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2023-11-21
2025-04-25
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References

  1. Anandaroop, R., Key, K., et al. [2014] Bayesian inversion of marine CSEM data from the Scarborough gas field using a transdimensional 2-D parametrization.Geophys. J. Int., 199, 1847–1860.
    [Google Scholar]
  2. Chopra, S., Castagna, J., and Xu, Y. [2009]. Thin-bed reflectivity inversion and some applications.First Break, 27, 55–62.
    [Google Scholar]
  3. Erokhin, G. and Bryksin, V. [2020]. High-resolution velocity model estimation by the RTH method.SEG International Exposition and 90th Annual Meeting, 2863–2867.
    [Google Scholar]
  4. Greer, S., Fomel, S., and Fry, M. [2019]. Prestack phase corrections using local seismic attributes. In SEG International Exposition and Annual Meeting.
    [Google Scholar]
  5. Kallweit, R.S. and WoodL.C. [1982]. The limits of resolution of zero-phase wavelets.Geophysics47(7), 1035–1046.
    [Google Scholar]
  6. Marfurt, K. and Kirlin, R. [2001]. Narrow-band spectral analysis and thin-bed tuning.Geophysics, 66(4), 1274–1283.
    [Google Scholar]
  7. Muller, G. [1985]. The reflectivity method: a tutorial.Journal of Geophysics, 58, 153–174.
    [Google Scholar]
  8. Osypov, K., Souza, M. et al. [2023]. Deep Learning Seismic Inversion: A case study from offshore Angola.84th EAGE Annual Conference & Exhibition: Extended abstract.
    [Google Scholar]
  9. Partyka, G., Gridley, J. and Lopez, J. [1999]. Interpretational applications of spectral decomposition in reservoir characterization.The Leading Edge, 18(3), 353–360.
    [Google Scholar]
  10. Puryear, C. and Castagna, P. [2008]. Layer-thickness determination and stratigraphic interpretation using spectral inversion: Theory and application.Geophysics, 73(2), R37–R48.
    [Google Scholar]
  11. Qu, D., Mosegaard, K. et al. [2023]. Using a synthetic data trained convolutional neural network for predicting subresolution thin layers from seismic data.Interpretation, 11(2): T339–T347.
    [Google Scholar]
  12. Rubino, J. and Velis, D. [2009]. Thin-bed prestack spectral inversion.Geophysics, 74(4), R49–R57.
    [Google Scholar]
  13. Shabelansky, A. and Osypov, K. [2018]. Enhancing seismic-imaging resolution by interpretation-driven data decomposition (IDDD). In SEG International Exposition and Annual Meeting (pp. SEG-2018)
    [Google Scholar]
  14. Schuster, G. [2017]. Seismic inversion. Society of Exploration Geophysicists.
    [Google Scholar]
  15. Sun, S., Nie, J., Wang, B., Zhao, L., He, Z., Zhang, H. et al. [2023]. Generating complete synthetic datasets for high-resolution amplitude-versus-offset attributes deep learning Inversion.Geophysical Prospecting, 71, 891–913.
    [Google Scholar]
  16. Wang, Y., Wang, Y-F. [2023a]. Quantitative evaluation of gas hydrate reservoir by AVO attributes analysis based on the Brekhovskikh equation.Petroleum Science.
    [Google Scholar]
  17. Wang, Y., Wang, Y-F. [2023b]. Spectral decomposition and multifrequency joint amplitude-variation-with-offset inversion based on the neural network.Geophysics, 88(3), P. R373–R383.
    [Google Scholar]
  18. Weglein, A. [2013]. A timely and necessary antidote to indirect methods and so-called P-wave FWI.The Leading Edge, 32(10), 1192–1204.
    [Google Scholar]
  19. Widess, M.B. [1973]. How thin is a thin bed?.Geophysics38(6), 1176–1180.
    [Google Scholar]
  20. Zeng, H. [2009]. How thin is a thin bed? An alternative perspective.The Leading Edge, 28(10), 1192–1197.
    [Google Scholar]
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