03
2024-04-19
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References

  1. Amundsen, L.
    [2001] Elimination of free-surface related multiples without the need of the source wavelet.Geophysics, 66(1), 327–341.
    [Google Scholar]
  2. Craft, K., L. and Paffenholz, J.
    [2007] Geophone noise attenuation and wave-field separation using multi-dimensional decomposition technique. 77th SEG Annual Meeting, Expanded Abstracts, 2630–2634.
    [Google Scholar]
  3. Dash, R., Spence, G., Hyndman, R., Grion, S. Wang, Y. and Ronen, S.
    [2009] Wide-area imaging from OBS multiples.Geophysics, 74(6), Q41–Q47.
    [Google Scholar]
  4. Fomel, S.
    [2007] Local seismic attributes.Geophysics, 72(3), A29–A33.
    [Google Scholar]
  5. Hale, D.
    [2006] Fast local cross-correlations of images. 76th SEG Annual Meeting, Expanded Abstracts, 3160–3164.
    [Google Scholar]
  6. Naeini, E. Z. L., Baboulaz, L. and Grion, S.
    [2011] Enhanced wavefield separation of OBS data. 73rd EAGE Conference and Exhibition, Extended Abstracts, B003.
    [Google Scholar]
  7. Paffenholz, J., Shurtleff, R., Hays, D. and Doherty, P.
    [2006] Shear Wave Noise on OBS Vz Data – Part I Evidence from Field Data. 68th EAGE Conference and Exhibition, Extended Abstracts, B046.
    [Google Scholar]
  8. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.
    [2011] Scikit-learn: Machine learning in Python.Journal of Machine Learning Research, 12, 2825–2830.
    [Google Scholar]
  9. Peng, C., Jin, H. and Wang, P.
    [2014] Noise attenuation for multi-sensor streamer data via cooperative de-noising. 84th SEG Annual Meeting, Expanded Abstracts, 1878–1882.
    [Google Scholar]
  10. Press, W. H., Teukolsky, S. A., Vetterling, W.T., Flannery, B. P.
    [2007] Numerical Recipes: The art of scientific computing. Cambridge University Press, New York.
  11. Poole, G., Casasanta, L. and Grion, S.
    [2012] Sparse τ-p Z-noise attenuation for ocean-bottom data. 82nd SEG Annual Meeting, Expanded Abstracts, 1–5.
    [Google Scholar]
  12. Stork, C.
    [2017] Removing complex land noise with modern pattern recognition using Machine Learning. 87th SEG Annual Meeting, Expanded Abstracts, 6089–6093.
    [Google Scholar]
  13. Turquais, P., Asgedom, E. G. and SöllnerW.
    [2017] Coherent noise suppression by learning and analyzing the morphology of the data.Geophysics, 82(6), V397–V441.
    [Google Scholar]
  14. van Vliet, L. J. and Verbeek, P.W.
    [1995] Estimators for orientation and anisotropy in digitized images. ASCI’95, Proceedings of the first Annual Conference of the Advanced School for Computing and Imaging, 442–450.
    [Google Scholar]
  15. Yu, Z., Kumar, C. and Ahmed
    [2011] Ocean bottom seismic noise attenuation using local attribute matching filter. 81st SEG Annual Meeting, Expanded Abstracts, 3586–3590.
    [Google Scholar]
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