1887
Volume 38 Number 7
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397
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2020-07-01
2024-04-25
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References

  1. Alerini, M. and Ursin, B.
    [2009]. Adaptive focusing window for seismic angle migration.Geophysics, 74 (1), S1–S10.
    [Google Scholar]
  2. Ball, V., Blangy, J.P., Pringle, K. and Schwark, J.
    [2011]. Seismic rock physics in the presence of attribute noise.81st SEG Annual Meeting, Extended Abstracts, 355–359.
    [Google Scholar]
  3. Cambois, G.
    [2001]. AVO processing: Myths and reality.71st SEG Annual Meeting, Extended Abstracts.
    [Google Scholar]
  4. Chemingui, N. and Biondi, B.
    [2002]. Seismic data reconstruction by inversion to common offset.Geophysics, 67, 1575–1585.
    [Google Scholar]
  5. Farmani, B. and Pedersen, M.
    [2020]. Extended Attributes for Machine Learning Denoise Process: First Step Towards Automation.82nd EAGE conference and Exhibition, Extended Abstracts.
    [Google Scholar]
  6. Gardner, G. H. F. and Canning, A.
    [1994]. Effects of irregular sampling on 3-D prestack migration.64th SEG Annual International Meeting. Expanded Abstracts, 1553–1556.
    [Google Scholar]
  7. Goodfellow, I., Bengio Y. and Courville, A.
    [2017]. Deep learning.MIT Press, Cambridge, MA.
    [Google Scholar]
  8. Hale, D.
    [2011]. Structure-oriented bilateral filtering of seismic images.81st SEG Annual Meeting, Expanded Abstracts, 3596–3600.
    [Google Scholar]
  9. Jia, Y. and Ma, J.
    [2017]. What can machine learning do for seismic processing? An interpolation application.Geophysics, 82 (3), 163–177.
    [Google Scholar]
  10. Klokov, A. and Fomel, S.
    [2012]. Optimal migration aperture for conflicting dips.82nd SEG Annual Meeting, Expanded Abstracts, 1–6.
    [Google Scholar]
  11. Martin, T, Saturni, C., Ashby, P.
    [2015]. Using machine learning to produce a global automated quantitative QC for noise attenuation.85th SEG Annual Meeting, Expanded Abstracts, 4790–4794.
    [Google Scholar]
  12. Øye, O. K. and Dahl, E. K.
    [2019]. Velocity Model Building from Raw Shot Gathers Using Machine Learning.81st EAGE conference and Exhibition, Extended Abstracts.
    [Google Scholar]
  13. Ronneberger, O., Fischer, P. and Brox, T.
    [2015]. U-Net: Convolutional Networks for biomedical Image Segmentation.Medical Image Computing and Computer-Assisted Intervention (MICCAI), 9351, 234–241.
    [Google Scholar]
  14. Schonewille, M., Klaedtke, A., Vigner, A., Brittan, J. and Martin, T.
    [2009]. Seismic data regularization with the anti-alias anti-leakage Fourier transform.First Break, 27, 85–92.
    [Google Scholar]
  15. Turquais, P., Söllner, W. and Pedersen, M.
    [2019]. Parabolic Dictionary Learning: A Method or Seismic Data Reconstruction Beyond the Linearity Assumption.81st EAGE Conference and Exhibition, Extended Abstracts.
    [Google Scholar]
  16. Yang, F. and Ma, J.
    [2019]. Deep-learning inversion: A next-generation seismic velocity model building method.Geophysics, 84 (4), 583–599.
    [Google Scholar]
  17. Zheng, Y., Zhang, Q., Yusifov, A., Shi, Y.
    [2019]. Applications of supervised deep learning for seismic interpretation and inversion.The Leading Edge, 38 (7), 526–533.
    [Google Scholar]
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