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Abstract

Summary

Metrics to assess machine learning methods are necessary for evaluation of results and for comparisons to existing methods. For the segmentation of faults in seismic data, we suggest the use of a robust Jaccard metric that allows for small lateral inaccuracies in fault positioning. This error tolerance is necessary because interpretations are often inaccurate or subjective as a result of low seismic resolution, noise or other image deficiencies. The metric is used to evaluate results during the development of a 3D convolution neural network. In practice, this is done by applying new versions of the convolutional neural network to field data and by using metrics to compare machine learning results to the manual interpretations.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202032015
2020-11-30
2024-04-25

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References

  1. Gramstad, O., Goledowski, B. and Nickel, M.
    [2019] Automated channel detection using deep convolutional neural networks. In: SEG Technical Program Expanded Abstracts 2019, Society of Exploration Geophysicists, 2483–2487.
     [Google Scholar]
  2. Gramstad, O. and Nickel, M.
    [2018] Automated top salt interpretation using a deep convolutional net. In: 80th EAGE Conference and Exhibition 2018.
     [Google Scholar]
  3. Guitton, A.
    [2018] 3D convolutional neural networks for fault interpretation. In: 80th EAGE Conference and Exhibition 2018.
     [Google Scholar]
  4. Li, Z., Di, H., Maniar, H. and Abubakar, A.
    [2019] Semi-Supervised deepmachine learning assisted seismic image segmentation and stratigraphic sequence interpretation. In: 81st EAGE Conference and Exhibition 2019.
     [Google Scholar]
  5. Ronneberger, O., Fischer, P. and Brox, T.
    [2015] U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, 234–241.
     [Google Scholar]
  6. Wu, X., Liang, L., Shi, Y. and 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]
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Robust Evaluation of Fault Prediction Results: Machine Learning Using Synthetic Seismic
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202032015
/content/papers/10.3997/2214-4609.202032015
/content/papers/10.3997/2214-4609.202032015
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