1887

Abstract

Summary

Random noise attenuation always played an important role in seismic data processing. This study introduces an effective deep learning approach for seismic noise attenuation. The method design a deep feed forward denoising convolutional neural networks with residual learning approach. It learns the noise from the noisy images instead of the latent clean images and obtains the denoised images by subtracting the learned residual from the noisy image. Moreover, the new representative achievements integrated with the residual learning include rectified linear unit and batch normalization. Then, we train the CNN model with poststack field datasets and use the model to suppress the random noise. The results of the field data reveal that the algorithm can remove the random noise and highlight the locally continuous reflectors without losing the resolution of these features.

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/content/papers/10.3997/2214-4609.201900851
2019-06-03
2020-08-06
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References

  1. Fomel, S.
    , 2007, Shaping regularization in geophysical-estimation problems: Geophysics, 72, no. 2, R29–R36.
    [Google Scholar]
  2. HeK, ZhangX, RenS, et al.
    , 2015. Deep Residual Learning for Image Recognition[J]. 770–778.
    [Google Scholar]
  3. Gulunay, N.
    , 2000, Noncausal spatial prediction filtering for random noise reduction on 3D poststack data: Geophysics, 65, 1641–1653.
    [Google Scholar]
  4. KrizhevskyA, SutskeverI, HintonG E
    , 2012. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc.1097–1105.
    [Google Scholar]
  5. LiuY, LiuN, LiuC
    2015, Adaptive prediction filtering int-x-ydomain for random noise attenuation using regularized nonstationary autoregression: Geophysics, 80(1), no.1 13–21.
    [Google Scholar]
  6. LecunY, BengioY, HintonG
    2015. Deep learning[J]. Nature, 521(7553):436.
    [Google Scholar]
  7. IoffeS, SzegedyC
    , 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift[J]. 448–456.
  8. Neelamani, R., A. I.Baumstein, D. G.Gillard, M. T.Hadidi, and W. L.Soroka
    , 2008, Coherent and random noise attenuation using the curvelet transform: The Leading Edge, 27, 240–248.
    [Google Scholar]
  9. Trickett, S., and L.Burroughs
    , 2009, Prestack rank-reduction-based noise suppression: CSEG Recorder, 34, 24–31.
    [Google Scholar]
  10. Zhang, R., and T.Ulrych
    , 2003, Physical wavelet frame denoising: Geophysics, 68, 225–231.
    [Google Scholar]
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