1887
Volume 69, Issue 6
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Significant advances have been made towards fault detection using deep learning. However, the fault labelling of seismic data requires great human effort. The resulting small sample problem makes traditional deep learning methods difficult to achieve desired results. Existing research proposes to train a deep learning model with labelled synthetic seismic data to get good fault detection results. However, due to the complexity of the actual geological situation, there are inevitable differences between synthetic seismic data and real seismic data in many aspects such as seismic signal frequency, frequency of fault distribution and degree of noise disturbance, which lead to the fact that the deep learning model trained by synthetic seismic data is difficult to get good fault detection result in field data applications. We propose to use transfer learning to reduce the impact of data differences to solve this problem: part of the deep transfer learning model is used to learn fault‐related features. And the other part of the deep transfer learning model is used to mine common features between the real seismic data and the synthetic seismic data, which makes the deep transfer learning model more suitable for real seismic data. Compared with the latest research progress, our method can greatly improve the effect of fault detection without real data label, which can significantly save the cost of manual label processing.

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/content/journals/10.1111/1365-2478.13097
2021-06-14
2024-04-26
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References

  1. Ben‐David, S., Blitzer, J., Crammer, K. and Pereira, F. (2007) Analysis of representations for domain adaptation. Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press; pp. 137–144.
    [Google Scholar]
  2. Ben‐David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F. and Vaughan, J.W. (2010) A theory of learning from different domains. Machine Learning, 79(1–2), 151–175.
    [Google Scholar]
  3. Cunha, A., Pochet, A., Lopes, H. and Gattass, M. (2020) Seismic fault detection in real data using transfer learning from a convolutional neural network pre‐trained with synthetic seismic data. Computers & Geosciences, 135, 104344.
    [Google Scholar]
  4. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F et al. (2016) Domain‐adversarial training of neural networks. Journal of Machine Learning Research, 17(1), 2096–2030.
    [Google Scholar]
  5. Gao, J., Fan, W., Jiang, J. and Han, J. (2008) Knowledge transfer via multiple model local structure mapping. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; pp. 283–291.
    [Google Scholar]
  6. Girshick, R., Donahue, J., Darrell, T. and Malik, J. (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE; pp. 580–587.
    [Google Scholar]
  7. Guillon, S., Joncour, F., Goutorbe, P. and Castanié, L. (2019) Reducing training dataset bias for automatic fault detection. In: SEG Technical Program Expanded Abstracts 2019 Society of Exploration Geophysicists, pp. 2423–2427.
    [Google Scholar]
  8. Guitton, A. (2018) 3D convolutional neural networks for fault interpretation. In: 80th EAGE Conference and Exhibition 2018.
    [Google Scholar]
  9. Guo, B., Li, L. and Luo, Y. (2018) A new method for automatic seismic fault detection using convolutional neural network. In: SEG Technical Program Expanded Abstracts 2018 Society of Exploration Geophysicists, pp. 1951–1955.
    [Google Scholar]
  10. He, K., Gkioxari, G., Dollar, P. and Girshick, R. (2017) Mask r‐cnn[C]//Proceedings of the IEEE international conference on computer vision. 2961–2969.
  11. Hong, S., Oh, J., Han, B. and Lee, H. (2016) Learning transferrable knowledge for semantic segmentation with deep convolutional neural network. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV. IEEE; pp. 3204‐3212,
    [Google Scholar]
  12. Huang, L., Dong, X. and Clee, T.E. (2017) A scalable deep learning platform for identifying geologic features from seismic attributes. The Leading Edge, 36(3), 249–256.
    [Google Scholar]
  13. Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) ImageNet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 25, 1097–1105.
    [Google Scholar]
  14. Pan, S.J. and Yang, Q. (2009) A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
    [Google Scholar]
  15. Valiant, L.G. (1984) A theory of the learnable. Proceedings of the sixteenth annual ACM symposium on Theory of computing. ACM; pp. 436–445.
    [Google Scholar]
  16. Van Der Maaten, L. (2013) Barnes‐hut‐sne. arXiv preprint arXiv:13013342.
  17. Wu, X., Liang, L., Shi, Y., Fomel, S. (2019) FaultSeg3D: Using synthetic data sets to train an end‐to‐end convolutional neural network for 3D seismic fault segmentation. Geophysics, 84(3), IM35–IM45.
    [Google Scholar]
  18. Xiong, W., Ji, X., Ma, Y., Wang, Y., AlBinHassan, N.B., Ali, M.N. and Luo, y. (2018) Seismic fault detection with convolutional neural network. Geophysics, 83(5), O97–O103.
    [Google Scholar]
  19. Zeiler, M.D. and Fergus, R. (2014) Visualizing and understanding convolutional networks[C]//European conference on computer vision. Springer, Cham, pp. 818–833.
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): Data processing; Interpretation

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