1887

Abstract

Summary

Various velocity parameterizations are used in Bayesian first-arrival tomography. We conduct a short review of the existing approaches and suggest the natural neighbor interpolation as a viable alternative. This parameterization possesses numerous useful properties. It provides naturally smooth models, which is particularly suitable for a refraction setting. It does not need any specific treatment at model boundaries, and, finally, does not need any additional parameters apart from velocities defined on a set of nodes. We compare this parameterization with a more conventional linear barycentric approach on a synthetic near-surface seismic dataset. The comparison shows that natural neighborbased tomography results in a more accurate estimation of seismic velocity inside the near-surface low-velocity anomaly and provides a lower estimate of velocity uncertainty in the whole model.

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/content/papers/10.3997/2214-4609.202112424
2021-10-18
2024-03-29
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References

  1. Belhadj, J., Romary, T., Gesret, A., Noble, M., and Figliuzzi, B.
    [2018]. New parameterizations for Bayesian seismic tomography. Inverse Problems, 34(6), 065007. https://doi.org/10.1088/1361-6420/aabce7
    [Google Scholar]
  2. Bodin, T., and Sambridge, M.
    [2009]. Seismic tomography with the reversible jump algorithm. Geophysical Journal International, 178(3), 1411–1436. https://doi.org/10.1111/j.1365-246X.2009.04226.x
    [Google Scholar]
  3. Bodin, T., Sambridge, M., Rawlinson, N., and Arroucau, P.
    [2012]. Transdimensional tomography with unknown data noise. Geophysical Journal International, 189(3), 1536–1556. https://doi.org/10.1111/j.1365-246X.2012.05414.x
    [Google Scholar]
  4. Egorov, A., Golikov, P., Silvestrov, I., and Bakulin, A.
    [2020]. Near-surface velocity uncertainty estimation through Bayesian tomography approach. SEG Technical Program Expanded Abstracts 2020, 3634–3638. https://doi.org/10.1190/segam2020-3411920.1
    [Google Scholar]
  5. Fichtner, A., Zunino, A., and Gebraad, L.
    [2019]. Hamiltonian Monte Carlo solution of tomographic inverse problems. Geophysical Journal International, 216(2), 1344–1363. https://doi.org/10.3929/ethz-b-000320896
    [Google Scholar]
  6. Galetti, E., Curtis, A., Meles, G. A., and Baptie, B.
    [2015]. Uncertainty loops in travel-time tomography from nonlinear wave physics. Physical Review Letters, 114(14), 148501.
    [Google Scholar]
  7. Hawkins, R., Bodin, T., Sambridge, M., Choblet, G., and Husson, L.
    [2019]. Trans-dimensional surface reconstruction with different classes of parameterization. Geochemistry, Geophysics, Geosystems, 20(1), 505–529.
    [Google Scholar]
  8. Park, S. W., Linsen, L., Kreylos, O., Owens, J. D., and Hamann, B.
    [2006]. Discrete Sibson interpolation. IEEE Transactions on Visualization and Computer Graphics, 12(2), 243–253.
    [Google Scholar]
  9. Ryberg, T., and Haberland, C.
    [2018]. Bayesian inversion of refraction seismic traveltime data. Geophysical Journal International, 212(3), 1645–1656. https://doi.org/10.1093/gji/ggx500
    [Google Scholar]
  10. Sibson, R.
    [1981]. A brief description of natural neighbor interpolation. Interpreting Multivariate Data.
    [Google Scholar]
  11. Zhang, X., and Curtis, A.
    [2020]. Seismic tomography using variational inference methods. Journal of Geophysical Research: Solid Earth, 125(4), e2019JB018589.
    [Google Scholar]
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