1887

Abstract

Summary

Based on image recognition ability of convolutional neural network (CNN), we propose a method of impedance inversion using sub-image element. First, sub-image element of seismic record is extracted as the input while the value of corresponding logging impedance is considered as the output of dataset. Then, based on the training and testing set, impedance profile is obtained by CNN. Finally, the predicted results of the proposed method are compared with those using traditional CNN, BP and Bayesian inversion, which shows that the proposed method can efficiently obtain more accurate and reasonable results only using seismic data.

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/content/papers/10.3997/2214-4609.201901391
2019-06-03
2024-04-16
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References

  1. Yang, L., Song, H. and Hao, T.
    [2005] Application of impedance inversion based on BP neural network.Process in Geophysics, 20(1), 34–37.
    [Google Scholar]
  2. Krizhevsky, A., Sutskever, I. and Hinton, G. E.
    [2012] Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).
    [Google Scholar]
  3. Abdel-Hamid, O., Mohamed, A. R., Jiang, H. and Penn, G.
    [2012] Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on (pp. 4277–4280). IEEE.
    [Google Scholar]
  4. Wu, X., Shi, Y., Fomel, S. and Liang, L.
    [2018] Convolutional neural networks for fault interpretation in seismic images. In SEG Technical Program Expanded Abstracts 2018 (pp. 1946–1950). Society of Exploration Geophysicists.
    [Google Scholar]
  5. Shi, Y., Wu, X. and Fomel, S.
    [2018] Automatic salt-body classification using a deep convolutional neural network. In SEG Technical Program Expanded Abstracts 2018 (pp. 1971–1975). Society of Exploration Geophysicists.
    [Google Scholar]
  6. Duan, Y. X., Li, G. T. and Sun, Q. F.
    [2016] Research on convolutional neural network for reservoir parameter prediction.Journal on Communications, (s1), 1–9.
    [Google Scholar]
  7. Fu, C., Lin, N., Zhang, D., Wen, B., Wei, Q. and Zhang, K.
    [2018] Prediction of reservoirs using multi-component seismic data and the deep learning method.Chinese Journal of Geophysics-Chinese Edition, 61(1), 293–303.
    [Google Scholar]
  8. Sang, K. H. and Zhang, F. C.
    [2018] Prediction of Reservoir Parameters of Machine Learning Based on Fuzzy Rough Set[J].CT Theory and Applications, 27(4): 455–464.
    [Google Scholar]
  9. Martin, G. S., Wiley, R. and Marfurt, K. J.
    [2006] Marmousi2: An elastic upgrade for Marmousi.The Leading Edge, 25(2), 156–166.
    [Google Scholar]
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