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Abstract

Summary

Local dip field has have been widely used in geophysical applications, such as structure prediction, seislet transform, trace interpolation and denoise. The plane-wave destruction (PWD) is the common method to estimate the local slope. However, the PWD is sensitive to strong noise. It is not easy to estimate an accurately local slope from noisy data by PWD algorithm. To estimate an accurate slope from noisy seismic data, we have proposed an architecture based on deep learning (DL). The architecture contains two sections: the convolutional and deconvolutional sections. The conventional section can learn the local features and the deconvolutional section constructs the output using the learned feature to match the target. Numerical tests on two examples demonstrate that the proposed method can obtain a relatively accurate dip field from noisy data.

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/content/papers/10.3997/2214-4609.202010260
2021-10-18
2024-04-29

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References

  1. Chen, Y., Zhang, G., Bai, M., Zu, S., Guan, Z. and Zhang, M.
    [2019] Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network.Earth and Space Science, 6, 1244–1261.
     [Google Scholar]
  2. Fomel, S.
    [2002] Applications of plane-wave destruction filters.Geophysics, 67, 1946–1960.
     [Google Scholar]
  3. [2008] Predictive painting of 3D seismic volumes.Geophysics, 75, A25–A30.
     [Google Scholar]
  4. Li, S., Ren, Y., Chen, Y., Yang, S., Wang, Y. and Jiang, P.
    [2019] Deep learning Inversion of Seismic Data.arXiv preprint arXiv:1901.07733.
     [Google Scholar]
  5. Liu, Y., Fomel, S. and Liu, C.
    [2015] Signal and noise separation in prestack seismic data using velocity-dependent seislet transform.Geophysics, 80, WD117–WD128.
     [Google Scholar]
  6. Liu, Y., Fomel, S. and Liu, G.
    [2010] Nonlinear structure-enhancing filtering using plane-wave prediction.Geophysical Prospecting, 58, 415–427.
     [Google Scholar]
  7. Marfurt, K.J.
    [2006] Robust estimates of 3D reflector dip and azimuth.Geophysics, 71, P29–P40.
     [Google Scholar]
  8. Mousavi, S.M., Zhu, W., William, E. and Gregory, B.
    [2019] Unsupervised Clustering of Seismic Signals Using Deep Convolutional Autoencoders.IEEE Geoscience & Remote Sensing Letters, 16, 1693–1697.
     [Google Scholar]
  9. Ottolini, R.
    [1983] Signal/noise separation in dip space.SEP report, 37, 143–149.
     [Google Scholar]
  10. Wang, B., Zhang, N., Lu, W. and Wang, J.
    [2018] Deep-learning-based seismic data interpolation: A preliminary result.Geophysics, 84, V11–V20.
     [Google Scholar]
  11. Zhang, G., Wang, Z. and Chen, Y.
    [2018] Deep learning for seismic lithology prediction.Geophysical Journal International, 215, 1368–1387.
     [Google Scholar]
  12. Zhang, K., Zuo, W., Chen, Y., Meng, D. and Zhang, L.
    [2017] Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.IEEE Transactions on Image Processing, 26, 3142–3155.
     [Google Scholar]
  13. Zu, S., Hui, Z., Rushan, W., Maocai, J. and Yangkang, C.
    [2019] Dictionary learning based on dip patch selection training for random noise attenuation.Geophysics, 84, V169–V183.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202010260
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Slope Estimation by Convolutional Neural Networks
Conference Proceedings 2021, 1 (2021); https://doi.org/10.3997/2214-4609.202010260
/content/papers/10.3997/2214-4609.202010260
/content/papers/10.3997/2214-4609.202010260
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