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Volume 72 Number 1
  • E-ISSN: 1365-2478

Abstract

Abstract

We propose a method to generate seismic images with corresponding fault labels for augmenting training data in automatic fault detection. Our method is based on two generative adversarial networks: one for creating a fault system and the other for generating two‐dimensional seismic images with faults as a condition. Our method can capture the characteristics of field seismic data during inference to generate samples that have properties of both field seismic data and synthetic training data. We then use the newly generated seismic images with corresponding fault labels to train a convolutional neural network for fault picking. We test the proposed approach on a three‐dimensional field dataset from the Gulf of Mexico. We use different areas in the field dataset as input to generate new training data for corresponding fault‐picking models. The results show that the generated training data from our method help in improving the fault‐picking models in the targeted areas.

© 2024 European Association of Geoscientists & Engineers. © 2022 European Association of Geoscientists & Engineers.
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/content/journals/10.1111/1365-2478.13245
2023-12-18
2025-04-25

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References

  1. Araya‐Polo, M., Dahlke, T., Frogner, C., Zhang, C., Poggio, T. & Hohl, D. (2017) Automated fault detection without seismic processing. The Leading Edge, 36, 208–214.
     [Google Scholar]
  2. Arjovsky, M., Chintala, S. & Bottou, L. (2017) Wasserstein generative adversarial networks. In: Precup, D. & Teh, Y.W. (Eds.) Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research. PMLR. MIT Press, pp. 214–223.
     [Google Scholar]
  3. Bartel, C.J. (2021) Data‐centric approach to improve machine learning models for inorganic materials. Patterns, 2(11), 100382.
     [Google Scholar]
  4. Chopra, S. & Marfurt, K.J. (2007) Seismic Attributes for Prospect Identification and Reservoir Characterization. Society of Exploration Geophysicists.
     [Google Scholar]
  5. Dagan, I., Lee, L. & Pereira, F. (1997) Similarity‐based methods for word sense disambiguation. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, ACL ′98/EACL ′98. Association for Computational Linguistics, pp. 56–63.
     [Google Scholar]
  6. Endres, D. & Schindelin, J. (2003) A new metric for probability distributions. IEEE Transactions on Information Theory, 49, 1858–1860.
     [Google Scholar]
  7. Gersztenkorn, A. & Marfurt, K.J. (1999) Eigenstructure‐based coherence computations as an aid to 3‐D structural and stratigraphic mapping. Geophysics, 64, 1468–1479.
     [Google Scholar]
  8. Goodfellow, I., Pouget‐Abadie, J., Mirza, M., Xu, B., Warde‐Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2014) Generative adversarial nets. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. & Weinberger, K.Q. (Eds.) Advances in Neural Information Processing Systems, 27, 2672–2680. Curran Associates, Inc.
     [Google Scholar]
  9. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V. & Courville, A. (2017) Improved training of Wasserstein GANs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS′17. Curran Associates Inc., pp. 5769–5779.
     [Google Scholar]
  10. Hu, B., Lei, C., Wang, D., Zhang, S. & Chen, Z. (2019) A preliminary study on data augmentation of deep learning for image classification. In: Proceedings of the 11th Asia‐Pacific Symposium on Internetware, Internetware ′19. Association for Computing Machinery, 1–6.
  11. Hu, G., Hu, Z., Liu, J., Cheng, F. & Peng, D. (2022) Seismic fault interpretation using deep learning‐based semantic segmentation method. IEEE Geoscience and Remote Sensing Letters, 19, No: 7500905, 1–5.
     [Google Scholar]
  12. Huang, L., Dong, X. & Clee, T.E. (2017) A scalable deep learning platform for identifying geologic features from seismic attributes. The Leading Edge, 36, 249–256.
     [Google Scholar]
  13. Isola, P., Zhu, J.‐Y., Zhou, T. & Efros, A.A. (2017) Image‐to‐image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. IEEE, pp. 5967–5976.
     [Google Scholar]
  14. Kantorovich, L. & Rubinstein, G.S. (1958) On a space of totally additive functions. Vestnik Leningrad University, 13, 52–59.
     [Google Scholar]
  15. Kantorovich, L.V. (1960) Mathematical methods of organizing and planning production. Management Science, 6, 366–422.
     [Google Scholar]
  16. Karimi, P., Fomel, S., Wood, L. & Dunlap, D. (2015) Predictive coherence. Interpretation, 3, SAE1–SAE7.
     [Google Scholar]
  17. Kaur, H., Pham, N. & Fomel, S. (2020) Improving the resolution of migrated images by approximating the inverse Hessian using deep learning. Geophysics, 85, WA173–WA183.
     [Google Scholar]
  18. Kaur, H., Pham, N. & Fomel, S. (2021) Seismic data interpolation using deep learning with generative adversarial networks. Geophysical Prospecting, 69, 307–326.
     [Google Scholar]
  19. Kington, J. (2015) Semblance, coherence, and other discontinuity attributes. The Leading Edge, 34, 1510–1512.
     [Google Scholar]
  20. Kullback, S. (1959) Information Theory and Statistics. Wiley.
     [Google Scholar]
  21. Liu, M., Jervis, M., Li, W. & Nivlet, P. (2020) Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks. Geophysics, 85, O47–O58.
     [Google Scholar]
  22. Marfurt, K.J., Kirlin, R.L., Farmer, S.L. & Bahorich, M.S. (1998) 3‐D seismic attributes using a semblance‐based coherency algorithm. Geophysics, 63, 1150–1165.
     [Google Scholar]
  23. Mirza, M. & Osindero, S. (2014) Conditional generative adversarial nets, ArXiv, arXiv, 1411–1784.
     [Google Scholar]
  24. Mosser, L., Dubrule, O. & Blunt, M.J. (2019) Stochastic seismic waveform inversion using generative adversarial networks as a geological prior. Mathematical Geosciences, 52, 53–79.
     [Google Scholar]
  25. Motamedi, M., Sakharnykh, N. & Kaldewey, T. (2021) A data‐centric approach for training deep neural networks with less data. In: Proceedings of the 35th International Conference on Neural Information Processing Systems, NIPS′21. Curran Associates Inc., 1–5.
  26. Nalepa, J., Marcinkiewicz, M. & Kawulok, M. (2019) Data augmentation for brain‐tumor segmentation: A review. Frontiers in Computational Neuroscience, 13, 83.
     [Google Scholar]
  27. Neff, T., Payer, C., Štern, D. & Urschler, M. (2018) Generative adversarial networks to synthetically augment data for deep learning based image segmentation. In: Proceedings of the OAGM Workshop 2018: Medical Image Analysis, Hall/Tyrol, Austria. Graz, Austria: Verlag der Technischen Universitít Graz, pp. 22–29.
     [Google Scholar]
  28. Pandey, S., Singh, P.R. & Tian, J. (2020) An image augmentation approach using two‐stage generative adversarial network for nuclei image segmentation. Biomedical Signal Processing and Control, 57, 101782.
     [Google Scholar]
  29. Pham, N., Dunlap, D. & Fomel, S. (2021) Channel facies and faults multisegmentation in seismic volumes. In: First International Meeting for Applied Geoscience and Energy. SEG, Expanded Abstracts, pp. 1430–1434.
     [Google Scholar]
  30. Phillips, M. & Fomel, S. (2017) Plane‐wave Sobel attribute for discontinuity enhancement in seismic images. Geophysics, 82, WB63–WB69.
     [Google Scholar]
  31. Picetti, F., Lipari, V., Bestagini, P. & Tubaro, S. (2018) A generative‐adversarial network for seismic‐imaging applications. In: 88th Annual International Meeting. SEG, Expanded Abstracts, pp. 2231–2235.
     [Google Scholar]
  32. Rolnick, D., Veit, A., Belongie, S. & Shavit, N. (2018) Deep learning is robust to massive label noise, ArXiv, arXiv, 1705–10694.
     [Google Scholar]
  33. Ronneberger, O., Fischer, P. & Brox, T. (2015) U‐Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M. & Frangi, A.F. (Eds.) Medical Image Computing and Computer‐Assisted Intervention – MICCAI 2015. Springer International Publishing, pp. 234–241.
     [Google Scholar]
  34. Sandfort, V., Yan, K., Pickhardt, P. & Summers, R. (2019) Data augmentation using generative adversarial networks (cyclegan) to improve generalizability in CT segmentation tasks. Scientific Reports, 9, 16884.
     [Google Scholar]
  35. Sobel, I. & Feldman, G. (1968) A 3x3 isotropic gradient operator for image processing. Stanford Artificial Project, 271–272.
     [Google Scholar]
  36. Song, H., Kim, M., Park, D. & Lee, J.‐G. (2020) Learning from noisy labels with deep neural networks: a survey [Preprint]. ArXiv, abs/2007.08199.
  37. Thanh‐Tung, H., Tran, T. & Venkatesh, S. (2019) Improving generalization and stability of generative adversarial networks. In: ICLR 2019: Proceedings of the 7th International Conference on Learning Representations. ICLR, pp. 1–18.
     [Google Scholar]
  38. Wang, Y., Wang, Q., Lu, W., Ge, Q. & Yan, X. (2022) Seismic impedance inversion based on cycle‐consistent generative adversarial network. Petroleum Science, 19, 147–161.
     [Google Scholar]
  39. Wu, J., Zhang, C., Xue, T., Freeman, W.T. & Tenenbaum, J.B. (2016) Learning a probabilistic latent space of object shapes via 3D generative‐adversarial modeling. In: Advances in Neural Information Processing Systems. Curran Associates Inc., pp. 82–90.
     [Google Scholar]
  40. Wu, X. (2017) Directional structure‐tensor‐based coherence to detect seismic faults and channels. Geophysics, 82, A13–A17.
     [Google Scholar]
  41. Wu, X. & Fomel, S. (2018) Automatic fault interpretation with optimal surface voting. Geophysics, 83, O67–O82.
     [Google Scholar]
  42. Wu, X., Geng, Z., Shi, Y., Pham, N., Fomel, S. & Caumon, G. (2020) Building realistic structure models to train convolutional neural networks for seismic structural interpretation. Geophysics, 85, WA27–WA39.
     [Google Scholar]
  43. 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, IM35–IM45.
     [Google Scholar]
  44. Wu, X., Shi, Y., Fomel, S. & Liang, L. (2018) Convolutional neural networks for fault interpretation in seismic images. In: 88th Annual International Meeting. SEG, Expanded Abstracts, pp. 1946–1950.
     [Google Scholar]
  45. Wu, X., Shi, Y., Fomel, S., Liang, L., Zhang, Q. & Yusifov, A.Z. (2019) Faultnet3d: predicting fault probabilities, strikes, and dips with a single convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 57, 9138–9155.
     [Google Scholar]
  46. Yuan, Y., Si, X. & Zheng, Y. (2020) Ground‐roll attenuation using generative adversarial networks. Geophysics, 85, WA255–WA267.
     [Google Scholar]
  47. Zhang, H., Chen, T., Liu, Y., Zhang, Y. & Liu, J. (2021) Automatic seismic facies interpretation using supervised deep learning. Geophysics, 86, IM15–IM33.
     [Google Scholar]
  48. Zhao, T. & Mukhopadhyay, P. (2018) A fault‐detection workflow using deep learning and image processing. In: 88th Annual International Meeting. SEG, Expanded Abstracts, pp. 1966–1970.
     [Google Scholar]
  49. Zhao, Y.X., Li, Y. & Wu, N. (2021) Data augmentation and its application in distributed acoustic sensing data denoising. Geophysical Journal International, 228, 119–133.
     [Google Scholar]
  50. Zhu, W., Mousavi, S.M. & Beroza, G.C. (2020) Chapter four – Seismic signal augmentation to improve generalization of deep neural networks. In: B.Moseley & L.Krischer (Eds.) Machine Learning in Geosciences, volume 61 of Advances in Geophysics. Elsevier, pp. 151–177.
     [Google Scholar]
/content/journals/10.1111/1365-2478.13245
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Seismic data augmentation for automatic fault picking using deep learning
Geophysical Prospecting 72, 125 (2023); https://doi.org/10.1111/1365-2478.13245
/content/journals/10.1111/1365-2478.13245
/content/journals/10.1111/1365-2478.13245
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  • Article Type: Research Article
Keyword(s): Interpretation; Seismics

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