Revista UIS Ingenierías

Vol. 22 No. 3 (2023): Revista UIS Ingenierías
Articles

Shot-gather Reconstruction using a Deep Data Prior-based Neural Network Approach

PDF
  • Luis Miguel Rodríguez-López,
  • Kareth León-López,
  • Paul Goyes-Peñafiel,
  • Laura Galvis,
  • Henry Arguello
Luis Miguel Rodríguez-López
Universidad Industrial de Santander
Kareth León-López
Universidad Industrial de Santander
Paul Goyes-Peñafiel
Universidad Industrial de Santander
Laura Galvis
Universidad Industrial de Santander
Henry Arguello
Universidad Industrial de Santander
DOI: https://doi.org/10.18273/revuin.v22n3-2023013

Published 2023-09-12

Keywords

  • Seismic data regularization,
  • deep learning,
  • unsupervised learning,
  • shot-gather reconstruction,
  • deep image prior,
  • seismic processing,
  • subsampled survey,
  • convolutional network,
  • seismic acquisition,
  • data interpolation
    • ...More
    Less

How to Cite

Rodríguez-López, L. M., León-López, K., Goyes-Peñafiel, P., Galvis, L., & Arguello , H. . (2023). Shot-gather Reconstruction using a Deep Data Prior-based Neural Network Approach. Revista UIS Ingenierías, 22(3), 177–188. https://doi.org/10.18273/revuin.v22n3-2023013

Copyright (c) 2023 Revista UIS Ingenierías

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Abstract

Seismic surveys are often affected by environmental obstacles or restrictions that prevent regular sampling in seismic acquisition. To address missing data, various methods, including deep learning techniques, have been developed to extract features from complex information, albeit with the limitation of requiring external seismic databases. While previous works have primarily focused on trace reconstruction, missing shot-gathers directly impact the seismic processing flow and represent a major challenge in seismic data regularization. In this paper, we propose DIPsgr, a seismic shot-gather reconstruction method that uses only the incomplete seismic acquisition measurements to estimate their missing information employing unsupervised deep learning. Numerical experiments on three databases demonstrate that DIPsgr recovers the complete set of traces in each shot-gather, with preserved information and seismic events.

PDF

Downloads

Download data is not yet available.

References

  1. L. Hatton, M. H. Worthington, J. Makin, “Seismic data processing: theory and practice,” Merlin Profiles Ltd., Tech. Rep., 1986.
  2. N. Cooper, L. A. Briceño, L. Alfredo, M. Vides, Manual para la adquisición y procesamiento de sísmica terrestre y su aplicación en Colombia. Colombia: Universidad Nacional de Colombia, 2010. [Online]. Available: https://www.anh.gov.co/documents/34/Manual_Tecnicas_Sismica_Terrestre.pdf
  3. C. L. Liner, Elements of 3D seismology. Society of Exploration Geophysicists, 2016.
  4. X. Wang, S. Yu, “Seismic Data Regularization on Nonequispaced Grid via a Joint Sparsity-Promotion Method,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022, doi: https://doi.org/10.1109/LGRS.2021.3072356
  5. W. Liu, S. Cao, G. Li, Y. He, “Reconstruction of seismic data with missing traces based on local random sampling and curvelet transform,” J. Appl. Geophys., vol. 115, pp. 129–139, 2015, doi: https://doi.org/10.1016/j.jappgeo.2015.02.009
  6. P. Yang, J. Gao, W. Chen, “Curvelet-based POCS interpolation of nonuniformly sampled seismic records,” J. Appl. Geophys., vol. 79, pp. 90–99, 2012, doi: https://doi.org/10.1016/j.jappgeo.2011.12.004
  7. F. Kong, F. Picetti, V. Lipari, P. Bestagini, X. Tang, S. Tubaro, “Deep Prior-Based Unsupervised Reconstruction of Irregularly Sampled Seismic Data,” IEEE Geosci. Remote Sens. Lett., vol. 19, pp. 1–5, 2022, doi: https://doi.org/10.1109/LGRS.2020.3044455
  8. Q. Liu, L. Fu, M. Zhang, “Deep-seismic-prior-based reconstruction of seismic data using convolutional neural networks,” Geophysics, vol. 86, no. 2, pp. V131–V142, Mar. 2021, doi: https://doi.org/10.1190/geo2019-0570.1
  9. N. Kazemi, E. Bongajum, M. D. Sacchi, “Surface-Consistent Sparse Multichannel Blind Deconvolution of Seismic Signals,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 6, pp. 3200–3207, 2016, doi: https://doi.org/10.1109/TGRS.2015.2513417
  10. O. Villarreal, K. León-López, D. Espinosa, W. Agudelo, H. Arguello, “Seismic source reconstruction in an orthogonal geometry based on local and non-local information in the time slice domain,” J. Appl. Geophys., vol. 170, p. 103846, 2019, doi: https://doi.org/10.1016/j.jappgeo.2019.103846
  11. K. L. Lopez, J. M. Ramirez, W. Agudelo, H. Arguello Fuentes, “Regular Multi-Shot Subsampling and Reconstruction on 3D Orthogonal Symmetric Seismic Grids via Compressive Sensing,” in 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA), 2019, pp. 1–5. doi: https://doi.org/10.1109/STSIVA.2019.8730279
  12. X. Chai, G. Tang, S. Wang, K. Lin, and R. Peng, “Deep Learning for Irregularly and Regularly Missing 3-D Data Reconstruction,” IEEE Trans. Geosci. Remote Sens., vol. 59, no. 7, pp. 6244–6265, 2021, doi: https://doi.org/10.1109/TGRS.2020.3016343
  13. S. Mandelli, F. Borra, V. Lipari, P. Bestagini, A. Sarti, S. Tubaro, “Seismic data interpolation through convolutional autoencoder,” in SEG Technical Program Expanded Abstracts 2018, Aug. 2018, no. October, pp. 4101–4105. doi: https://doi.org/10.1190/segam2018-2995428.1
  14. S. Mandelli, V. Lipari, P. Bestagini, S. Tubaro, “Interpolation and Denoising of Seismic Data using Convolutional Neural Networks,” arXiv.org, pp. 1–17, Jan. 2019, doi: https://doi.org/10.48550/arXiv.1901.07927
  15. D. Kuijpers, I. Vasconcelos, and P. Putzky, “Reconstructing missing seismic data using Deep Learning,” arXiv.org, Jan. 2021, doi: https://doi.org/10.48550/arXiv.2101.09554
  16. J. D. Kelleher, “Deep learning,” Mit Press Essential Knowledge Series, vol. 1, 2019.
  17. D. Ulyanov, A. Vedaldi, V. Lempitsky, “Deep Image Prior,” Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 9446 – 9454, doi: https://doi.org/10.1109/CVPR.2018.00984
  18. B. Wang, W. Lu, “Accurate and efficient seismic data interpolation in the principal frequency wavenumber domain,” J. Geophys. Eng., vol. 14, no. 6, pp. 1475–1483, 2017, doi: https://doi.org/10.1088/1742-2140/aa82dc
  19. W. Xiongwen, W. Huazhong, Z. Xiaopeng, “High dimensional seismic data interpolation with weighted matching pursuit based on compressed sensing,” J. Geophys. Eng., vol. 11, no. 6, p. 065003, 2014, doi: https://doi.org/10.1088/1742-2132/11/6/065003
  20. Q. Wang, Y. Shen, L. Fu, H. Li, “Seismic data interpolation using deep internal learning,” Exploration Geophysics, vol. 51, no. 6, pp. 683–697, 2020, doi: https://doi.org/10.1080/08123985.2020.1748496
  21. P. Goyes-Penafiel, E. Vargas, C. V. Correa, W. Agudelo, B. Wohlberg, H. Arguello, “A Consensus Equilibrium Approach for 3-D Land Seismic Shots Recovery,” IEEE Geosci. Remote Sens. Lett., vol. 19, pp. 1–5, 2022, doi: https://doi.org/10.1109/LGRS.2021.3082421
  22. F. Luporini et al., “Architecture and Performance of Devito, a System for Automated Stencil Computation,” ACM Trans. Math. Softw., vol. 46, no. 1, pp. 1–28, 2020, doi: https://doi.org/10.1145/3374916
  23. R. Versteeg, “The Marmousi experience: Velocity model determination on a synthetic complex data set,” Lead. Edge, vol. 13, no. 9, pp. 927–936, 1994, doi: https://doi.org/10.1190/1.1437051
  24. Robert G. Keys and Douglas J. Foster, “Mobil AVO viking graben line 12,” 2023. [Online]. Available: https://wiki.seg.org/wiki/Mobil_AVO_viking_graben_line_12
  25. G. B. Madiba, G. A. McMechan, “Processing, inversion, and interpretation of a 2D seismic data set from the North Viking Graben, North Sea,” Geophysics, vol. 68, no. 3, pp. 837–848, May 2003, doi: https://doi.org/10.1190/1.1581036
  26. P. Goyes-Peñafiel et al., “Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach,” IEEE Trans. Geosci. Remote Sens., vol. 61, pp. 1–12, 2023, doi: https://doi.org/10.1109/TGRS.2023.3290468
  27. D. P. Kingma, J. Ba, “Adam: A Method for stochastic optimization,” arXiv preprint arXiv:1412.6980, pp. arXiv–1412, 2017. [Online]. Available: https://arxiv.org/abs/1412.6980