Skip to main content
SpringerLink
Log in

Fault Detection in Seismic Data Using Graph Attention Network

  • Conference paper
  • First Online:
Book cover Database and Expert Systems Applications (DEXA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13427))

Included in the following conference series:

Abstract

This paper presents a natural attention-based approach for automated fault detection in seismic data. Fault analysis in seismic data is important for drilling and exploration in the oil and natural gas industries. The seismic fault is a perceptual phenomenon, and manual fault detection is still practiced in various industries. The convolutional neural network (CNN) is the most commonly used method in the newly conducted research for automated fault detection. However, our paper uses a graph attention network (GAT) based approach. We first extract 2D patches centered around the points of concern. Next, we present these extracted patches in the graph domain using the k-nearest neighbor graph. The graph representation of patches is connectional in the graph domain based on seismic amplitude similarity. Then, we apply GAT to classify the faults. Both the training and testing sets contain both synthetic and real data. The proposed methodology gives good accuracy when applied to field data.

We are grateful to the Oil and Natural Gas Corporation (ONGC), India, for supporting the work and providing real seismic data used in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abu-El-Haija, S., Kapoor, A., Perozzi, B., Lee, J.: N-GCN: multi-scale graph convolution for semi-supervised node classification. CoRR arXiv:abs/1802.08888 (2018). http://arxiv.org/abs/1802.08888

  2. Admasu, F., Back, S., Toennies, K.: Autotracking of faults on 3D seismic data. Geophysics 71, A49–A53 (2006). https://doi.org/10.1190/1.2358399

    Article  Google Scholar 

  3. AlBinHassan, N.M., Marfurt, K.: Fault detection using hough transforms. In: SEG Technical Program Expanded Abstracts, pp. 1719–1721 (2005). https://doi.org/10.1190/1.1817639

  4. Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 29, pp. 2001–2009. Curran Associates, Inc. (2016). https://doi.org/10.5555/3157096.3157320

  5. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006). JMLR:v7:belkin06a

    Google Scholar 

  6. Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine reading. CoRR abs/1601.06733 (2016). http://arxiv.org/abs/1601.06733

  7. Cunha, A., Pochet, A., Lopes, H., Gattass, M.: Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data. Comput. Geosci. 135, 104344 (2020). https://doi.org/10.1016/j.cageo.2019.104344

    Article  Google Scholar 

  8. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS 2016, pp. 3844–3852. Curran Associates Inc. (2016). https://doi.org/10.5555/3157382.3157527

  9. Di, H., Wang, Z., AlRegib, G.: Seismic fault detection from post-stack amplitude by convolutional neural networks. In: 80th EAGE Conference and Exhibition, pp. 1–5 (2018). https://doi.org/10.3997/2214-4609.201800733

  10. Di, H., Shafiq, A., Alregib, G.: Seismic-fault detection based on multiattribute support vector machine analysis. In: SEG Technical Program Expanded Abstracts, pp. 2039–2044 (2017). https://doi.org/10.1190/segam2017-17748277.1

  11. Duvenaud, D.K., et al.: Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing Systems, vol. 28, pp. 2224–2232. Curran Associates, Inc. (2015). https://doi.org/10.5555/2969442.2969488

  12. Gibson, D., Spann, M., Turner, J., Wright, T.: Fault surface detection in 3-D seismic data. IEEE Trans. Geosci. Remote Sens. 43, 2094–2102 (2005). https://doi.org/10.1109/TGRS.2005.852769

    Article  Google Scholar 

  13. Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 855–864. Association for Computing Machinery (2016). https://doi.org/10.1145/2939672.2939754

  14. Hong, D., Gao, L., Yao, J., Zhang, B., Plaza, A., Chanussot, J.: Graph convolutional networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59, 1–13 (2020). https://doi.org/10.1109/TGRS.2020.3015157

    Article  Google Scholar 

  15. Huang, L., Dong, X., Clee, T.E.: A scalable deep learning platform for identifying geologic features from seismic attributes. Lead. Edge 36(3), 249–256 (2017). https://doi.org/10.1190/tle36030249.1

    Article  Google Scholar 

  16. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR arXiv:abs/1609.02907 (2016). http://arxiv.org/abs/1609.02907

  17. Lin, Z., et al.: A structured self-attentive sentence embedding. CoRR abs/1703.03130 (2017). http://arxiv.org/abs/1703.03130

  18. Mahadik, R., Routray, A.: Fault detection and optimization in seismic dataset using multiscale fusion of a geometric attribute. In: IECON 2019–45th Annual Conference of the IEEE Industrial Electronics Society, vol. 1, pp. 107–112 (2019). https://doi.org/10.1109/IECON.2019.8927569

  19. Martineau, M., Raveaux, R., Conte, D., Venturini, G.: Graph matching as a graph convolution operator for graph neural networks. Pattern Recogn. Lett. 149, 59–66 (2021). https://doi.org/10.1016/j.patrec.2021.06.008

    Article  Google Scholar 

  20. Palo, P., Routray, A., Mahadik, R., Singh, S., Tandon, R., Bahuguna, C.S.: Fault interpretation using neural networks. In: 13th Biennial International Conference and Exhibition. SPG, February 2020. https://www.spgindia.org/Kochi2020-expanded-abstracts/id-389-fault-interpretation-using-neural-networks.pdf

  21. Palo, P., Routray, A., Singh, S.: Seismic fault analysis using graph signal regularization. In: 2021 29th European Signal Processing Conference (EUSIPCO), pp. 1835–1839 (2021, forthcoming)

    Google Scholar 

  22. Pedersen, S.I., Randen, T., Sonneland, L., Steen, Ø.: Automatic fault extraction using artificial ants. In: SEG Technical Program Expanded Abstracts, pp. 512–515 (2005). https://doi.org/10.1190/1.1817297

  23. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 701–710. Association for Computing Machinery (2014). https://doi.org/10.1145/2623330.2623732

  24. Perraudin, N., et al.: GSPBOX: a toolbox for signal processing on graphs. arXiv e-prints, August 2014

    Google Scholar 

  25. Pochet, A., Diniz, P.H.B., Lopes, H., Gattass, M.: Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps. IEEE Geosci. Remote Sens. Lett. 16(3), 352–356 (2019). https://doi.org/10.1109/LGRS.2018.2875836

    Article  Google Scholar 

  26. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)

    Google Scholar 

  27. Wang, Z., AlRegib, G.: Fault detection in seismic datasets using hough transform. In: IEEE International Conference on Acoustic, Speech and Signal Processing, pp. 2372–2376, May 2014. https://doi.org/10.1109/ICASSP.2014.6854024

  28. Weston, J., Ratle, F., Collobert, R.: Deep learning via semi-supervised embedding. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 1168–1175. Association for Computing Machinery (2008). https://doi.org/10.1145/1390156.1390303

  29. Wu, X., Shi, Y., Fomel, S., Liang, L., Zhang, Q., Yusifov, A.Z.: FaultNet3D: predicting fault probabilities, strikes, and dips with a single convolutional neural network. IEEE Trans. Geosci. Remote Sens. 57(11), 9138–9155 (2019). https://doi.org/10.1109/TGRS.2019.2925003

    Article  Google Scholar 

  30. Wu, X., Liang, L., Shi, Y., Fomel, S.: FaultSeg3D: using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics 84, IM35–IM45 (2019). https://doi.org/10.1190/geo2018-0646.1

  31. Xiong, W., et al.: Seismic fault detection with convolutional neural network. Geophysics 83(5), O97–O103 (2018). https://doi.org/10.1190/geo2017-0666.1

    Article  Google Scholar 

  32. Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 7370–7377 (2019). https://doi.org/10.1609/aaai.v33i01.33017370

  33. Zheng, Y., Zhang, Q., Yusifov, A., Shi, Y.: Applications of supervised deep learning for seismic interpretation and inversion. Lead. Edge 38(7), 526–533 (2019). https://doi.org/10.1190/tle38070526.1

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, Kharagpur, 721302, West Bengal, India

    Patitapaban Palo & Aurobinda Routray

  2. Oil and Natural Gas Corporation Ltd., Dehradun, 248003, Uttarakhand, India

    Sanjai Kumar Singh

Authors
  1. Patitapaban Palo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aurobinda Routray
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanjai Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patitapaban Palo .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Palo, P., Routray, A., Singh, S.K. (2022). Fault Detection in Seismic Data Using Graph Attention Network. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13427. Springer, Cham. https://doi.org/10.1007/978-3-031-12426-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-12426-6_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12425-9

  • Online ISBN: 978-3-031-12426-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation