1887
Volume 72, Issue 9
  • E-ISSN: 1365-2478

Abstract

Abstract

Across various global regions abundant in oil and natural gas reserves, the presence of substantial sub‐surface salt deposits holds significant relevance. Accurate identification of salt domes becomes crucial for enterprises engaged in oil and gas exploration. Our research introduces a precise method for the automatic detection of salt domes, leveraging advanced deep learning architectures such as U‐net, transformers, artificial intelligence generative models and liquid state machines. In comparison with state‐of‐the‐art techniques, our model demonstrates superior performance, achieving a stable and validated intersection over the union metric, indicating high accuracy and robustness. Furthermore, the Dice similarity coefficient attaining underscores the model's proficiency in closely aligning with ground truth across diverse scenarios. This evaluation, conducted on 1000 seismic images, reveals that our proposed architecture is not only comparable but often surpasses existing segmentation models in effectiveness and reliability.

© 2024 European Association of Geoscientists & Engineers. © 2024 European Association of Geoscientists & Engineers.
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2024-10-11
2026-02-19

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References

  1. Amin, A. & Deriche, M. (2016) Salt‐dome detection using a codebook‐based learning model. IEEE Geoscience and Remote Sensing Letters, 13(11), 1636–1640. https://doi.org/10.1109/LGRS.2016.2599435
     [Google Scholar]
  2. 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. Cham: Springer International Publishing, pp. 218–231. https://doi.org/10.1007/978‐3‐030‐33676‐9_15
  3. Belenitskaya, G.A. (2016) Salt tectonics at the margins of young oceans. Geotectonics, 50(1), 244–256. https://doi.org/10.1134/S0016852116030031
     [Google Scholar]
  4. Bidollahkhani, M., Atasoy, F. & Abdellatef, H. (2023) LTC‐SE: Expanding the potential of liquid time‐constant neural networks for scalable AI and embedded systems. Preprint. https://doi.org/10.48550/arXiv.2304.08691.
  5. Bridi Guazzelli, A., Roisenberg, M. & Rodrigues, B.B. (2020) Efficient 3D semantic segmentation of seismic images using orthogonal planes 2D convolutional neural networks. In: 2020 International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE, pp. 1–8. https://doi.org/10.1109/ijcnn48605.2020.9206661
  6. Celecia, A., Figueiredo, K., Rodriguez, C., Vellasco, M., Maldonado, E., Silva, M.A. et al. (2021) Unsupervised machine learning applied to seismic interpretation: Towards an unsupervised automated interpretation tool. Sensors, 21(19), 6347. https://doi.org/10.3390/s21196347. https://www.mdpi.com/1424‐8220/21/19/6347
     [Google Scholar]
  7. Chen, Z., Xu, C., Lv, H., Liu, S. & Ji, Y. (2023) Understanding and improving adversarial transferability of vision transformers and convolutional neural networks. Information Sciences, 648, 119474. https://doi.org/10.1016/j.ins.2023.119474
     [Google Scholar]
  8. Cruz, P.M. & Navarro, J.P. (2018) Salt segmentation with fully convolutional networks and transfer learning. In First EAGE Workshop on High Performance Computing for Upstream in Latin America, September 2018. Houten, the Netherlands: EAGE Publications, pp. 1–5. https://doi.org/10.3997/2214‐4609.201803083
  9. Howard, A., Sharma, A., Lenamond, A., Compu Ter, C., Adamck, J., McDonald, M., Kainkaryam, S. et al. (2018) TGS Salt Identification Challenge. Kaggle. https://www.kaggle.com/competitions/tgs-salt-identification-challenge
  10. Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y. et al. (2020) Unet 3+: A full‐scale connected U‐net for medical image segmentation. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway, NJ: IEEE, 1055–1059. https://api.semanticscholar.org/CorpusID:215828394
  11. Jiayi, C. & Wei, S. (2022) GAN‐VAE: Elevate generative ineffective image through variational autoencoder. In: 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). Piscataway, NJ: IEEE, pp. 765–770. https://doi.org/10.1109/PRAI55851.2022.9904067
  12. Jones, I.F. & Davison, I. (2014) Seismic imaging in and around salt bodies. Interpretation, 2(4), SL1–SL20. https://doi.org/10.1190/INT‐2014‐0033.1
     [Google Scholar]
  13. Karchevskiy, M., Ashrapov, I. & Kozinkin, L. (2018) Automatic salt deposits segmentation: A deep learning approach. Preprint. arXiv.1812.01429. https://api.semanticscholar.org/CorpusID:54446213.
  14. Karmenova, M., Tlebaldinova, A., Krak, I., Denissova, N., Popova, G., Zhantassova, Z. et al. (2022) An approach for clustering of seismic events using unsupervised machine learning. Acta Polytechnica Hungarica, 19(5), 7–22. https://doi.org/10.12700/aph.19.5.2022.5.1
     [Google Scholar]
  15. Kingma, D.P. & Welling, M. (2019) An introduction to variational autoencoders. Foundations and Trends in Machine Learning, 12(4), 307–392. https://www.nowpublishers.com/article/Details/MAL-056
     [Google Scholar]
  16. Lawal, A., Mayyala, Q., Zerguine, A. & Beghdadi, A. (2021) Salt dome detection using context‐aware saliency. In: 2020 28th European Signal Processing Conference (EUSIPCO). Piscataway, NJ: IEEE, pp. 1906–1910. https://doi.org/10.23919/Eusipco47968.2020.9287538
  17. Li, K., Song, J., Xia, S., Luo, B., Wang, J., Zhong, Y. et al. (2023) Salt structure identification based on U‐net model with target flip, multiple distillation and self‐distillation methods. Frontiers in Earth Sciences, 10, 1071637. https://doi.org/10.3389/feart.2022.1071637
     [Google Scholar]
  18. Liu, W., Li, Y., Liu, Q., Wang, X., Meng, Q., Zhang, D. et al. (2023) Roles of gypsum/salt‐bearing sequence in hydrocarbon accumulation and storage. Energy Geoscience, 4(1), 93–102. https://doi.org/10.1016/j.engeos.2022.08.005
     [Google Scholar]
  19. Maass, W. (2011) Liquid state machines: motivation, theory, and applications. In: Cooper, S.B. (Ed.) Computability in context: Computation and logic in the real world. 275–296.
     [Google Scholar]
  20. Milosavljević, A. (2020) Identification of salt deposits on seismic images using deep learning method for semantic segmentation. ISPRS International Journal of Geo‐Information, 9(1), 24. https://www.mdpi.com/2220‐9964/9/1/24
     [Google Scholar]
  21. Pinheiro Cinelli, L., Araújo Marins, M., Barros da Silva, E.A. & Lima Netto, S. (2021) Variational autoencoder. In: Variational methods for machine learning with applications to deep networks. Cham: Springer International Publishing, pp. 111–149. https://doi.org/10.1007/978‐3‐030‐70679‐1_5
  22. Qureshi, N.M.F., Abid, B., Khan, B.M. & Memon, R.A. (2022) Seismic facies segmentation using ensemble of convolutional neural networks. Wireless Communications and Mobile Computing, 2022.
  23. Raina, V., Molchanova, N., Graziani, M., Malinin, A., Muller, H., Cuadra, M.B. et al. (2023) Tackling bias in the Dice similarity coefficient: introducing NDSC for white matter lesion segmentation. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). Piscataway, NJ: IEEE, 1–5. https://api.semanticscholar.org/CorpusID:256808237
  24. Ronneberger, O., Fischer, P., & Brox, T. (2015) U‐Net: Convolutional networks for biomedical image segmentation. Preprint. https://doi.org/10.48550/arXiv.1505.04597
  25. Ronneberger, O. (2017) Invited talk: U‐Net convolutional networks for biomedical image segmentation, (pp. 3–3). In: Maier‐Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (Eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Berlin: Springer, p. 3. https://doi.org/10.1007/978‐3‐662‐54345‐0_3
  26. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N. et al. (2017) Attention is all youneed. Preprint. https://arxiv.org/abs/1706.03762
  27. Wang, H., Xie, S., Lin, L., Iwamoto, Y., Han, X.‐H., Chen, Y.‐W. et al. (2022) Mixed transformer U‐Net for medical image segmentation. In: ICASSP 2022‐2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway, NJ: IEEE, pp. 2390–2394. https://doi.org/10.1109/ICASSP43922.2022.9746172
  28. Wang, Q., Jin, Y. & Li, P. (2015) General‐purpose LSM learning processor architecture and theoretically guided design space exploration. In 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS). Piscataway, NJ: IEEE, pp. 1–4. https://doi.org/10.1109/BioCAS.2015.7348397
  29. Wang, Z., Di, H., Shafiq, M.A., Alaudah, Y. & AlRegib, G. (2018) Successful leveraging of image processing and machine learning in seismic structural interpretation: a review. The Leading Edge, 37, 451–461. https://doi.org/10.1190/tle37060451.1
     [Google Scholar]
  30. Zhou, X., Tyagi, M. & Sharma, J. (2020) Enhanced automatic segmentation of salt bodies from seismic images using wavelet convolutional neural networks. In: EAGE 2020 Annual Conference AMP; Exhibition Online. Houten, the Netherlands: European Association of Geoscientists & Engineers, pp. 1–5. https://doi.org/10.3997/2214‐4609.202011987
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Accurate identification of salt domes using deep learning techniques: Transformers, generative artificial intelligence and liquid state machines
Geophysical Prospecting 72, 3280 (2024); https://doi.org/10.1111/1365-2478.13603
/content/journals/10.1111/1365-2478.13603
/content/journals/10.1111/1365-2478.13603
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  • Article Type: Research Article
Keyword(s): artificial intelligence generative; deep learning; liquid state machines; salt domes identification; seismic images; transformers; U‐net

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