1887
Volume 72 Number 1
  • E-ISSN: 1365-2478

Abstract

Abstract

Seismic velocity plays an important role in imaging and identifying underground geology. Conventional seismic velocity inversion methods, like full waveform inversion, directly update the velocity model based on the misfit between the observed and synthetic data. However, seismic velocity inversion is a highly nonlinear process, and the inversion effect greatly relies on the initial inversion model. In this paper, we propose a novel network‐domain full waveform inversion method. Different from the existing network‐domain full waveform inversion methods, which use random or fixed numbers as network input, we reparameterize the low‐dimensional acoustic velocity model in a high‐dimensional inversion network parameter domain with seismic observed data as the network input. In this way, the physical information within the observed data can be directly encoded into the inversion parameters, leading to a better inversion effect than the current network‐domain full waveform inversion method. Moreover, comparison experiments on the Society of Exploration Geophysicists and the European Association of Geoscientists and Engineers Overthrust model and the Marmousi model show the advantages of the proposed method over conventional full waveform inversion from the aspects of inversion accuracy, robustness to noisy data, and more complex geological structures. These advantages may benefit from the fact that reparameterization within the inversion network domain can empower the inversion process with the regularization ability of denoising and mitigating the cycle‐skipping issue. In the end, the potential of the proposed method in terms of network initialization is further discussed.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.13292
2023-12-18
2025-04-30
Loading full text...

Full text loading...

References

  1. Adler, A., Araya‐Polo, M. & Poggio, T. (2019) Deep recurrent architectures for seismic tomography. In: 81st EAGE conference and exhibition, volume 2019. EAGE, Extended Abstracts. Houten, the Netherlands: European Association of Geoscientists & Engineers, pp. 1–5.
  2. Adler, A., Araya‐Polo, M. & Poggio, T. (2021) Deep learning for seismic inverse problems: toward the acceleration of geophysical analysis workflows. IEEE Signal Processing Magazine, 38(2), 89–119.
    [Google Scholar]
  3. Alfarraj, M. & AlRegib, G. (2019a) Semi‐supervised learning for acoustic impedance inversion. In: 89th Annual international meeting. SEG, Extended Abstracts. Houston, TX: Society of Exploration Geophysicists, pp. 2298–2302.
    [Google Scholar]
  4. Alfarraj, M. & AlRegib, G. (2019b) Semisupervised sequence modeling for elastic impedance inversion. Interpretation, 7(3), SE237–SE249.
    [Google Scholar]
  5. Allen‐Zhu, Z., Li, Y. & Song, Z. (2019) A convergence theory for deep learning via over‐parameterization. In: International conference on machine learning. Proceedings of Machine Learning Research, Vol. 97. Cambridge, MA: MIT Press, pp. 242–252.
    [Google Scholar]
  6. Aragao, O. & Sava, P. (2020) Elastic full wavefield inversion with probabilistic petrophysical clustering. Geophysical Prospecting, 68(4), 1341–1355.
    [Google Scholar]
  7. Araya‐Polo, M., Adler, A., Farris, S. & Jennings, J. (2020) Fast and accurate seismic tomography via deep learning. In Deep learning: Algorithms and applications (pp. 129–156). Berlin: Springer.
    [Google Scholar]
  8. Araya‐Polo, M., Farris, S. & Florez, M. (2019) Deep learning‐driven velocity model building workflow. The Leading Edge, 38(11), 872a1–872a9.
    [Google Scholar]
  9. Araya‐Polo, M., Jennings, J., Adler, A. & Dahlke, T. (2018) Deep‐learning tomography. The Leading Edge, 37(1), 58–66.
    [Google Scholar]
  10. Biswas, R., Sen, M.K., Das, V. & Mukerji, T. (2019) Prestack and poststack inversion using a physics‐guided convolutional neural network. Interpretation, 7(3), SE161–SE174.
    [Google Scholar]
  11. Choromanska, A., Henaff, M., Mathieu, M., Arous, G.B. & Lecun, Y. (2014) The loss surfaces of multilayer networks. Eprint Arxiv, 192–204.
    [Google Scholar]
  12. Collobert, R. & Weston, J. (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning New York: Association for Computing Machinery, pp. 160–167.
  13. Dauphin, Y.N., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S. & Bengio, Y. (2014) Identifying and attacking the saddle point problem in high‐dimensional non‐convex optimization. In: Advances in neural information processing systems, 27. Cambridge, MA: MIT Press, pp. 2933–2941.
  14. Di, H., Li, Z., Maniar, H. & Abubakar, A. (2020) Seismic stratigraphy interpretation by deep convolutional neural networks: a semisupervised workflow. Geophysics, 85(4), WA77–WA86.
    [Google Scholar]
  15. Di, H., Wang, Z. & AlRegib, G. (2018) Deep convolutional neural networks for seismic salt‐body delineation. In: AAPG annual convention and exhibition. https://doi.org/10.1306/70630Di2018
  16. Du, S., Lee, J., Li, H., Wang, L. & Zhai, X. (2019) Gradient descent finds global minima of deep neural networks. In: International conference on machine learning. Proceedings of Machine Learning Research, volume 97. Cambridge, MA: MIT Press, pp. 1675–1685.
    [Google Scholar]
  17. Esser, E., Guasch, L., van Leeuwen, T., Aravkin, A.Y. & Herrmann, F.J. (2018) Total variation regularization strategies in full‐waveform inversion. SIAM Journal on Imaging Sciences, 11(1), 376–406.
    [Google Scholar]
  18. Fabien‐Ouellet, G. & Sarkar, R. (2020) Seismic velocity estimation: a deep recurrent neural‐network approach. Geophysics, 85(1), U21–U29.
    [Google Scholar]
  19. Feng, S., Lin, Y. & Wohlberg, B. (2022) Multiscale data‐driven seismic full‐waveform inversion with field data study. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14.
    [Google Scholar]
  20. Guitton, A. (2012) Blocky regularization schemes for full‐waveform inversion. Geophysical Prospecting, 60(5), 870–884.
    [Google Scholar]
  21. He, Q. & Wang, Y. (2021) Reparameterized full‐waveform inversion using deep neural networks. Geophysics, 86(1), V1–V13.
    [Google Scholar]
  22. Huang, Y. & Schuster, G.T. (2018) Full‐waveform inversion with multisource frequency selection of marine streamer data. Geophysical Prospecting, 66(7), 1243–1257.
    [Google Scholar]
  23. Jin, P., Zhang, X., Chen, Y., Huang, S.X., Liu, Z. & Lin, Y. (2021) Unsupervised learning of full‐waveform inversion: connecting CNN and partial differential equation in a loop [Preprint]. arXiv:2110.07584.
  24. Kaur, H., Pham, N. & Fomel, S. (2021) Seismic data interpolation using deep learning with generative adversarial networks. Geophysical Prospecting, 69(2), 307–326.
    [Google Scholar]
  25. Kawaguchi, K. (2016) Deep learning without poor local minima. In: Advances in neural information processing systems, 29. Red Hook, NY: Curran Associates, pp. 586–594.
    [Google Scholar]
  26. Kazei, V., Ovcharenko, O., Plotnitskii, P., Peter, D., Zhang, X. & Alkhalifah, T. (2021) Mapping full seismic waveforms to vertical velocity profiles by deep learning. Geophysics, 86(5), R711–R721.
    [Google Scholar]
  27. Kendall, A. & Gal, Y. (2017) What uncertainties do we need in bayesian deep learning for computer vision? In: Advances in neural information processing systems. Red Hook, NY: Curran Associates, pp. 5574–5584.
  28. Kingma, D.P. & Ba, J. (2014) Adam: A method for stochastic optimization [Preprint]. arXiv:1412.6980.
  29. Kong, F., Picetti, F., Lipari, V., Bestagini, P., Tang, X. & Tubaro, S. (2022) Deep prior‐based unsupervised reconstruction of irregularly sampled seismic data. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
    [Google Scholar]
  30. LeCun, Y., Bengio, Y. & Hinton, G. (2015) Deep learning. Nature, 521(7553), 436–444.
    [Google Scholar]
  31. Leshno, M., Lin, V.Y., Pinkus, A. & Schocken, S. (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Networks, 6(6), 861–867.
    [Google Scholar]
  32. Li, S., Liu, B., Ren, Y., Chen, Y., Yang, S., Wang, Y. & Jiang, P. (2020) Deep‐learning inversion of seismic data. IEEE Transactions on Geoscience and Remote Sensing, 58(3), 2135–2149.
    [Google Scholar]
  33. Li, Y. & Alkhalifah, T. (2021) Extended full waveform inversion with matching filter. Geophysical Prospecting, 69(7), 1441–1454.
    [Google Scholar]
  34. Liu, B., Yue, J., Zuo, Z., Xu, X., Fu, C., Yang, S. & Jiang, P. (2022) Unsupervised deep learning for random noise attenuation of seismic data. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
    [Google Scholar]
  35. Liu, Q., Fu, L. & Zhang, M. (2021) Deep‐seismic‐prior‐based reconstruction of seismic data using convolutional neural networks. Geophysics, 86(2), V131–V142.
    [Google Scholar]
  36. Liu, X., Li, B., Li, J., Chen, X., Li, Q. & Chen, Y. (2021) Semi‐supervised deep autoencoder for seismic facies classification. Geophysical Prospecting, 69(6), 1295–1315.
    [Google Scholar]
  37. Park, M.J. & Sacchi, M.D. (2020) Automatic velocity analysis using convolutional neural network and transfer learning. Geophysics, 85(1), V33–V43.
    [Google Scholar]
  38. Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L. & Lerer, A. (2017) Automatic differentiation in PyTorch. In: Proceedings of the 31st International conference on neural information processing systems. Red Hook, NY: Curran Associates, pp. 1–4.
  39. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) PyTorch: an imperative style, high‐performance deep learning library. In Advances in neural information processing systems. Red Hook, NY: Curran Associates, pp. 8024–8035.
    [Google Scholar]
  40. Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., Bengio, Y. & Courville, A. (2019) On the spectral bias of neural networks. In International conference on machine learning. Proceedings of Machine Learning Research, volume 97. Cambridge, MA: MIT Press, pp. 5301–5310.
    [Google Scholar]
  41. Ren, Y., Nie, L., Yang, S., Jiang, P. & Chen, Y. (2021) Building complex seismic velocity models for deep learning inversion. IEEE Access, 9, 63767–63778.
    [Google Scholar]
  42. Ren, Y., Xu, X., Yang, S., Nie, L. & Chen, Y. (2020) A physics‐based neural‐network way to perform seismic full waveform inversion. IEEE Access, 8, 112266–112277.
    [Google Scholar]
  43. Rudin, L.I., Osher, S. & Fatemi, E. (1992) Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60(1–4), 259–268.
    [Google Scholar]
  44. Saraiva, M., Forechi, A., De Oliveira Neto, J., DelRey, A. & Rauber, T. (2021) Data‐driven full‐waveform inversion surrogate using conditional generative adversarial networks. In: 2021 International joint conference on neural networks (IJCNN). Piscataway, NJ: IEEE, pp. 1–8.
  45. Sun, B. & 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]
  46. Sun, B. & Alkhalifah, T. (2020a) Ml‐descent: an optimization algorithm for full‐waveform inversion using machine learning. Geophysics, 85(6), R477–R492.
    [Google Scholar]
  47. Sun, B. & Alkhalifah, T. (2020b) ML‐misfit: learning a robust misfit function for full‐waveform inversion using machine learning. [Preprint] arXiv:2002.03163.
  48. Sun, J., Innanen, K.A. & Huang, C. (2021) Physics‐guided deep learning for seismic inversion with hybrid training and uncertainty analysis. Geophysics, 86(3), R303–R317.
    [Google Scholar]
  49. Sun, J., Niu, Z., Innanen, K.A., Li, J. & Trad, D.O. (2020) A theory‐guided deep‐learning formulation and optimization of seismic waveform inversion. Geophysics, 85(2), R87–R99.
    [Google Scholar]
  50. Symes, W.W. (2008) Migration velocity analysis and waveform inversion. Geophysical Prospecting, 56(6), 765–790.
    [Google Scholar]
  51. Ulyanov, D., Vedaldi, A. & Lempitsky, V. (2018) Deep image prior. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway, NJ: IEEE, pp. 9446–9454.
    [Google Scholar]
  52. Virieux, J. & Operto, S. (2009) An overview of full‐waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1–WCC26.
    [Google Scholar]
  53. Voulodimos, A., Doulamis, N., Doulamis, A. & Protopapadakis, E. (2018) Deep learning for computer vision: a brief review. Computational Intelligence and Neuroscience, 2018, 7068349.
    [Google Scholar]
  54. Waheed, U.b., Alkhalifah, T., Haghighat, E., Song, C. & Virieux, J. (2021) PINNtomo: seismic tomography using physics‐informed neural networks [Preprint]. arXiv:2104.01588.
  55. Waheed, U.b., Haghighat, E., Alkhalifah, T., Song, C. & Hao, Q. (2021) PINNeik: eikonal solution using physics‐informed neural networks. Computers & Geosciences, 155, 104833.
    [Google Scholar]
  56. Wang, Y., Wang, Q., Lu, W., Ge, Q. & Yan, X. (2022) Seismic impedance inversion based on cycle‐consistent generative adversarial network. Petroleum Science, 19(1), 147–161.
    [Google Scholar]
  57. Wang, Y., Wang, Q., Lu, W. & Li, H. (2022) Physics‐constrained seismic impedance inversion based on deep learning. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
    [Google Scholar]
  58. Wu, Y., Lin, Y. & Zhou, Z. (2018) Inversionnet: accurate and efficient seismic waveform inversion with convolutional neural networks. In: 88th annual international meeting. SEG, Expanded Abstracts. Houston, TX: Society of Exploration Geophysicists, pp. 2096–2100.
    [Google Scholar]
  59. Wu, Y. & McMechan, G.A. (2019) Parametric convolutional neural network‐domain full‐waveform inversion. Geophysics, 84(6), R881–R896.
    [Google Scholar]
  60. Xue, Z., Zhu, H. & Fomel, S. (2017) Full‐waveform inversion using seislet regularization. Geophysics, 82(5), A43–A49.
    [Google Scholar]
  61. Yin, W., Kann, K., Yu, M. & Schütze, H. (2017) Comparative study of CNN and RNN for natural language processing [Preprint]. arXiv:1702.01923.
  62. Young, T., Hazarika, D., Poria, S. & Cambria, E. (2018) Recent trends in deep learning based natural language processing. IEEE Computational Intelligence Magazine, 13(3), 55–75.
    [Google Scholar]
  63. Zhang, W., Gao, J., Gao, Z. & Chen, H. (2020) Adjoint‐driven deep‐learning seismic full‐waveform inversion. IEEE Transactions on Geoscience and Remote Sensing, 59(10), 8913–8932.
    [Google Scholar]
  64. Zhu, W., Xu, K., Darve, E., Biondi, B. & Beroza, G.C. (2022) Integrating deep neural networks with full‐waveform inversion: reparameterization, regularization, and uncertainty quantification. Geophysics, 87(1), R93–R109.
    [Google Scholar]
/content/journals/10.1111/1365-2478.13292
Loading
/content/journals/10.1111/1365-2478.13292
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): full waveform; inversion; numerical study; seismics

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error