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Volume 72 Number 1
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Full‐waveform inversion, a popular technique that promises high‐resolution models, has helped in improving the salt definition in inverted velocity models. The success of the inversion relies heavily on having prior knowledge of the salt, and using advanced acquisition technology with long offsets and low frequencies. Salt bodies are often constructed by recursively picking the top and bottom of the salt from seismic images corresponding to tomography models, combined with flooding techniques. The process is time consuming and highly prone to error, especially in picking the bottom of the salt. Many studies suggest performing full‐waveform inversion with long offsets and low frequencies after constructing the salt bodies to correct the misinterpreted boundaries. Here, we focus on detecting the bottom of the salt automatically by utilizing deep learning tools. We specifically generate many random one‐dimensional models, containing or free of salt bodies, and calculate the corresponding shot gathers. We then apply full‐waveform inversion starting with salt flooded versions of those models, and the results of the full‐waveform inversion become inputs to the neural network, whereas the corresponding true one‐dimensional models are the output. The network is trained in a regression manner to detect the bottom of the salt and estimate the subsalt velocity. We analyse three scenarios in creating the training datasets and test their performance on the two‐dimensional BP 2004 salt model. We show that when the network succeeds in estimating the subsalt velocity, the requirement of low frequencies and long offsets are somewhat mitigated. In general, this work allows us to merge the top‐to‐bottom approach with full‐waveform inversion, save the bottom of the salt picking time and empower full‐waveform inversion to converge in the absence of low frequencies and long offsets in the data.

© 2024 European Association of Geoscientists & Engineers. © 2022 European Association of Geoscientists & Engineers.
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/content/journals/10.1111/1365-2478.13193
2023-12-18
2025-05-24

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References

  1. Alali, A., Sun, B. and Alkhalifah, T. (2020) The effectiveness of a pseudo‐inverse extended born operator to handle lateral heterogeneity for imaging and velocity analysis applications. Geophysical Prospecting, 68(4), 1154–1166.
     [Google Scholar]
  2. Alali, A., Sun, B., Kazei, V. and Alkalifah, T. (2020) Salt body flooding using activation functions from machine learning. In: 82nd EAGE Conference and Exhibition 2020. European Association of Geoscientists & Engineers, Vol. 2020, pp. 1–5.
  3. Alkhalifah, T. & Song, C. (2019) An efficient wavefield inversion: Using a modified source function in the wave equation. Geophysics, 84(6), R909–R922.
     [Google Scholar]
  4. Alkhalifah, T., Sun, B.B. and Wu, Z. (2018) Full model wavenumber inversion: identifying sources of information for the elusive middle model wavenumbers. Geophysics, 83(6), R597–R610.
     [Google Scholar]
  5. Ao, R.‐D., Dong, L.‐G. and Chi, B.‐X. (2015) Source‐independent envelope‐based FWI to build an initial model. Chinese Journal of Geophysics, 58(6), 1998–2010.
     [Google Scholar]
  6. Asnaashari, A., Brossier, R., Garambois, S., Audebert, F., Thore, P. and Virieux, J. (2013) Regularized seismic full waveform inversion with prior model information. Geophysics, 78(2), R25–R36.
     [Google Scholar]
  7. Bansal, R., Krebs, J., Routh, P., Lee, S., Anderson, J., Baumstein, A., et al,. (2013) Simultaneous‐source full‐wavefield inversion. The Leading Edge, 32(9), 1100–1108.
     [Google Scholar]
  8. Brenders, A. and Pratt, R.G. (2007) Waveform tomography of marine seismic data: What can limited offset offer? In: 2007 SEG Annual Meeting Expanded Abstracts. SEG, p. 3124.
  9. Bunks, C., Saleck, F.M., Zaleski, S. and Chavent, G. (1995) Multiscale seismic waveform inversion. Geophysics, 60(5), 1457–1473.
     [Google Scholar]
  10. Chi, B., Dong, L. and Liu, Y. (2014) Full waveform inversion method using envelope objective function without low frequency data. Journal of Applied Geophysics, 109, 36–46.
     [Google Scholar]
  11. Choi, Y. and Alkhalifah, T. (2015) Unwrapped phase inversion with an exponential damping. Geophysics, 80(5), R251–R264.
     [Google Scholar]
  12. Datta, D. and Sen, M.K. (2016) Estimating a starting model for full‐waveform inversion using a global optimization method. Geophysics, 81(4), R211–R223.
     [Google Scholar]
  13. Dellinger, J., Brenders, A.J., Sandschaper, J., Regone, C., Etgen, J., et al,. (2017) The Garden Banks model experience. The Leading Edge, 36(2), 151–158.
     [Google Scholar]
  14. Esser, E., Guasch, L., Herrmann, F.J. and Warner, M. (2016) Constrained waveform inversion for automatic salt flooding. The Leading Edge, 35(3), 235–239.
     [Google Scholar]
  15. Gramstad, O. and Nickel, M. (2018) Automated interpretation of top and base salt using deep convolutional networks. In: SEG Technical Program Expanded Abstracts 2018 . Society of Exploration Geophysicists, pp. 1956–1960.
  16. Jones, I.F. and Davison, I. (2014) Seismic imaging in and around salt bodies. Interpretation, 2(4), SL1–SL20.
     [Google Scholar]
  17. Kalita, M., Kazei, V., Choi, Y. and Alkhalifah, T. (2019) Regularized full‐waveform inversion with automated salt flooding. Geophysics, 84(4), R569–R582.
     [Google Scholar]
  18. Kazei, V., Kalita, M. and Alkhalifah, T. (2017) Salt‐body inversion with minimum gradient support and Sobolev space norm regularizations. In: 79th EAGE Conference and Exhibition 2017, EAGE, Vol. 2017, pp. 1–5.
  19. Kazei, V., Ovcharenko, O., Plotnitskii, P., Peter, D., Zhang, X. and Alkhalifah, T. (2021) Mapping full seismic waveforms to vertical velocity profiles by deep learning. Geophysics, 86(5), 1–50.
     [Google Scholar]
  20. Kumar, R., Zhu, H., Vandrasi, V., Dobesh, D. & Vazquez, A. (2019) Updating salt model using FWI on WAZ data in the Perdido area: benefits and challenges. In: SEG International Exposition and Annual Meeting. OnePetro. 1270–1274.
  21. Leveille, J.P., Jones, I.F., Zhou, Z.‐Z., Wang, B. and Liu, F. (2011) Subsalt imaging for exploration, production, and development: A review. Geophysics, 76(5), WB3–WB20.
     [Google Scholar]
  22. Lewis, W. and Vigh, D. (2017) Deep learning prior models from seismic images for full‐waveform inversion. In: SEG Technical Program Expanded Abstracts 2017 . Society of Exploration Geophysicists, pp. 1512–1517.
  23. Loris, I. and Verhoeven, C. (2012) Iterative algorithms for total variation‐like reconstructions in seismic tomography. GEM‐International Journal on Geomathematics, 3(2), 179–208.
     [Google Scholar]
  24. Mulder, W. and Plessix, R.‐E. (2008) Exploring some issues in acoustic full waveform inversion. Geophysical Prospecting, 56(6), 827–841.
     [Google Scholar]
  25. Ovcharenko, O., Kazei, V., Peter, D. and Alkhalifah, T. (2018) Variance‐based model interpolation for improved full‐waveform inversion in the presence of salt bodies. Geophysics, 83(5), R541–R551.
     [Google Scholar]
  26. Plessix, R.‐E., Baeten, G., de Maag, J.W., Klaassen, M., Rujie, Z. and Zhifei, T. (2010) Application of acoustic full waveform inversion to a low‐frequency large‐offset land data set. In: SEG Technical Program Expanded Abstracts 2010 . Society of Exploration Geophysicists, pp. 930–934.
  27. Prieux, V., Brossier, R., Operto, S. and Virieux, J. (2013) Multiparameter full waveform inversion of multicomponent ocean‐bottom‐cable data from the Valhall field. part 1: imaging compressional wave speed, density and attenuation. Geophysical Journal International, 194(3), 1640–1664.
     [Google Scholar]
  28. Richardson, A. (2021) Deepwave. https://doi.org/10.5281/zenodo.3829886
  29. Sen, S., Kainkaryam, S., Ong, C. and Sharma, A. (2020) Saltnet: a production‐scale deep learning pipeline for automated salt model building. The Leading Edge, 39(3), 195–203.
     [Google Scholar]
  30. Shen, X., Ahmed, I., Brenders, A., Dellinger, J., Etgen, J. and Michell, S. (2017) Salt model building at Atlantis with full‐waveform inversion. In: SEG Technical Program Expanded Abstracts 2017 . Society of Exploration Geophysicists, pp. 1507–1511.
  31. Shi, Y., Wu, X. and Fomel, S. (2019) Saltseg: Automatic 3D salt segmentation using a deep convolutional neural network. Interpretation, 7(3), SE113–SE122.
     [Google Scholar]
  32. Sirgue, L. (2006) The importance of low frequency and large offset in waveform inversion. In: 68th EAGE Conference and Exhibition incorporating SPE EUROPEC 2006. European Association of Geoscientists & Engineers, p. cp–2.
  33. Sirgue, L. and Pratt, R.G. (2004) Efficient waveform inversion and imaging: A strategy for selecting temporal frequencies. Geophysics, 69(1), 231–248.
     [Google Scholar]
  34. Song, C., Alkhalifah, T. and Waheed, U.B. (2021) Solving the frequency‐domain acoustic VTI wave equation using physics‐informed neural networks. Geophysical Journal International, 225(2), 846–859.
     [Google Scholar]
  35. Sun, B. and Alkhalifah, T. (2019) The application of an optimal transport to a preconditioned data matching function for robust waveform inversion. Geophysics, 84(6), R923–R945.
     [Google Scholar]
  36. Tarantola, A. (1984) Inversion of seismic reflection data in the acoustic approximation. Geophysics, 49(8), 1259–1266.
     [Google Scholar]
  37. Vigh, D., Jiao, K., Huang, W., Moldoveanu, N. and Kapoor, J. (2013) Long‐offset‐aided full‐waveform inversion. In: 75th EAGE Conference & Exhibition incorporating SPE EUROPEC 2013. European Association of Geoscientists & Engineers, p. cp–348.
  38. Virieux, J. and Operto, S. (2009) An overview of full‐waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1–WCC26.
     [Google Scholar]
  39. Waldeland, A. and Solberg, A. (2017) Salt classification using deep learning. In: 79th EAGE Conference and Exhibition 2017, number 1. European Association of Geoscientists & Engineers, pp. 1–5.
  40. Waldeland, A.U., Jensen, A.C., Gelius, L.‐J. and Solberg, A. H.S. (2018) Convolutional neural networks for automated seismic interpretation. The Leading Edge, 37(7), 529–537.
     [Google Scholar]
  41. Wang, P., Zhang, Z., Mei, J., Lin, F. and Huang, R. (2019) Full‐waveform inversion for salt: a coming of age. The Leading Edge, 38(3), 204–213.
     [Google Scholar]
  42. Warner, M. and Guasch, L. (2016) Adaptive waveform inversion: theory. Geophysics, 81(6), R429–R445.
     [Google Scholar]
  43. Xue, Z., Zhu, H. and Fomel, S. (2017) Full‐waveform inversion using seislet regularization. Geophysics, 82(5), A43–A49.
     [Google Scholar]
  44. Zeng, Y., Jiang, K. and Chen, J. (2019) Automatic seismic salt interpretation with deep convolutional neural networks. In: ICISDM 2019: Proceedings of the 2019 Third International Conference on Information System and Data Mining. ACM Press, pp. 16–20.
     [Google Scholar]
/content/journals/10.1111/1365-2478.13193
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Deep learning unflooding for robust subsalt waveform inversion
Geophysical Prospecting 72, 7 (2023); https://doi.org/10.1111/1365-2478.13193
/content/journals/10.1111/1365-2478.13193
/content/journals/10.1111/1365-2478.13193
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  • Article Type: Research Article
Keyword(s): deep learning; Inversion; salt; unflooding

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