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Semi-supervised Segmentation of Salt Bodies in Seismic Images Using an Ensemble of Convolutional Neural Networks

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Book cover Pattern Recognition (DAGM GCPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11824))

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Abstract

Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention. One of the essential challenges of seismic imaging is detecting subsurface salt structure which is indispensable for the identification of hydrocarbon reservoirs and drill path planning. Unfortunately, the exact identification of large salt deposits is notoriously difficult and professional seismic imaging often requires expert human interpretation of salt bodies. Convolutional neural networks (CNNs) have been successfully applied in many fields, and several attempts have been made in the field of seismic imaging. But the high cost of manual annotations by geophysics experts and scarce publicly available labeled datasets hinder the performance of the existing CNN-based methods. In this work, we propose a semi-supervised method for segmentation (delineation) of salt bodies in seismic images which utilizes unlabeled data for multi-round self-training. To reduce error amplification during self-training we propose a scheme which uses an ensemble of CNNs. We show that our approach outperforms state-of-the-art on the TGS Salt Identification Challenge dataset and is ranked the first among the 3234 competing methods. The source code is available at GitHub.

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References

  1. Bautista, M.A., Sanakoyeu, A., Tikhoncheva, E., Ommer, B.: CliqueCNN: deep unsupervised exemplar learning. In: Advances in Neural Information Processing Systems, pp. 3846–3854 (2016)

    Google Scholar 

  2. Bautista, M.A., Sanakoyeu, A., Ommer, B.: Deep unsupervised similarity learning using partially ordered sets. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1923–1932 (2017)

    Google Scholar 

  3. Bedi, J., Toshniwal, D.: SFA-GTM: seismic facies analysis based on generative topographic map and RBF. arXiv preprint arXiv:1806.00193 (2018)

  4. Berman, M., Rannen Triki, A., Blaschko, M.B.: The lovász-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4413–4421 (2018)

    Google Scholar 

  5. Büchler, U., Brattoli, B., Ommer, B.: Improving spatiotemporal self-supervision by deep reinforcement learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 797–814. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_47

    Chapter  Google Scholar 

  6. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062 (2014)

  7. Di, H., Shafiq, M., AlRegib, G.: Multi-attribute k-means clustering for salt-boundary delineation from three-dimensional seismic data. Geophys. J. Int. 215(3), 1999–2007 (2018)

    Article  Google Scholar 

  8. Di, H., Wang, Z., AlRegib, G.: Real-time seismic-image interpretation via deconvolutional neural network. In: SEG Technical Program Expanded Abstracts 2018, pp. 2051–2055. Society of Exploration Geophysicists (2018)

    Google Scholar 

  9. Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422–1430 (2015)

    Google Scholar 

  10. Dramsch, J.S., Lüthje, M.: Deep-learning seismic facies on state-of-the-art CNN architectures. SEG Technical Program Expanded Abstr. 2018, 2036–2040 (2018)

    Google Scholar 

  11. Fazakis, N., Karlos, S., Kotsiantis, S., Sgarbas, K.: Self-trained LMT for semisupervised learning. Comput. Intell. Neurosci. 2016, 10 (2016)

    Article  Google Scholar 

  12. Halpert, A., Clapp, R.G.: Salt body segmentation with dip and frequency attributes. Stanford Explor. Project 113, 1–12 (2008)

    Google Scholar 

  13. Han, W., Feng, R., Wang, L., Cheng, Y.: A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification. ISPRS J. Photogrammetry Remote Sens. 145, 23–43 (2018)

    Article  Google Scholar 

  14. Li, H., Xiong, P., An, J., Wang, L.: Pyramid attention network for semantic segmentation. In: Proceedings of the British Machine Vision Conference (BMVC) (2018)

    Google Scholar 

  15. Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 447–456 (2015)

    Google Scholar 

  16. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  17. Hegazy, T., AlRegib, G.: Texture attributes for detecting salt bodies in seismic data. In: SEG Technical Program Expanded Abstracts 2014, pp. 1455–1459. Society of Exploration Geophysicists (2014)

    Google Scholar 

  18. Iglovikov, V., Shvets, A.: TernausNet: U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation. arXiv preprint arXiv:1801.05746 (2018)

  19. Jiang, H., Larsson, G., Maire, M., Shakhnarovich, G., Learned-Miller, E.: Self-supervised relative depth learning for urban scene understanding. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 20–37. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_2

    Chapter  Google Scholar 

  20. Jones, I.F., Davison, I.: Seismic imaging in and around salt bodies. Interpretation 2(4), SL1–SL20 (2014)

    Article  Google Scholar 

  21. Kaggle: TGS salt identification challenge (2018). https://www.kaggle.com/c/tgs-salt-identification-challenge. Accessed 20 Oct 2018

  22. Karchevskiy, M., Ashrapov, I., Kozinkin, L.: Automatic salt deposits segmentation: a deep learning approach. arXiv Machine Learning (2018)

    Google Scholar 

  23. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report. Citeseer (2009)

    Google Scholar 

  24. Larsson, G., Maire, M., Shakhnarovich, G.: Learning representations for automatic colorization. In: European Conference on Computer Vision, pp. 577–593 (2016)

    Google Scholar 

  25. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  26. Lee, D.H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol. 3, p. 2 (2013)

    Google Scholar 

  27. Lee, H.Y., Huang, J.B., Singh, M., Yang, M.H.: Unsupervised representation learning by sorting sequences. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 667–676 (2017)

    Google Scholar 

  28. Lee, H.W., Kim, N.r., Lee, J.H.: Deep neural network self-training based on unsupervised learning and dropout. Int. J. Fuzzy Log. Intell. Syst. 17(1), 1–9 (2017)

    Google Scholar 

  29. Li, M., Zhou, Z.-H.: SETRED: self-training with editing. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 611–621. Springer, Heidelberg (2005). https://doi.org/10.1007/11430919_71

    Chapter  Google Scholar 

  30. Livieris, I.: A new ensemble semi-supervised self-labeled algorithm. Informatica 49, 221–234 (2019)

    Google Scholar 

  31. Livieris, I., Kanavos, A., Tampakas, V., Pintelas, P.: An ensemble SSL algorithm for efficient chest x-ray image classification. J. Imaging 4(7), 95 (2018)

    Article  Google Scholar 

  32. Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)

  33. Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69–84. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_5

    Chapter  Google Scholar 

  34. Peters, B., Granek, J., Haber, E.: Multi-resolution neural networks for tracking seismic horizons from few training images. arXiv preprint arXiv:1812.11092 (2018)

  35. Pitas, I., Kotropoulos, C.: A texture-based approach to the segmentation of seismic images. Pattern Recogn. 25(9), 929–945 (1992)

    Article  Google Scholar 

  36. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  37. Roy, A.G., Navab, N., Wachinger, C.: Concurrent spatial and channel ‘Squeeze & Excitation’ in fully convolutional networks. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 421–429. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_48

    Chapter  Google Scholar 

  38. Sanakoyeu, A., Bautista, M.A., Ommer, B.: Deep unsupervised learning of visual similarities. Pattern Recogn. 78, 331–343 (2018)

    Article  Google Scholar 

  39. Telford, W.M., Telford, W., Geldart, L., Sheriff, R.E., Sheriff, R.: Applied Geophysics, vol. 1. Cambridge University Press, Cambridge (1990)

    Book  Google Scholar 

  40. Triguero, I., García, S., Herrera, F.: Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study. Knowl. Inf. Syst. 42(2), 245–284 (2015)

    Article  Google Scholar 

  41. Waldeland, A.U., Jensen, A.C., Gelius, L.J., Solberg, A.H.S.: Convolutional neural networks for automated seismic interpretation. Lead. Edge 37(7), 529–537 (2018)

    Article  Google Scholar 

  42. Wang, G., Xie, X., Lai, J., Zhuo, J.: Deep growing learning. In: IEEE International Conference on Computer Vision (ICCV), pp. 2831–2839. IEEE (2017)

    Google Scholar 

  43. Wang, W., Yang, F., Ma, J.: Automatic salt detection with machine learning. In: 80th EAGE Conference and Exhibition 2018 (2018)

    Google Scholar 

  44. Wrona, T., Pan, I., Gawthorpe, R.L., Fossen, H.: Seismic facies analysis using machine learning. Geophysics 83(5), O83–O95 (2018)

    Article  Google Scholar 

  45. Wu, X.: Methods to compute salt likelihoods and extract salt boundaries from 3D seismic images. Geophysics 81(6), IM119–IM126 (2016)

    Article  Google Scholar 

  46. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)

    Google Scholar 

  47. Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: 33rd Annual Meeting of the Association for Computational Linguistics (1995)

    Google Scholar 

  48. Yu, Z., et al.: Progressive semisupervised learning of multiple classifiers. IEEE Trans. Cybern. 48(2), 689–702 (2018)

    Article  Google Scholar 

  49. Zeng, Y., Jiang, K., Chen, J.: Automatic seismic salt interpretation with deep convolutional neural networks. arXiv preprint arXiv:1812.01101 (2018)

  50. Zhao, T., Jayaram, V., Roy, A., Marfurt, K.J.: A comparison of classification techniques for seismic facies recognition. Interpretation 3(4), SAE29–SAE58 (2015)

    Article  Google Scholar 

  51. Zhu, X.J.: Semi-supervised learning literature survey. Technical report. University of Wisconsin-Madison Department of Computer Sciences (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. H2O.ai, Minsk, Belarus

    Yauhen Babakhin

  2. Heidelberg Collaboratory for Image Processing, IWR, Heidelberg University, Heidelberg, Germany

    Artsiom Sanakoyeu

  3. Ritsumeikan University, Kyoto, Japan

    Hirotoshi Kitamura

Authors
  1. Yauhen Babakhin
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  2. Artsiom Sanakoyeu
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  3. Hirotoshi Kitamura
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Correspondence to Yauhen Babakhin .

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Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. University of Hamburg, Hamburg, Germany

    Simone Frintrop

  3. University of Münster, Münster, Germany

    Xiaoyi Jiang

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Babakhin, Y., Sanakoyeu, A., Kitamura, H. (2019). Semi-supervised Segmentation of Salt Bodies in Seismic Images Using an Ensemble of Convolutional Neural Networks. In: Fink, G., Frintrop, S., Jiang, X. (eds) Pattern Recognition. DAGM GCPR 2019. Lecture Notes in Computer Science(), vol 11824. Springer, Cham. https://doi.org/10.1007/978-3-030-33676-9_15

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  • DOI: https://doi.org/10.1007/978-3-030-33676-9_15

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  • Online ISBN: 978-3-030-33676-9

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