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Abstract

Summary

Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise, and loss of signal information at the receivers that leads to incomplete traces. In this work, we employ a generative solution, since it can explicitly model complex data distributions and hence, yield to a better decision-making process. In particular, we introduce diffusion models for multiple removal. To that end, we run experiments on synthetic and on real data, and we compare the deep diffusion performance with standard algorithms. We believe that our pioneer study not only demonstrates the capability of diffusion models, but also opens the door to future research to integrate generative models in seismic workflows.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202310387
2023-06-05
2025-06-13

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References

  1. Breuer, Alexander; Ettrich, Norman; Habelitz, Peter (2020): Deep learning in seismic processing: Trim statics and demultiple. In : SEG Technical Program Expanded Abstracts 2020: Society of Exploration Geophysicists, pp. 3199–3203.
     [Google Scholar]
  2. Durall, Ricard; Ghanim, Ammar; Ettrich, Norman; Keuper, Janis (2022a): Dissecting U-net for Seismic Application: An In-Depth Study on Deep Learning Multiple Removal. In arXiv preprint arXiv:2206.12112.
     [Google Scholar]
  3. Durall, Ricard; Ghanim, Ammar; Fernandez, Mario; Ettrich, Norman; Keuper, Janis (2022b): Deep Diffusion Models for Seismic Processing. In arXiv preprint arXiv:2207.10451.
     [Google Scholar]
  4. Durall, Ricard; Tschannen, Valentin; Pfreundt, Franz-Josef; Keuper, Janis (2020): Synthesizing seismic diffractions using a generative adversarial network. In : SEG Technical Program Expanded Abstracts 2020: Society of Exploration Geophysicists, pp. 1491–1495.
     [Google Scholar]
  5. Hampson, Dan (1986): Inverse velocity stacking for multiple elimination. In : SEG Technical Program Expanded Abstracts 1986: Society of Exploration Geophysicists, pp. 422–424.
     [Google Scholar]
  6. Ho, Jonathan; Jain, Ajay; Abbeel, Pieter (2020): Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems33, pp. 6840–6851.
     [Google Scholar]
  7. Saharia, Chitwan; Ho, Jonathan; Chan, William; Salimans, Tim; Fleet, David J.; Norouzi, Mohammad (2021): Image super-resolution via iterative refinement. In arXiv preprint arXiv:2104.07636.
     [Google Scholar]
  8. Sohl-Dickstein, Jascha; Weiss, Eric; Maheswaranathan, Niru; Ganguli, Surya (2015): Deep unsupervised learning using nonequilibrium thermodynamics. In : International Conference on Machine Learning. PMLR, pp. 2256–2265.
     [Google Scholar]
/content/papers/10.3997/2214-4609.202310387
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Deep Diffusion Models for Multiple Removal
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202310387
/content/papers/10.3997/2214-4609.202310387
/content/papers/10.3997/2214-4609.202310387
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