1887
Volume 73, Issue 6
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

The uncertainty of model parameters obtained by full‐waveform inversion can be determined from the Hessian of the least‐squares error functional. A description of uncertainty characterisation is presented that takes the null space of the Hessian into account and does not rely on the Bayesian formulation. Because the Hessian is generally too costly to compute and too large to be stored, a segmented representation of perturbations of the reconstructed subsurface model in the form of geological units is proposed. This enables the computation of the Hessian and the related covariance matrix on a larger length scale. Synthetic two‐dimensional isotropic elastic examples illustrate how conditional and marginal uncertainties can be estimated for the properties per geological unit by themselves and in relation to other units.

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2025-07-09
2026-02-11
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References

  1. Aitken, A. C.1935. “On Least Squares and Linear Combinations of Observations.” Proceedings of the Royal Society of Edinburgh55: 42–48. https://doi.org/10.1017/S0370164600014346
    [Google Scholar]
  2. Albert, A.1972. Regression and the Moore‐Penrose Pseudoinverse. Academic Press.
  3. Backus, G., and F.Gilbert. 1970. “Uniqueness in the Inversion of Inaccurate Gross Earth Data.” Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences266, no. 1173: 123–192. https://doi.org/10.1098/rsta.1970.0005
    [Google Scholar]
  4. Barbosa, C. H., L. N.Kunstmann, R. M.Silva, C. D.Alves, B. S.Silva, D. M.Filho, M.Mattoso, F. A.Rochinha, and A. L.Coutinho. 2020. “A Workflow for Seismic Imaging With Quantified Uncertainty” Computers & Geosciences145: 104615. https://doi.org/10.1016/j.cageo.2020.104615
    [Google Scholar]
  5. Ben‐Artzi, G., H.Hel‐Or, and Y.Hel‐Or. 2007. “The Gray‐Code Filter Kernels” IEEE Transactions on Pattern Analysis and Machine Intelligence29, no. 3: 382–393. https://dx.doi.org/10.1109/TPAMI.2007.62
    [Google Scholar]
  6. Betancourt, M.2018. “A Conceptual Introduction to Hamiltonian Monte Carlo.” Preprint, arXiv, July 16. https://doi.org/10.48550/arXiv.1701.02434
  7. Bharadwaj, P., W.Mulder, and G.Drijkoningen. 2016. “Full Waveform Inversion With An Auxiliary Bump Functional” Geophysical Journal International206, no. 2: 1076–1092. https://doi.org/10.1093/gji/ggw129
    [Google Scholar]
  8. Biswas, R., M.Walker, J. Zhang, P.Paramo, K.Wolf, S.Gerth, J.Winterbourne, A.Roy, P.Morris, C.Decalf, Y.Zheng, and R.Warnick. 2023. “Bayesian AVA Elastic Seismic Inversion Using Stein Variational Gradient Descent (SVGD).” In Conference Proceedings, 84th EAGE Annual Conference & Exhibition, vol. 2023, 1–5. European Association of Geoscientists & Engineers. https://doi.org/10.3997/2214‐4609.202310835
  9. Bodin, T., M.Sambridge, and K.Gallagher. 2009. “A Self‐Parametrizing Partition Model Approach to Tomographic Inverse Problems” Inverse Problems25, no. 5: 55009. https://doi.org/10.1088/0266‐5611/25/5/055009
    [Google Scholar]
  10. Bozdaǧ, E., J.Trampert, and J.Tromp. 2011. “Misfit Functions for Full Waveform Inversion Based on Instantaneous Phase and Envelope Measurements” Geophysical Journal International185, no. 2: 845–870. https://doi.org/10.1111/j.1365‐246X.2011.04970.x
    [Google Scholar]
  11. Bui‐Thanh, T., C.Burstedde, O.Ghattas, J.Martin, G.Stadler, and L. C.Wilcox. 2012. “Extreme‐Scale UQ or Bayesian Inverse Problems Governed by PDEs.” In 2012 International Conference for High Performance Computing, Networking, Storage and Analysis, 1–11. IEEE. https://doi.org/10.1109/SC.2012.56
  12. Bunks, C., F. M.Saleck, S.Zaleski, and G.Chavent. 1995. “Multiscale Seismic Waveform Inversion” Geophysics60, no. 5: 1457–1473. https://doi.org/10.1190/1.1443880
    [Google Scholar]
  13. Burdick, S., and V.Lekić. 2017. “Velocity Variations and Uncertainty From Transdimensional P‐Wave Tomography of North America” Geophysical Journal International209, no. 2: 1337–1351. https://doi.org/10.1093/gji/ggx091
    [Google Scholar]
  14. Cao, J., R.Brossier, Y.Capdeville, L.Métivier, and S.Sambolian. 2024. “A Fully Scalable Homogenization Method to Upscale 3‐D Elastic Media” Geophysical Journal International238, no. 1: 72–90. https://doi.org/10.1093/gji/ggae132
    [Google Scholar]
  15. Capdeville, Y., and L.Métivier. 2018. “Elastic Full Waveform Inversion Based on the Homogenization Method: Theoretical Framework and 2‐D Numerical Illustrations” Geophysical Journal International213, no. 2: 1093–1112. https://doi.org/10.1093/gji/ggy039
    [Google Scholar]
  16. Chen, B., and X.‐B.Xie. 2015. “An Efficient Method for Broadband Seismic Illumination and Resolution Analyses.” In SEG Technical Program Expanded Abstracts 2015, 4227–4231. Society of Exploration Geophysicists. https://doi.org/10.1190/segam2015‐5926976.1
  17. Cupillard, P., and Y.Capdeville. 2018. “Non‐Periodic Homogenization of 3‐D Elastic Media for the Seismic Wave Equation” Geophysical Journal International213, no. 2: 983–1001. https://doi.org/10.1093/gji/ggy032
    [Google Scholar]
  18. Deal, M. M., and G.Nolet. 1996. “Null‐Space Shuttles” Geophysical Journal International124, no. 2: 372–380. https://doi.org/10.1111/j.1365‐246X.1996.tb07027.x
    [Google Scholar]
  19. Duane, S., A.Kennedy, B. J.Pendleton, and D.Roweth. 1987. “Hybrid Monte Carlo” Physics Letters B195, no. 2: 216–222. https://doi.org/10.1016/0370‐2693(87)91197‐X
    [Google Scholar]
  20. Eckart, C., and G.Young. 1936. “The Approximation of One Matrix by Another of Lower Rank” Psychometrika1, no. 3: 211–218. https://doi.org/10.1007/BF02288367
    [Google Scholar]
  21. Eikrem, K. S., G.Nævdal, and M.Jakobsen. 2019. “Iterated Extended Kalman Filter Method for Time‐Lapse Seismic Full‐Waveform Inversion” Geophysical Prospecting67, no. 2: 379–394. https://doi.org/10.1111/1365‐2478.12730
    [Google Scholar]
  22. Ely, G., A.Malcolm, and O. V.Poliannikov. 2018. “Assessing Uncertainties in Velocity Models and Images With a Fast Nonlinear Uncertainty Quantification Method” Geophysics83, no. 2: R63–R75. https://doi.org/10.1190/geo2017‐0321.1
    [Google Scholar]
  23. Engquist, B., and Y.Yang. 2022. “Optimal Transport Based Seismic Inversion: Beyond Cycle Skipping” Communications on Pure and Applied Mathematics75, no. 10: 2201–2244. https://doi.org/10.1002/cpa.21990
    [Google Scholar]
  24. Fichtner, A., and J.Trampert. 2011a. “Hessian Kernels of Seismic of Seismic Data Functional Based Upon Adjoint Techniques” Geophysical Journal International185, no. 2: 775–798. https://doi.org/10.1111/j.1365‐246X.2011.04966.x
    [Google Scholar]
  25. Fichtner, A., and J.Trampert. 2011b. “Resolution Analysis in Full Waveform Inversion” Geophysical Journal International187, no. 3: 1604–1624. https://doi.org/10.1111/j.1365‐246X.2011.05218.x
    [Google Scholar]
  26. Fichtner, A., and T.vanLeeuwen. 2015. “Resolution Analysis By Random Probing” Journal of Geophysical Research: Solid Earth120, no. 8: 5549–5573. https://doi.org/10.1002/2015JB012106
    [Google Scholar]
  27. Fichtner, A., and A.Zunino. 2019. “Hamiltonian Nullspace Shuttles” Geophysical Research Letters46, no. 2: 644–651. https://doi.org/10.1029/2018GL080931
    [Google Scholar]
  28. Fino, B. J., and V. R.Algazi. 1976. “Unified Matrix Treatment of the Fast Walsh‐Hadamard Transform” IEEE Transactions on ComputersC‐25, no. 11: 1142–1146. https://doi.org/10.1109/TC.1976.1674569
    [Google Scholar]
  29. Gfeller, D., and P. D. L.Rios. 2007. “Spectral Coarse Graining of Complex Networks” Physical Review Letters99: 038701. https://doi.org/10.1103/PhysRevLett.99.038701
    [Google Scholar]
  30. Gibson, R. L., K.Gao, E.Chung, and Y.Efendiev. 2014. “Multiscale Modeling of Acoustic Wave Propagation in 2D Media” Geophysics79, no. 2: T61–T75. https://doi.org/10.1190/geo2012‐0208.1
    [Google Scholar]
  31. Gradshteyn, I. S., and I. M.Ryzhik. 2000. Table of Integrals, Series, and Products. 6th ed.Academic Press.
  32. Guo, P., S.Singh, V. A.Vaddineni, G.Visser, I.Grevemeyer, and E.Saygin. 2020. “Nonlinear Full Waveform Inversion of Wide‐Aperture OBS Data for Moho Structure Using a Trans‐Dimensional Bayesian Method” Geophysical Journal International224, no. 2: 1056–1078. https://doi.org/10.1093/gji/ggaa505
    [Google Scholar]
  33. Hackbusch, W.1985. Multi‐Grid Methods and Applications. Springer. https://doi.org/10.1007/978‐3‐662‐02427‐0
  34. Hak, B., and W. A.Mulder. 2010. “Migration for Velocity and Attenuation Perturbations” Geophysical Prospecting58, no. 6: 939–952. https://doi.org/10.1111/j.1365‐2478.2010.00866.x
    [Google Scholar]
  35. Hanson, A. J. 1995. II.4 ‐ 4 Rotations for N‐Dimensional Graphics. In: PaethA. W., ed. Graphics Gems V, pp. 55–64. Academic Press. https://doi.org/10.1016/B978‐0‐12‐543457‐7.50017‐6
    [Google Scholar]
  36. Hawkins, R., and M.Sambridge. 2015. “Geophysical Imaging Using Trans‐Dimensional Trees” Geophysical Journal International203, no. 2: 972–1000. https://doi.org/10.1093/gji/ggv326
    [Google Scholar]
  37. Hoffmann, A., R.Brossier, L.Métivier, and A.Tarayoun. 2024. “Local Uncertainty Quantification for 3‐D Time‐Domain Full‐Waveform Inversion With Ensemble Kalman Filters: Application to a North Sea OBC Data Set” Geophysical Journal International237, no. 3: 1353–1383. https://doi.org/10.1093/gji/ggae114
    [Google Scholar]
  38. Huang, X.2023. “Full Wavefield Inversion with Multiples: Nonlinear Bayesian Inverse Multiple Scattering Theory Beyond the Born Approximation” Geophysics88, no. 6: T289–T303. https://doi.org/10.1190/geo2022‐0604.1
    [Google Scholar]
  39. Huang, X., K. S.Eikrem, M.Jakobsen, and G.Nævdal. 2020. “Bayesian Full‐Waveform Inversion in Anisotropic Elastic Media Using the Iterated Extended Kalman Filter” Geophysics85, no. 4: C125–C139. https://doi.org/10.1190/geo2019‐0644.1
    [Google Scholar]
  40. Inoue, H., Y.Fukao, K.Tanabe, and Y.Ogata. 1990. “Whole Mantle P‐Wave Travel Time Tomography” Physics of the Earth and Planetary Interiors59, no. 4: 294–328. https://doi.org/10.1016/0031‐9201(90)90236‐Q
    [Google Scholar]
  41. Izzatullah, M., T.Alkhalifah, J.Romero, M.Corrales, N.Luiken, and M.Ravasi. 2023. “Plug‐and‐Play Stein Variational Gradient Descent for Bayesian Post‐Stack Seismic Inversion.” In Proceedings of the 84th EAGE Annual Conference & Exhibition, vol. 2023, 1–5. European Association of Geoscientists & Engineers. https://doi.org/10.3997/2214‐4609.202310177
  42. Jiao, K., D.Sun, X.Cheng, and D.Vigh. 2015. “Adjustive Full Waveform Inversion.” In SEG Technical Program Expanded Abstracts 2015, 1091–1095. Society of Exploration Geophysicists. https://doi.org/10.1190/segam2015‐5901541.1
  43. Keating, D., and K. A.Innanen. 2021. “Null‐Space Shuttles for Targeted Uncertainty Analysis in Full‐Waveform Inversion” Geophysics86, no. 1: R63–R76. https://doi.org/10.1190/GEO2020‐0192.1
    [Google Scholar]
  44. Lévêque, J.‐J., L.Rivera, and G.Wittlinger. 1993. “On the Use of the Checker‐Board Test to Assess the Resolution of Tomographic Inversions” Geophysical Journal International115, no. 1: 313–318. https://doi.org/10.1111/j.1365‐246X.1993.tb05605.x
    [Google Scholar]
  45. Liu, Q., and D.Peter. 2019a. “Square‐Root Variable Metric Based Elastic Full‐Waveform Inversion–Part 1: Theory and Validation” Geophysical Journal International218, no. 2: 1121–1135. https://doi.org/10.1093/gji/ggz188
    [Google Scholar]
  46. Liu, Q., and D.Peter. 2019b. “Square‐Root Variable Metric Based Elastic Full‐Waveform Inversion–Part 2: Uncertainty Estimation” Geophysical Journal International218, no. 2: 1100–1120. https://doi.org/10.1093/gji/ggz137
    [Google Scholar]
  47. Liu, Q., and D.Wang. 2016. “Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm.” In Advances in Neural Information Processing Systems edited by D.Lee, M.Sugiyama, U.Luxburg, I.Guyon, and R.Garnett, vol. 29, 2378–2386. Curran Associates. https://dl.acm.org/doi/10.5555/3157096.3157362
  48. Loris, I., G.Nolet, I.Daubechies, and F. A.Dahlen. 2007. “Tomographic Inversion Using ℓ1$\ell _1$‐Norm Regularization of Wavelet Coefficients” Geophysical Journal International170, no. 1: 359–370. https://doi.org/10.1111/j.1365‐246X.2007.03409.x
    [Google Scholar]
  49. Malinverno, A., and S.Leaney. 2005. “A Monte Carlo Method to Quantify Uncertainty in the Inversion of Zero‐Offset VSP Data.” In SEG Technical Program Expanded Abstracts 2000, 2393–2396. SEG. https://doi.org/10.1190/1.1815943
  50. Martin, J., L. C.Wilcox, C.Burstedde, and O.Ghattas. 2012. “A Stochastic Newton MCMC Method for Large‐Scale Statistical Inverse Problems With Application to Seismic Inversion” SIAM Journal on Scientific Computing34, no. 3: A1460–A1487. https://doi.org/10.1137/110845598
    [Google Scholar]
  51. Meju, M. A.2009. “Regularized Extremal Bounds Analysis (REBA): An Approach To Quantifying Uncertainty in Nonlinear Geophysical Inverse Problems” Geophysical Research Letters36, no. L03304: 1–5. https://doi.org/10.1029/2008GL036407
    [Google Scholar]
  52. Métivier, L., A.Allain, R.Brossier, Q.Mérigot, E.Oudet, and J.Virieux. 2018. “Optimal Transport for Mitigating Cycle Skipping in Full‐Waveform Inversion: A Graph‐Space Transform Approach” Geophysics83, no. 5: R515–R540. https://doi.org/10.1190/geo2017‐0807.1
    [Google Scholar]
  53. Métivier, L., R.Brossier, J.Virieux, and S.Operto. 2013. “Full Waveform Inversion and the Truncated Newton Method” SIAM Journal on Scientific Computing35, no. 2: B401–B437. https://doi.org/10.1137/16M1093239
    [Google Scholar]
  54. Minkoff, S. E.1996. “A Computationally Feasible Approximate Resolution Matrix for Seismic Inverse Problems” Geophysical Journal International126, no. 2: 345–359. https://doi.org/10.1111/j.1365‐246X.1996.tb05295.x
    [Google Scholar]
  55. Mulder, W. A.2021. “Numerical Methods, Multigrid.” In Encyclopedia of Solid Earth Geophysics, Part 12, edited by H. K.Gupta, 895–900. Encyclopedia of Earth Sciences Series. Springer. https://dx.doi.org/10.1007/978‐90‐481‐8702‐7_153
  56. Mulder, W. A., and B. N.Kuvshinov. 2023. “Estimating Large‐Scale Uncertainty in the Context of Full‐Waveform Inversion.” In Proceedings of the 84th EAGE Annual Conference & Exhibition, vol. 2023, 1–5. European Association of Geoscientists & Engineers. https://doi.org/10.3997/2214‐4609.202310304
  57. Mulder, W. A., and B. N.Kuvshinov. 2025. “Accelerating Target‐Oriented Multi‐Parameter Elastic Full‐Waveform Uncertainty Estimation By Reciprocity” Geophysical Prospecting73, no. 1: 38–48. https://doi.org/10.1111/1365‐2478.13650
    [Google Scholar]
  58. Østmo, S., W. A.Mulder, and R.Plessix. 2002. “Finite‐Difference Iterative Migration By Linearized Waveform Inversion in the Frequency Domain.” In SEG Technical Program Expanded Abstracts 2002, 1384–1387. Society of Exploration Geophysicists. https://doi.org/10.1190/1.1816917
  59. Owhadi, H., and L.Zhang. 2008. “Numerical Homogenization of the Acoustic Wave Equations with a Continuum of Scales” Computer Methods in Applied Mechanics and Engineering198, no. 3: 397–406. https://doi.org/10.1016/j.cma.2008.08.012
    [Google Scholar]
  60. Petra, N., and E. W.Sachs. 2021. “Second Order Adjoints in Optimization.” In Numerical Analysis and Optimization, edited by M.Al‐Baali, A. P.Anton, and L.Grandinetti, 209–230. Springer International Publishing. https://doi.org/10.1007/978‐3‐030‐72040‐7_10
  61. Piana, A. N., G.Giacomuzzi, and A.Malinverno. 2015. “Local Three‐Dimensional Earthquake Tomography by Trans‐Dimensional Monte Carlo Sampling” Geophysical Journal International201, no. 3: 1598–1617. https://doi.org/10.1093/gji/ggv084
    [Google Scholar]
  62. Plessix, R., and W. A.Mulder. 2004. “Frequency‐Domain Finite‐Difference Amplitude‐Preserving Migration” Geophysical Journal International157, no. 3: 975–987. https://doi.org/10.1111/j.1365‐246X.2004.02282.x
    [Google Scholar]
  63. Pratt, R. G., C.Shin, and G. J.Hicks. 1998. “Gauss‐Newton and Full Newton Methods in Frequency‐Space Seismic Waveform Inversion” Geophysical Journal International133, no. 2: 341–362. https://doi.org/10.1046/j.1365‐246X.1998.00498.x
    [Google Scholar]
  64. Pringle, R. M., and A. A.Rayner. 1971. Generalized Inverse Matrices With Application to Statistics. Griffin.
  65. Qu, L., M.Araya‐Polo, and L.Demanet. 2024. “Uncertainty Quantification in Seismic Inversion Through Integrated Importance Sampling and Ensemble Methods.” Preprint, arXiv, September 10. https://doi.org/10.48550/arXiv.2409.06840
  66. Rawlinson, N., A.Fichtner, M.Sambridge, and M. K.Young. 2014. Chapter One ‐ Seismic Tomography and the Assessment of Uncertainty, 1–76. vol. 55 Advances in Geophysics. Elsevier, https://doi.org/10.1016/bs.agph.2014.08.001
  67. Ray, A., S.Kaplan, J.Washbourne, and U.Albertin. 2017. “Low Frequency Full Waveform Seismic Inversion within a Tree Based Bayesian Framework” Geophysical Journal International212, no. 1: 522–542. https://doi.org/10.1093/gji/ggx428
    [Google Scholar]
  68. Revelo Obando, B.2018. “Full Waveform Inversion in a MCMC Framework.” Master's thesis, Delft University of Technology, Delft, The Netherlands. http://resolver.tudelft.nl/uuid:3232eba7‐453d‐43a3‐a20d‐71ee4826f986
  69. Rickett, J. E.2003. “Illumination‐Based Normalization for Wave‐Equation Depth Migration” Geophysics68, no. 4: 1371–1379. https://doi.org/10.1190/1.1598130
    [Google Scholar]
  70. Riffaud, S., M. A.Fernández, and D.Lombardi. 2024. “A Low‐Rank Solver for Parameter Estimation and Uncertainty Quantification in Time‐Dependent Systems of Partial Differential Equations” Journal of Scientific Computing99, no. 2. https://doi.org/10.1007/s10915‐024‐02488‐3
    [Google Scholar]
  71. Riyanti, C. D., W. A.Mulder, G.Baeten, and R.‐E.Plessix. 2008. “An Application of a Novel Frequency‐domain Finite‐difference Solver to Compute 3D Amplitude‐preserving Migration Weights.” In Proceedings of the 70th EAGE Conference and Exhibition incorporating SPE EUROPEC 2008. European Association of Geoscientists & Engineers. https://doi.org/10.3997/2214‐4609.20147692
  72. Rizzuti, G., A.Siahkoohi, P. A.Witte, and F. J.Herrmann. 2020. “Parameterizing Uncertainty By Deep Invertible Networks: An Application To Reservoir Characterization.” In SEG Technical Program Expanded Abstracts 2020, 1541–1545. Society of Exploration Geophysicists. https://doi.org/10.1190/segam2020‐3428150.1
  73. Sen, M. K., and R.Biswas. 2017. “Transdimensional Seismic Inversion Using the Reversible Jump Hamiltonian Monte Carlo Algorithm” Geophysics82, no. 3: R119–R134. https://doi.org/10.1190/geo2016‐0010.1
    [Google Scholar]
  74. Siahkoohi, A., G.Rizzuti, R.Orozco, and F. J.Herrmann. 2023. “Reliable Amortized Variational Inference With Physics‐Based Latent Distribution Correction” Geophysics88, no. 3: R297–R322. https://doi.org/10.1190/geo2022‐0472.1
    [Google Scholar]
  75. Simons, F. J., I.Loris, G.Nolet, I. C.Daubechies, S.Voronin, J. S.Judd, P. A.Vetter, J.Charléty, and C.Vonesch. 2011. “Solving or Resolving Global Tomographic Models With Spherical Wavelets, and the Scale and Sparsity of Seismic Heterogeneity” Geophysical Journal International187, no. 2: 969–988. https://doi.org/10.1111/j.1365‐246X.2011.05190.x
    [Google Scholar]
  76. Stuart, G. K., S. E.Minkoff, and F.Pereira. 2019. “A Two‐Stage Markov Chain Monte Carlo Method for Seismic Inversion and Uncertainty Quantification” Geophysics84, no. 6: R1003–R1020. https://doi.org/10.1190/GEO2018‐0893.1
    [Google Scholar]
  77. Sun, J., K.Innanen, and C.Huang. 2021. “Physics‐Guided Deep Learning for Seismic Inversion With Hybrid Training and Uncertainty Analysis” Geophysics86, no. 3: R303–R317. https://doi.org/10.1190/geo2020‐0312.1
    [Google Scholar]
  78. Tarantola, A.1984. “Linearized Inversion of Seismic Reflection Data” Geophysical Prospecting32, no. 6: 998–1015. https://doi.org/10.1111/j.1365‐2478.1984.tb00751.x
    [Google Scholar]
  79. Tarantola, A.2005. Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM. https://doi.org/10.1137/1.9780898717921
  80. Thompson, A.2017. “The Cascading Haar Wavelet Algorithm for Computing the Walsh‐Hadamard Transform” IEEE Signal Processing Letters24, no. 7: 1020–1023. https://doi.org/10.1109/LSP.2017.2705247
    [Google Scholar]
  81. Thurin, J., R.Brossier, and L.Métivier. 2019. “Ensemble‐Based Uncertainty Estimation in Full Waveform Inversion” Geophysical Journal International219, no. 3: 1613–1635. https://doi.org/10.1093/gji/ggz384
    [Google Scholar]
  82. van Leeuwen, T., and W. A.Mulder. 2008. “Velocity Analysis Based on Data Correlation” Geophysical Prospecting56, no. 6: 791–803. https://doi.org/10.1111/j.1365‐2478.2008.00704.x
    [Google Scholar]
  83. Vasco, D. W.2007. “Invariance, Groups, and Non‐Uniqueness: The Discrete Case” Geophysical Journal International168, no. 2: 473–490. https://doi.org/10.1111/j.1365‐246X.2006.03161.x
    [Google Scholar]
  84. Vasco, D. W., L. R.Johnson, and O. Marques. 2003. “Resolution, Uncertainty, and Whole Earth Tomography” Journal of Geophysical Research: Solid Earth108, no. B1: ESE 9–1–ESE 9–26. https://doi.org/10.1029/2001JB000412
    [Google Scholar]
  85. Wang, W., G. A.McMechan, and J.Ma. 2023. “Reweighted Variational Full‐Waveform Inversions” Geophysics88, no. 4: R499–R512. https://doi.org/10.1190/geo2021‐0766.1
    [Google Scholar]
  86. Warner, M., and L.Guasch. 2016. “Adaptive Waveform Inversion: Theory” Geophysics81, no. 6: R429–R445. https://doi.org/10.1190/geo2015‐0387.1
    [Google Scholar]
  87. Zhang, X., and A.Curtis. 2020. “Variational Full‐Waveform Inversion” Geophysical Journal International222, no. 1: 406–411. https://doi.org/10.1093/gji/ggaa170
    [Google Scholar]
  88. Zhao, Z., and M. K.Sen. 2021. “A Gradient‐Based Markov Chain Monte Carlo Method for Full‐Waveform Inversion and Uncertainty Analysis” Geophysics86, no. 1: R15–R30. https://doi.org/10.1190/geo2019‐0585.1
    [Google Scholar]
  89. Zhu, D., and R.Gibson. 2018. “Seismic Inversion and Uncertainty Quantification Using Transdimensional Markov Chain Monte Carlo Method” Geophysics83, no. 4: R321–R334. https://doi.org/10.1190/geo2016‐0594.1
    [Google Scholar]
  90. Zhu, H., S.Li, S.Fomel, G.Stadler, and O. Ghattas. 2016. “A Bayesian Approach to Estimate Uncertainty for Full‐Waveform Inversion Using A Priori Information From Depth Migration” Geophysics81, no. 5: R307–R323. https://doi.org/10.1190/geo2015‐0641.1
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): elastics; full waveform; parameter estimation; seismics

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