1887

Abstract

Summary

A main challenge in seismic imaging is acquiring densely sampled data. Compressed Sensing has provided theoretical foundations upon which desired sampling rate can be achieved by applying a sparsity promoting algorithm on sub-sampled data. The key point in successful recovery is to deploy a randomized sampling scheme. In this paper, we propose a novel deep learning-based method for fast and accurate reconstruction of heavily under-sampled seismic data, regardless of type of sampling. A neural network learns to do reconstruction directly from data via an adversarial process. Once trained, the reconstruction can be done by just feeding the frequency slice with missing data into the neural network. This adaptive nonlinear model makes the algorithm extremely flexible, applicable to data with arbitrarily type of sampling. With the assumption that we have access to training data, the quality of reconstructed slice is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201801393
2018-06-11
2024-04-20
Loading full text...

Full text loading...

References

  1. Csáji, B.C.
    [2001] Approximation with artificial neural networks. Faculty of Sciences, Etvs Lornd University, Hungary, 24, 48.
    [Google Scholar]
  2. Da Silva, C. and Herrmann, F.J.
    [2014] Low-rank Promoting Transformations and Tensor Interpolation-Applications to Seismic Data Denoising. In: 76th EAGE Conference and Exhibition 2014.
    [Google Scholar]
  3. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y.
    [2014] Generative Adversarial Nets. Advances in neural information processing systems, 2672–2680.
    [Google Scholar]
  4. Hauser, S. and Ma, J.
    [2012] Seismic data reconstruction via shearlet-regularized directional inpainting.
    [Google Scholar]
  5. Herrmann, F.J. and Hennenfent, G.
    [2008] Non-parametric seismic data recovery with curvelet frames. Geophysical Journal International, 173(1), 233–248.
    [Google Scholar]
  6. Isola, P., Zhu, J.Y., Zhou, T. and Efros, A.A.
    [2016] Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004.
    [Google Scholar]
  7. Kreimer, N., Stanton, A. and Sacchi, M.D.
    [2013] Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction. Geophysics, 78(6), V273–V284.
    [Google Scholar]
  8. Krizhevsky, A., Sutskever, I. and Hinton, G.E.
    [2012] Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 1097–1105.
    [Google Scholar]
  9. Kumar, R., Aravkin, A.Y., Mansour, H., Recht, B. and Herrmann, F.J.
    [2013] Seismic data interpolation and denoising using svd-free low-rank matrix factorization. In: 75th EAGE Conference & Exhibition incorporating SPE EUROPEC 2013.
    [Google Scholar]
  10. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z. and Smolley, S.P.
    [2016] Least squares generative adversarial networks. arXiv preprint ArXiv:1611.04076.
    [Google Scholar]
  11. Oropeza, V. and Sacchi, M.
    [2011] Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis. Geophysics, 76(3), V25–V32.
    [Google Scholar]
  12. Sacchi, M.D., Ulrych, T.J. and Walker, C.J.
    [1998] Interpolation and extrapolation using a high-resolution discrete Fourier transform. IEEE Transactions on Signal Processing, 46(1), 31–38.
    [Google Scholar]
  13. Wason, H. and Herrmann, F.J.
    [2013] Time-jittered ocean bottom seismic acquisition. In: SEG Technical Program Expanded Abstracts 2013, Society of Exploration Geophysicists, 1–6.
    [Google Scholar]
  14. Yarman, C.E., Kumar, R. and Rickett, J.
    [2017] A model based data driven dictionary learning for seismic data representation. Geophysical Prospecting.
    [Google Scholar]
  15. Zhu, J.Y., Park, T., Isola, P. and Efros, A.A.
    [2017a] Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593.
    [Google Scholar]
  16. Zhu, L., Liu, E. and McClellan, J.H.
    [2017b] Joint seismic data denoising and interpolation with double-sparsity dictionary learning. Journal of Geophysics and Engineering, 14(4), 802.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201801393
Loading
/content/papers/10.3997/2214-4609.201801393
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