1887
Volume 37, Issue 9
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Abstract

Seismic data are often either irregularly or insufficiently sampled. Irregular sampling can be due to encountered obstacles in the acquisition, thus resulting in a seismic data gap, whereas insufficient sampling is the result of a coarse acquisition grid, thus leading to sparse sampling along the spatial direction of the data. This irregular or insufficient sampling can affect the accuracy and resolution of seismic data processing steps such as surface-related multiple elimination, migration and inversion. For example, in the simple case of sparse sampling, it leads not just to the loss of high-wavenumbers, but also causes spatial aliasing due to the overlap of aliasing energy artifacts with the signal energy. When we image this spatially aliased coarse data, we encounter the trade-off between the resolution of the image and the aliasing artifacts. Therefore, seismic data interpolation has always been an essential requirement in seismic data processing.

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2019-09-01
2024-04-23
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References

  1. Arjovsky, M., Chintala, S. and Bottou, L.
    [2017]. Wasserstein Generative Adversarial Networks. 34th International Conference on Machine Learning, PMLR 70,214–223.
    [Google Scholar]
  2. Dong, C., Loy, C.C., He, K. and Tang, X.
    [2015]. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2), 295–307.
    [Google Scholar]
  3. Fomel, S.
    [2003]. Seismic reflection data interpolation with differential offset and shot continuation. Geophysics, 68, 733–744.
    [Google Scholar]
  4. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S. and Bengio, Y.
    [2014]. Generative adversarial nets. Advances in neural information processing systems, 2672–2680.
    [Google Scholar]
  5. Gulunay, N.
    [2003]. Seismic trace interpolation in the Fourier transform domain. Geophysics, 68, 355–369.
    [Google Scholar]
  6. Halpert, A.D.
    [2018]. Deep learning-enabled seismic image enhancement. 88th SEG Annual International Meeting, Expanded Abstracts, 2081–2085.
    [Google Scholar]
  7. Kim, J.J., Lee, K. and Lee, K. M.
    [2016]. Accurate image super-resolution using very deep convolutional networks. IEEE Conference on Computer Vision and Pattern Recognition, 1646–1654.
    [Google Scholar]
  8. Krizhevsky, A., Sutskever, I. and Hinton, G.E.
    [2012]. ImageNet classification with deep convolutional neural networks. 25th International Conference on Advances in Neural Information Processing Systems, 1097–1105.
    [Google Scholar]
  9. LeCun, Y., Bengio, Y. and Hinton, G.
    [2015]. Deep learning. Nature, 521, 436–444.
    [Google Scholar]
  10. Liu, B. and Sacchi, M.
    [2004]. Minimum weighted norm interpolation of seismic records. Geophysics, 69, 1560–1568.
    [Google Scholar]
  11. Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L. and Lerer, A.
    [2017]. Automatic differentiation in PyTorch. NIPS Autodiff Workshop: The Future of Gradient-based Machine Learning Software and Techniques.
    [Google Scholar]
  12. Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.
    [2016]. Deep convolutional neural networks for computer-aided detection. CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298.
    [Google Scholar]
  13. Spitz, S.
    [1991]. Seismic trace interpolation in the F-X domain. Geophysics, 56, 785–794.
    [Google Scholar]
  14. Trickett, S., Burroughs, L., Milton, A., Walton, L. and DackR.
    [2010]. Rank reduction-based trace interpolation. 80th SEG Annual International Meeting, Expanded Abstracts, 3829–3833.
    [Google Scholar]
  15. Yang, W., Zhang, X., Tian, Y., Wang, W. and Xue, J.
    [2019]. Deep learning for single image super-resolution: A brief review. IEEE Transactions on Multimedia, under review.
    [Google Scholar]
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