1887
Volume 73, Issue 4
  • E-ISSN: 1365-2478
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Abstract

Abstract

Multiple attenuation is an important step in seismic data processing, leading to improved imaging and interpretation. Radon‐based algorithms are commonly used for discriminating primaries and multiples in common depth point seismic gathers. This process implies a large number of parameters that need to be optimized for a satisfactory result. Moreover, Radon‐based approaches sometimes present challenges in discriminating primaries and multiples with similar moveouts. Deep learning, based on convolutional neural networks, has recently shown promising results in seismic processing tasks that could mitigate the challenges of conventional methods. In this work, we detail how to train convolutional neural networks with only synthetic seismic data for assessing the demultiple problem in field datasets. We compare different training strategies for multiples removal based on different loss functions. We evaluate the performance of the different strategies on 400 clean and noisy synthetic data. We found that training a convolutional neural network to predict the multiples and then subtracting them from the input image is the most effective strategy for demultiple, especially for noisy data. Finally, we test our model to predict multiples on an elastic synthetic dataset and four distinctive field datasets. Our proposed approach reports successful generalization capabilities predicting and eliminating internal and surface‐related multiples before and after migration while mitigating Radon challenges and relieving the user from any manual tasks. As a result, our effectively trained models bring a new valuable tool for seismic demultiple to consider in existing processing workflows.

© 2025 European Association of Geoscientists & Engineers. © 2025 The Author(s). Geophysical Prospecting published by John Wiley & Sons Ltd on behalf of European Association of Geoscientists & Engineers.
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2025-04-17
2026-02-14

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References

  1. Abbasi, S. & Jaiswal, P. (2013) Attenuating long‐period multiples in short‐offset 2D streamer data: Gulf of California. In: SEG technical program expanded abstracts 2013. Houston, TX: Society of Exploration Geophysicists, pp. 4201–4205.
  2. Beylkin, G. (1987) Discrete Radon transform. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35, 162–172.
     [Google Scholar]
  3. Birnie, C., Ravasi, M., Liu, S. & Alkhalifah, T. (2021) The potential of self‐supervised networks for random noise suppression in seismic data. Artificial Intelligence in Geosciences, 2, 47–59.
     [Google Scholar]
  4. Breuer, A., Ettrich, N. & Habelitz, P. (2020) Deep learning in seismic processing: trim statics and demultiple. In: SEG technical program expanded abstracts. Houston, TX: Society of Exploration Geophysicists, pp. 3199–3203.
  5. Bugge, A.J., Evensen, A.K., Lie, J.E. & Nilsen, E.H. (2021) Demonstrating multiple attenuation with model‐driven processing using neural networks. The Leading Edge, 40(11), 831–836.
     [Google Scholar]
  6. Chai, X., Gu, H., Li, F., Duan, H., Hu, X. & Lin, K. (2020) Deep learning for irregularly and regularly missing data reconstruction. Scientific Reports, 10, 3302.
     [Google Scholar]
  7. Chamorro, D., Zhao, J., Birnie, C., Staring, M., Moritz, F. & Ravasi, M. (2023) Deep learning‐based extraction of surface wave dispersion curves from seismic shot gathers. arXiv preprint, arXiv:2305.13990.
  8. Chang, S.‐P., Pubellier, M., Delescluse, M., Qiu, Y., Nirrengarten, M., Mohn, G., Chamot‐Rooke, N. & Liang, Y. (2022) Crustal architecture and evolution of the southwestern south china sea: Implications to continental breakup. Marine and Petroleum Geology, 136, 105450.
     [Google Scholar]
  9. Devlin, J., Chang, M.‐W., Lee, K. & Toutanova, K. (2019) Bert: Pre‐training of deep bidirectional transformers for language understanding. arXiv preprint, arXiv:1810.04805.
  10. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J. & Houlsby, N. (2021) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint, arXiv:2010.11929.
  11. Dragoset, B. (1999) A practical approach to surface multiple attenuation. The Leading Edge, 18, 104–108.
     [Google Scholar]
  12. Durall, R., Ghanim, A., Ettrich, N. & Keuper, J. (2022) Dissecting u‐net for seismic application: An in‐depth study on deep learning multiple removal. arXiv preprint, arXiv: 2206.12112.
  13. Durall, R., Ghanim, A., Fernandez, M.R., Ettrich, N. & Keuper, J. (2023) Deep diffusion models for seismic processing. Computers and Geosciences, 177, 105377.
     [Google Scholar]
  14. Fernandez, M., Durall, R., Ettrich, N., Delescluse, M., Rabaute, A. & Keuper, J. (2022a) A benchmark study of deep learning methods for seismic interpolation. In: 83rd EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, pp. 1–5.
  15. Fernandez, M., Durall, R., Ettrich, N., Delescluse, M., Rabaute, A. & Keuper, J. (2022b) A comparison of deep learning paradigms for seismic data interpolation. In Second EAGE Digitalization Conference and Exhibition. European Association of Geoscientists & Engineers, pp. 1–5.
  16. Fernandez, M., Durall, R., Ettrich, N., Delescluse, M., Rabaute, A. & Keuper, J. (2022c) Image‐to‐image seismic interpolation. In 83rd EAGE Annual Conference and Exhibition Workshop Program. European Association of Geoscientists & Engineers, pp. 1–5.
  17. Fernandez, M., Ettrich, N., Delescluse, M., Rabaute, A. & Keuper, J. (2023a) Deep learning strategies for seismic demultiple. In: Third EAGE Digitalization Conference and Exhibition. European Association of Geoscientists & Engineers, pp. 1–5.
  18. Fernandez, M., Ettrich, N., Delescluse, M., Rabaute, A. & Keuper, J. (2023b) Seismic demultiple with deep learning. In: 84th EAGE Annual Conference and Exhibition. European Association of Geoscientists & Engineers, pp. 1–5.
  19. Fernandez, M., Ettrich, N., Delescluse, M., Rabaute, A. & Keuper, J. (2024) Towards flexible demultiple with deep learning. In Fourth international meeting for applied geoscience & energy expanded abstracts. Houston, TX: Society of Exploration Geophysicists and American Association of Petroleum Geologists.
  20. Foster, D.J. & Mosher, C.C. (1992) Suppression of multiple reflections using the radon transform. Geophysics, 57, 386–395.
     [Google Scholar]
  21. Goodfellow, I., Bengio, Y. & Courville, A. (2016a) Deep learning. Cambridge, MA: MIT Press. http://www.deeplearningbook.org.
     [Google Scholar]
  22. Goodfellow, I., Bengio, Y. & Courville, A. (2016b) Deep learning. Cambridge, MA: MIT Press.
     [Google Scholar]
  23. Goodfellow, I., Pouget‐Abadie, J., Mirza, M., Xu, B., Warde‐Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2014) Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 1–9.
     [Google Scholar]
  24. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, L., Wang, G., Cai, J. & Chen, T. (2017) Recent advances in convolutional neural networks. arXiv preprint, arXiv:1512.07108v6.
  25. Guitton, A. & Verschuur, D. (2004) Adaptive subtraction of multiples using the l1‐norm. Geophysical Prospecting, 52(1), 27–38.
     [Google Scholar]
  26. Guo, R., Maniar, H., Di, H., Moldoveanu, N., Abubakar, A. & Li, M. (2020) Ground roll attenuation with an unsupervised deep learning approach. In: SEG technical program expanded abstracts 2020. Houston, TX: Society of Exploration Geophysicists, pp. 3164–3168.
  27. Hampson, D. (1986) Inverse velocity stacking for multiple elimination. In: 1986 SEG annual meeting, SEG 1986, Houston, TX: Society of Exploration Geophysicists, pp, 422–424.
  28. Harsuko, R. & Alkhalifah, T.A. (2022) Storseismic: a new paradigm in deep learning for seismic processing. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–15.
     [Google Scholar]
  29. Ho, J., Jain, A. & Abbeel, P. (2020) Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840–6851.
     [Google Scholar]
  30. Isola, P., Zhu, J.‐Y., Zhou, T. & Efros, A.A. (2016) Image‐to‐image translation with conditional adversarial networks. arXiv preprint, arXiv:1611.07004.
  31. Kaur, H., Fomel, S. & Pham, N. (2020) Seismic ground‐roll noise attenuation using deep learning. Geophysical Prospecting, 68(7), 2064–2077.
     [Google Scholar]
  32. Kaur, H., Pham, N. & Fomel, S. (2020) Separating primaries and multiples using hyperbolic radon transform with deep learning. In: SEG technical program expanded abstracts 2020. Houston, TX: Society of Exploration Geophysicists, pp. 1496–1500.
  33. Kingma, D.P. & Ba, J. (2017) Adam: a method for stochastic optimization. arXiv preprint, arXiv:1412.6980v9.
  34. Krizhevsky, A., Sutskever, I. & Hinton, G.E. (2012) Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 84–90.
     [Google Scholar]
  35. LeCun, Y., Bengio, Y. & Hinton, G. (2015) Deep learning. Nature, 521(7553), 436–444.
     [Google Scholar]
  36. LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998) Gradient‐based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
     [Google Scholar]
  37. Li, Z. & Gao, H. (2020) Feature extraction based on the convolutional neural network for adaptive multiple subtraction. Marine Geophysical Research, 41, 10.
     [Google Scholar]
  38. Liu, Y., Sangineto, E., Bi, W., Sebe, N., Lepri, B. & Nadai, M.D. (2021) Efficient training of visual transformers with small datasets. arXiv preprint, arXiv:2106.03746v2.
  39. Long, J., Shelhamer, E. & Darrell, T. (2015) Fully convolutional networks for semantic segmentation. arXiv preprint, arXiv:1411.4038v2.
  40. Lu, W. (2013) An accelerated sparse time‐invariant Radon transform in the mixed frequency‐time domain based on iterative 2D model shrinkage. Geophysics, 78, V147–V155.
     [Google Scholar]
  41. Mandelli, S., Borra, F., Lipari, V., Bestagini, P., Sarti, A. & Tubaro, S. (2018) Seismic data interpolation through convolutional autoencoder. In SEG technical program expanded abstracts 2018. Houston, TX: Society of Exploration Geophysicists, pp. 4101–4105.
  42. Mandelli, S., Lipari, V., Bestagini, P. & Tubaro, S. (2019) Interpolation and denoising of seismic data using convolutional neural networks. arXiv preprint, arXiv:1901.07927.
  43. Mousavi, S.M., Beroza, G.C., Mukerji, T. & Rasht‐Behesht, M. (2024) Applications of deep neural networks in exploration seismology: a technical survey. Geophysics, 89(1), WA95‐WA115.
     [Google Scholar]
  44. Oz, Y. (2001) Seismic data analysis. Tulsa, OK: Society of Exploration Geophysicists.
     [Google Scholar]
  45. Qiu, C., Wu, B., Liu, N., Zhu, X. & Ren, H. (2022) Deep learning prior model for unsupervised seismic data random noise attenuation. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
     [Google Scholar]
  46. Qu, S., Verschuur, E., Zhang, D. & Chen, Y. (2021) Training deep networks with only synthetic data: deep‐learning‐based near‐offset reconstruction for (closed‐loop) surface‐related multiple estimation on shallow‐water field data. Geophysics, 86, A39–A43.
     [Google Scholar]
  47. Ronneberger, O., Fischer, P. & Brox, T. (2015) U‐Net: convolutional networks for biomedical image segmentation. In International Conference on medical image computing and computer‐assisted intervention, Berlin: Springer, pp. 234–241.
     [Google Scholar]
  48. Sabbione, J.I. & Sacchi, M.D. (2017) Attenuating multiples with the restricted domain hyperbolic Radon transform. In: 15th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 31 July‐3 August 2017. Rio de Janeiro: Brazilian Geophysical Society, pp. 603–608.
     [Google Scholar]
  49. Sacchi, M.D. & Ulrych, T.J. (1995) High‐resolution velocity gathers and offset space reconstruction. Geophysics, 60, 1169–1177.
     [Google Scholar]
  50. Shirui, W., Wenyi, H., Pengyu, Y., Xuqing, W., Qunshan, Z., Prashanth, N., German, O.B. & Jiefu, C. (2021) Seismic deblending by self‐supervised deep learning with a blind‐trace network. In: First International Meeting for Applied Geoscience & Energy Expanded Abstracts, 40(11), 831–836.
     [Google Scholar]
  51. Shuey, R.T. (1985) A simplification of the Zoeppritz equations. Geophysics, 50(4), 609–614.
     [Google Scholar]
  52. Siahkoohi, A., Verschuur, D.J. & Herrmann, F.J. (2019) Surface‐related multiple elimination with deep learning. In: SEG technical program expanded abstracts 2019. Houston, TX: Society of Exploration Geophysicists, pp. 4629–4634.
  53. Sohn, K., Lee, H. & Yan, X. (2015) Learning structured output representation using deep conditional generative models. Advances in Neural Information Processing Systems, 28, 3483–3491.
     [Google Scholar]
  54. Taner, M. (1980) Long period sea‐floor multiples and their suppression. Geophysical Prospecting, 28, 30–48.
     [Google Scholar]
  55. Thorson, J.R. & Claerbout, J.F. (1985) Velocity‐stack and slant‐stack stochastic inversion. Geophysics, 50, 2727–2741.
     [Google Scholar]
  56. Trad, D., Ulrych, T. & Sacchi, M. (2003) Latest views of the sparse radon transform. Geophysics, 68, 386–399.
     [Google Scholar]
  57. Ulyanov, D., Vedaldi, A. & Lempitsky, V. (2017) Deep image prior. arXiv preprint, arXiv:1711.1092.
  58. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L. & Polosukhin, I. (2023) Attention is all you need. arXiv preprint, arXiv:1706.03762v7.
  59. Verschuur, D. & Berkhout, A. (1997) Estimation of multiple scattering by iterative inversion, part ii: Practical aspects and examples. Geophysics, 62(5), 1596–1611. ISSN: 0016‐8033.
     [Google Scholar]
  60. Verschuur, D. J., Berkhout, A. J. & Wapenaar, C. P. A. (1999) Adaptive surface‐related multiple elimination. Geophysics, 57, 1166–1177.
     [Google Scholar]
  61. Vershuur, D. J. (2013) Seismic multiple removal techniques: past, present and future. Houten, the Netherlands: European Association of Geoscientists & Engineers.
     [Google Scholar]
  62. Wang, W., McMechan, G.A., Ma, J. & Xie, F. (2021) Automatic velocity picking from semblances with a new deep‐learning regression strategy: Comparison with a classification approach. Geophysics, 86(2), U1–U13.
     [Google Scholar]
  63. Wiggins, W. (1998) Attenuation of complex water‐bottom multiples by wave equation prediction and subtraction. Geophysics, 53(12), 1527–1539.
     [Google Scholar]
  64. Wiggins, W. (1999) Multiple attenuation by explicit wave extrapolation to an interpreted horizon. The Leading Edge, 18, 46–54.
     [Google Scholar]
  65. Yang, F. & Ma, J. (2019) Deep‐learning inversion: A next‐generation seismic velocity model building method. Geophysics, 84, R583–R599.
     [Google Scholar]
  66. Zhang, D., de Leeuw, M. & Verschuur, E. (2021) Deep learning‐based seismic surface‐related multiple adaptive subtraction with synthetic primary labels. In First International Meeting for Applied Geoscience & Energy Expanded Abstracts. Houston, TX: Society of Exploration Geophysicists, pp. 2844–2848.
  67. Zhang, M., Liu, Y. & Chen, Y. (2019) Unsupervised seismic random noise attenuation based on deep convolutional neural network. IEEE Access, 7, 179810–179822.
     [Google Scholar]
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Towards deep learning for seismic demultiple
Geophysical Prospecting 73, 1185 (2025); https://doi.org/10.1111/1365-2478.13672
/content/journals/10.1111/1365-2478.13672
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  • Article Type: Research Article
Keyword(s): data processing; deep learning; demultiple; multiples; noise; seismic

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