AccScience Publishing / JSE / Article
Submit to JSE
Cite this article
5
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ARTICLE

3D data-domain reflection tomography for initial velocity model building using challenging 3D seismic data

A. BAKULIN1 ,  I. SILVESTROV1 ,  D. NEKLYUDOV2 ,  K. GADYLSHIN2 ,  M. PROTASOV2
Show Less
1 EXPEC Advanced Research Center, Saudi Aramco, Building 137, Dhahran 31311, Saudi Arabia.,
2 Institute of Petroleum Geology and Geophysics SB RAS, Academician Koptyug ave. 3, 630090 Novosibirsk, Russia.,
JSE 2021, 30(5), 419–446;
Submitted: 9 June 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published: 9 June 2025
© 2025 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
Cite
XML
Abstract

Bakulin, A., Silvestrov, I., Neklyudov, D., Gadylshin, K. and Protasov, M., 2021. 3D data-domain reflection tomography for initial velocity model building using challenging 3D seismic data. Journal of Seismic Exploration, 30: 419-446. We present a novel workflow to build a reliable initial velocity depth model from challenging seismic data. This workflow is based on automated 3D grid reflection tomography that utilizes coherent poststack and prestack reflection events in the data domain. The workflow consists of two parts: data preconditioning and nonlinear tomographic inversion. Data preconditioning is underpinned by robust data-driven prestack data enhancement in the form of 3D nonlinear beamforming. Operating directly in the data domain, we obtain robust NMO velocities and pick main reflection events on stacked time images. Ray-based tomographic inversion fits prestack traveltimes approximated by hyperbolae using the engine of standard grid reflection tomography. Powerful prestack enhancement, combined with regularization of observed traveltimes by hyperbolae, delivers a robust and computationally efficient approach to reconstructing the velocity depth model directly in the data domain during the early stages of seismic processing. The new approach enables iterative depth processing critical for low signal-to-noise ratio data such as land seismic with small field arrays or single sensors. We present the tomographic workflow details and showcase the method’s capabilities using synthetic and real data examples.

Keywords
reflection tomography
inverse problem
land seismic data
References
  1. Al-Chalabi, M., 1973. Series approximation in velocity and traveltime computations.
  2. Geophys. Prosp., 21: 783-795.
  3. Alkhalifah, T., 1997. Velocity analysis using nonhyperbolic moveout in transversely
  4. isotropic media. Geophysics, 62: 1839-1854.
  5. Bakulin, A., Silvestrov, I., Dmitriev, M., Neklyudov, D., Protasov, M., Gadylshin, K.,
  6. and Dolgov, 2020. Nonlinear beamforming for enhancement of 3D prestack land
  7. seismic data. Geophysics, 85 (3): 1MJ-Z13.
  8. Bakulin, A., Dmitriev, M., Silvestrov, I., Neklyudov, D., Gadylshin, K. and Protasov, P.,
  9. 2018a. Efficient prestack enhancement based on local stacking: finding optimal
  10. domain for modern 3D land-seismic data. Expanded Abstr., 88th Ann. Internat.
  11. SEG Mtg., Anaheim: 4598-4602.
  12. Bakulin, A., Silvestrov, I., Dmitriev, M., Neklyudov, D., Protasov, M., Gadylshin, K.,
  13. Tcheverda, V. and Dolgov, V., 2018b. Nonlinear beamforming for enhancing
  14. prestack data with challenging near surface or overburden. First Break, 36(12):
  15. 121-126.
  16. Bakulin, A. and Erickson, K., 2017. Enhance-estimate-image: New processing approach
  17. for single-sensor and other seismic data with low prestack signal-to-noise ratio.
  18. Expanded Abstr., 87th Ann. Internat. SEG Mtg., Houston: 5001-5005.
  19. de Bazelaire, E., 1988. Normal moveout revisited - inhomogeneous media and curved
  20. interfaces. Geophysics, 53: 143-157.
  21. Baykulov, M. and Gajewski, D., 2009. Prestack seismic data enhancement with partial
  22. common-reflection-surface (CRS) stack. Geophysics, 74(3): V49-V58.
  23. Berkovitch, A., Deev, K. and Landa, E., 2011. How non-hyperbolic multifocusing
  24. improves depth imaging. First Break, 27: 95-103.
  25. Billette, F. and Lambaré, G., 1998. Velocity macro-model estimation by
  26. stereotomography. Geophys. J. Internat., 135: 671-680.
  27. Buzlukov, V. and Landa, E., 2013. Imaging improvement by prestack signal
  28. enhancement. Geophys. Prosp., 61: 1150-1158.
  29. Castle, R.J., 1988. Shifted hyperbolas and normal moveout. Expanded Abstr., 58th Ann.
  30. Internat. SEG Mtg., Anaheim: $9.3.
  31. Chauris, H., Noble, M.S., Lambaré, G. and Podvin, P., 2002. Migration velocity analysis
  32. from locally coherent events in 2D laterally heterogeneous media - Part I:
  33. Theoretical aspects. Geophysics, 67: 1202-1212.
  34. Duveneck, E., 2004. Tomographic determination of seismic velocity models with
  35. kinematic wavefield attributes. Logos Verlag, Berlin.
  36. Duveneck, E., 2004. Velocity model estimation with data-derived wavefront attributes.
  37. Geophysics, 69: 265-274.
  38. Diimmong S., Meier, K., Gajewski, D. and Hiibscher, C., 2008. Comparison of prestack
  39. stereotomography and NIP wave tomography for velocity model building:
  40. Instances from the Messinian evaporates. Geophysics, 73(5): WE291-
  41. VE302.
  42. Fagin S., 1999. Model Based Depth Imaging. SEG, Tulsa, OK.
  43. Fehmers, G.C. and Hocker, C.F.W., 2003. Fast structural interpretation with structure-
  44. oriented filtering. Geophysics, 68: 1286-1293.
  45. Guillaume, P., Lambaré, G., Leblanc, O., Mitouard, P., Moigne, K., Montel, J., Prescott,
  46. T., Siliqi, R., Vidal, N., Zhang, X. and Zimine, S., 2008. Kinematic invariants: an
  47. efficient and flexible approach for velocity model building. Expanded Abstr., 88th
  48. Ann. Internat. SEG Mtg., Anaheim.
  49. Guiziou, J.L., Mallet, J.L. and Madariaga, R., 1996. 3D seismic reflection tomography on
  50. top of the GOCAD depth modeler. Geophysics, 61: 1499-1510.
  51. Gjoystdal, H. and Ursin, B., 1981. Inversion of reflection times in three dimensions.
  52. Geophysics, 46: 972-983.
  53. Hubral, P. and Krey, T., 1980. Interval Velocities from Seismic Reflection Time
  54. Measurements. SEG, Tulsa, OK.
  55. Jones, I.F., 2004. A review of 3D PreSDM model building techniques. First Break, 21:
  56. 41-54.
  57. Jones, LF., 2010. An Introduction to Velocity Model Building. EAGE, Houten,
  58. Netherlands.
  59. Kliiver, T., 2006. Velocity model building using migration to residual time. Expanded
  60. Abstr., 88th Ann. Internat. SEG Mtg, Anaheim: 2022-2026.
  61. Lambaré, G., 2008. Stereotomography. Geophysics, 73(5): VE25-VE34.
  62. Lambaré, G., Guillaume, P. and Montel, J.P., 2014. Recent advances in ray-based
  63. tomography. Extended Abstr., 76th EAGE Conf., Amsterdam: We G103 01.
  64. Lavaud, B., Baina, R. and Landa, E., 2004. Automatic robust velocity estimation by
  65. poststack stereotomography. Expanded Abstr., 74th Ann. Internat. SEG Mtg.,
  66. Denver: 2351-2354.
  67. Van der Made, P.M., van Riel, P. and Berkhout, A.J., 1987. Estimation of complex
  68. velocity models from stacking information. Expanded Abstr., 57th Ann. Internat.
  69. SEG Mtg., New Orleans: 821-823.
  70. Neckludov, D., Baina, R. and Landa, E., 2006. Residual stereotomographic inversion.
  71. Geophysics, 71(4): E35-E39.
  72. Paige, C.C. and Saunders, M.A., 1982. LSQR: An algorithm for sparse linear equations
  73. and sparse least squares. ACM Transact. Math. Software, 8: 43-71.
  74. Rakotoarisoa, H., Guillaume, P., Blondel, P.C. and Charles, S., 1995. An adapted
  75. geometrical criterion for 3-D tomographic inversion. Expanded Abstr., 88th Ann.
  76. Internat. SEG Mtg., Anaheim: 1062-1065.
  77. Rauch-Davies, M., Berkovitch, A., Deev. K. and Landa, E., 2013. Non-hyperbolic
  78. multifocusing imaging for prestack signal enhancement. Expanded Abstr., 83rd
  79. Ann. Internat. SEG Mtg., Houston: 4618-4622.
  80. Robein, E., 2010. Seismic Imaging: A Review of the Techniques, their Principles, Merits
  81. and Limitations. EAGE, Houten, Netherlands.
  82. Scales, J., Gersztenkorn, A. and Treitel, S., 1988. Fast Ip solution of large, sparse linear
  83. systems, application to seismic traveltime tomography. J. Comput. Phys., 75: 313-
  84. Sexton, P.A., 1998. 3D velocity-depth model building using surface seismic and well
  85. data (SuperDix). Ph.D. Thesis, University of Durham., Durham.
  86. Sexton, P. and Williamson, P., 1998. 3D Anisotropic velocity estimation by model-based
  87. inversion of prestack traveltimes. Expanded Abstr., 68th Ann. Internat. SEG Mtg.,
  88. New Orleans: 1855-1858.
  89. Tanushev, N., Popovici, A. and Hardesty, S., 2017. Fast, high-resolution beam
  90. tomography and velocity-model building. The Leading Edge, 36: 140-145.
  91. Tsvankin, I. and Thomsen, L., 1994. Nonhyperbolic reflection moveout in anisotropic
  92. media. Geophysics, 59: 1290-1304.
  93. Woodward, V., Nichols, D., Zdraveva, O., Whitfield, P. and Johns, T., 2008. A decade of
  94. tomography. Geophysics, 73(5): VE5-VE11.
  95. Yilmaz, O., 2001. Seismic Data Analysis: Processing, Inversion, and Interpretation of
  96. Seismic Data. SEG, Tulsa, OK.
  97. Xie, Y., 2017. 3D prestack data enhancement with a simplified CO CRS. Extended
  98. Abstr., 79th EAGE Conf., Paris: WeP7.13
Share
Back to top
Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept