Engineering

On the Steadfastness of the Least-Square Reverse-Time Migration Wavefield Extrapolation via 1st-Order Riemannian Axis Finite-Difference Solver

Authors

  • HUSSEIN MUHAMMED

    Shandong Provincial Key Laboratory of Reservoir Geology, China University of Petroleum (East China)
    Author
  • Xiaodong Sun

    Shandong Provincial Key Laboratory of Reservoir Geology, China University of Petroleum (East China), Qingdao, 266580, China
    Author
  • Liping Gao

    College of Sciences and Arts, School of Mathematics, China University of Petroleum (East China), Qingdao, 266580, China
    Author
  • AbdelHafiz Gadelmula

    Centre for Seismological Phenomena, Department of Geology, Faculty of Science, University of Khartoum, Sudan
    Author
  • Zhenchun Li

    Shandong Provincial Key Laboratory of Reservoir Geology, China University of Petroleum (East China), Qingdao, 266580, China
    Author

DOI:

https://doi.org/10.71107/dh3px571

Keywords:

Seismic waves propagation; Stability analysis; Riemannian metric tensors; Claerbout’s principle; Finite-Difference modelling.

Abstract

Exploring Earth's deep regions through pioneering Least-Squares Reverse-Time Migration (LSRTM) methods is of significant interest due to its exceptional structural clarity. This cutting-edge seismic imaging technique is time-consuming and memory-intensive, so wavefield extrapolation is proposed in the Pseudodepth domain (1st-order Riemannian coordinate system’s axis) to address these issues and prevent oversampling/aliasing when modeling deeper subsurface zones. Stabilizing the generated Riemannian wavefield involves implementing an appropriate mapping velocity and obtaining the vertical axis operator that partially converts the finite difference solver from time to frequency domains. Each Cartesian point  has a corresponding vertical-time point , allowing interpolation of the reconstructed source wavefield through a Cartesian-to-Riemannian mapping function. Our stability and convergence analysis indicates that the spatial derivatives of the 1st-order Riemannian axis can be approximated by Fourier pseudo-spectral methods and fast-Fourier transforms using a special Gaussian-like impulse function. This function generates the source term vector-matrix within the finite-difference operator. The mapping velocity, derived as a differential form of the initial input velocity model, controls the CFL conditions of the associated Riemannian-finite difference operator. Numerical, synthetic, and seismic field data examples show that this approach is more stable and efficient in extrapolating a smooth 1st-order Riemannian axis-based finite-difference wavefield while adhering to Claerbout’s principle for locating subsurface reflectors. Additionally, choosing the appropriate sampling rate for the new vertical axis is inversely related to the maximum frequency of the impulse wavelet and directly related to the minimum velocity value in the model.

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Author Biographies

  • HUSSEIN MUHAMMED, Shandong Provincial Key Laboratory of Reservoir Geology, China University of Petroleum (East China)

    B.Sc. (Hons.) degree in Geology from the University of Khartoum, Sudan (Oct. 2015) and M.Sc. degree in Geological Engineering from China University of Petroleum (East China). PhD in math at Northwestern Polytechnical University. His research interests include: Geophysical Exploration Methods, Unified Field theory, Finite-Difference Methods, Finite-Element modeling, CFDs and Numerical Analysis.

  • Xiaodong Sun, Shandong Provincial Key Laboratory of Reservoir Geology, China University of Petroleum (East China), Qingdao, 266580, China

    Shandong Provincial Key Laboratory of Reservoir Geology, China University of Petroleum (East China), Qingdao, 266580, China

  • Liping Gao, College of Sciences and Arts, School of Mathematics, China University of Petroleum (East China), Qingdao, 266580, China

    College of Sciences and Arts, School of Mathematics, China University of Petroleum (East China), Qingdao, 266580, China

  • AbdelHafiz Gadelmula, Centre for Seismological Phenomena, Department of Geology, Faculty of Science, University of Khartoum, Sudan

    Centre for Seismological Phenomena, Department of Geology, Faculty of Science, University of Khartoum, Sudan

  • Zhenchun Li, Shandong Provincial Key Laboratory of Reservoir Geology, China University of Petroleum (East China), Qingdao, 266580, China

    Shandong Provincial Key Laboratory of Reservoir Geology, China University of Petroleum (East China), Qingdao, 266580, China

References

1. Alkhalifah T., 1997. Seismic data processing in vertically inhomogeneous TI media, Geophysics 62: 662-675. DOI: https://doi.org/10.1190/1.1444175

2. Alkhalifah, T., Fomel, S., & Biondi, B. 2001. The space–time domain: theory and modelling for anisotropic media. Geophysical Journal International, 144(1), 105-113. DOI: https://doi.org/10.1046/j.1365-246x.2001.00300.x

3. Alkhalifah, T., Song, C., & Huang, X. 2021. High-dimensional wavefield solutions based on neural network functions. In First International Meeting for Applied Geoscience & Energy (pp. 2440-2444). Society of Exploration Geophysicists. DOI: https://doi.org/10.1190/segam2021-3584030.1

4. Alkhalifah, T., Song, C., bin Waheed, U., & Hao, Q. 2021. Wavefield solutions from machine learned functions constrained by the Helmholtz equation. Artificial Intelligence in Geosciences, 2, 11-19. DOI: https://doi.org/10.1016/j.aiig.2021.08.002

5. Almuteri, K., Shragge, J., & Sava, P. (2023). Modeling the seismic wavefield of moving marine vibrator source. Geophysics, 89(1), 1-68. DOI: https://doi.org/10.1190/geo2023-0176.1

6. Alzahrani, H., & Shragge, J. 2021. Seismic Velocity Model Building Using Neural Networks: Training Data Design and Learning Generalization. Geophysics, 87(2), 1-73. DOI: https://doi.org/10.1190/geo2020-0547.1

7. Bleistein, N., Zhang, Y., Xu, S., Zhang, G., and Gray, S. H., 2005. Migration/ inversion: think image point coordinates, process in acquisition surface coordinates: Inverse Problems, 21, 1715–1744. 21/5/013. DOI: https://doi.org/10.1088/0266-5611/21/5/013

8. Carcione, J. M., G. C. Herman, and A. P. E. Ten Kroode, 2002. Seismic modeling: Geophysics, 67, 1304–1325, DOI: https://doi.org/10.1190/1.1500393

9. Claerbout, J. F., 1971. Toward a unified theory of reflector mapping: Geophysics, 36, 467–481. DOI: https://doi.org/10.1190/1.1440185

10. Claerbout, J. F., 1985. Imaging the earth’s interior: Blackwell Scientific Publications. doi:10.1111/j.1365-246X.1986.tb01086. x.

11. Claerbout, J., and Doherty, S., 1972. Downward continuation of moveout corrected seismograms: Geophysics, 37, 741–768. DOI: https://doi.org/10.1190/1.1440298

12. Dai, W., & Schuster, G. T. 2013. Plane-Wave Least-Squares Reverse-Time Migration. Geophysics, 78(4), S165-S177. DOI: https://doi.org/10.1190/geo2012-0377.1

13. Dai, W., Fowler, P., and Schuster, G. T., 2012. Multi-source least-squares reverse time migration: Geophysical Prospecting, 60, 681–695. DOI: https://doi.org/10.1111/j.1365-2478.2012.01092.x

14. De Jonge, T., Vinje, V., Poole, G., Hou, S., & Iversen, E. 2022. Debubbling seismic data using a generalized neural network. Geophysics, 87(1), V1-V14. DOI: https://doi.org/10.1190/geo2021-0053.1

15. Duveneck E, Milcik P, Bakker PM, Perkins C. Acoustic VTI wave equations and their application for anisotropic reverse-time migration. In SEG Technical Program Expanded Abstracts 2008 Jan 15, Society of Exploration Geophysicists pp. 2186-2190. DOI: https://doi.org/10.1190/1.3059320

16. Jaramillo, H. H., and Bleistein, N., 1999. The link of Kirchhoff migration and demigration to Kirchhoff and Born modeling. Geophysics, 64, 1793–1805. DOI: https://doi.org/10.1190/1.1444685

17. Jean Virieux, 1984. SH-wave propagation in heterogeneous media: Velocity‐stress finite‐difference method. Geophysics 49: 1933-1942. DOI: https://doi.org/10.1190/1.1441605

18. Jean Virieux, 1986. P-SV wave propagation in heterogeneous media: Velocity-stress finite-difference method, Geophysics, 51: 889-901. DOI: https://doi.org/10.1190/1.1442147

19. Jizhong Yang, Yuzhu Liu, and Liangguo Dong. 2016. Least-squares reverse time migration in the presence of density variations. Geophysics 81: S497-S509. DOI: https://doi.org/10.1190/geo2016-0075.1

20. K. Adel, H. Mohamed, and E. Mohamed, 2013. Domain limited solution of the wave equation in Riemannian coordinates. Geophysics 78: T21-T27. DOI: https://doi.org/10.1190/geo2012-0027.1

21. Kaplan, S. T., Routh, P. S., and Sacchi, M. D., 2010. Derivation of forward and adjoint operators for least-squares shot-profile split-step migration. Geophysics 75, S225–S235. DOI: https://doi.org/10.1190/1.3506146

22. Khalil, A., Sun, J., Zhang, Y., and Poole, G., 2013. RTM noise attenuation and image enhancement using time-shift gathers. 83rd Annual International Meeting, SEG, Expanded Abstracts, 1–5. Doi: 10.1190/IGC Beijing 2014-127. DOI: https://doi.org/10.3997/2214-4609.20141485

23. Khaniani, H., Bancroft, J. C., and von Lunen, E., 2016. Iterative multiparameter waveform inversion of precritical reflection data using pre-stack time Kirchhoff approximation. Geophysics, 81, R15–R27. DOI: https://doi.org/10.1190/geo2014-0560.1

24. Kumar, R., & Sharma, A. 2019. Absorbing boundary condition (ABC) and perfectly matched layer (PML) in numerical beam propagation: a comparison. Optical and Quantum Electronics, 51(2), 1-13. DOI: https://doi.org/10.1007/s11082-019-1770-7

25. Li, X., Ji, G., Guan, B., Du, Z., Han, C., & Cheng, Q. (2024). Elimination of seismic characteristics of solid-filled in ultra-deep fractured-vuggy reservoirs. Exploration Geophysics, 1-17. DOI: https://doi.org/10.1080/08123985.2024.2307582

26. Li, Y., Bakulin, A., Nivlet, P., Smith, R., & Alkhalifah, T. 2021. Target-oriented time-lapse elastic full-waveform inversion assisted by deep learning with prior information. In SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy. One Petro. Doi: 10.1190/segam2021-3581711.1. DOI: https://doi.org/10.1190/segam2021-3581711.1

27. Ma X. X. and Alkhalifah T., 2013. Wavefield Extrapolation in Pseudodepth Domain. Geophysics 78: S81-S91. DOI: https://doi.org/10.1190/geo2012-0237.1

28. Majid, S. Z., Faridi, W. A., Asjad, M. I., Abd El-Rahman, M., & Eldin, S. M. (2023). Explicit soliton structure formation for the Riemann wave equation and a sensitive demonstration. Fractal and Fractional, 7(2), 102. DOI: https://doi.org/10.3390/fractalfract7020102

29. Metallinos, A., Chondros, M., & Papadimitriou, A. (2021). Simulating Nearshore Wave Processes Utilizing an Enhanced Boussinesq-Type Model. Modelling, 2(4), 686-705. DOI: https://doi.org/10.3390/modelling2040037

30. Moczo, P., J. Kristek, and M. Galis, 2014. The finite-difference modelling of earthquake motions: Waves and ruptures. Cambridge University Press. doi: 10.1017/CBO9781139236911. DOI: https://doi.org/10.1017/CBO9781139236911

31. Moczo, P., J. Kristek, M. Galis, E. Chaljub, and V. Etienne, 2011. 3-D finite difference, finite-element, discontinuous-Galerkin and spectral-element schemes analyzed for their accuracy with respect to P-wave to S-wave speed ratio. Geophysical Journal International, 187, 1645–1667. DOI: https://doi.org/10.1111/j.1365-246X.2011.05221.x

32. Moczo, P., J. O. A. Robertsson, and L. Eisner, 2007. The finite-difference time-domain method for modeling of seismic wave propagation. Advances in Geophysics, 48, 421–516. DOI: https://doi.org/10.1016/S0065-2687(06)48008-0

33. Mulder, W. A., 2021. A numerically exact non-reflecting boundary condition applied to the acoustic Helmholtz equation. Geophysics, 0: 1-42. DOI: https://doi.org/10.3997/2214-4609.202010362

34. Pasalic Damir and McGarry Ray, 2010. Convolutional perfectly matched layer for isotropic and anisotropic acoustic wave equations. SEG Technical Program Expanded Abstracts, 2925-2929. DOI: https://doi.org/10.1190/1.3513453

35. Robertsson, J. O. A., J. O. Blanch, and W. W. Symes, 1994. Viscoelastic finite-difference modeling. Geophysics, 59, 1444–1456. DOI: https://doi.org/10.1190/1.1443701

36. Quiceno, H. R., & Arias, C. (2019). Analysis of the stability and dispersion for a Riemannian acoustic wave equation. Applied Mathematics and Computation, 341, 288-300. DOI: https://doi.org/10.1016/j.amc.2018.08.047

37. Sava, P. and S. Fomel, (2001), 3-D traveltime computation using Huygens wavefront tracing. Geophysics, 66, 883-889. DOI: https://doi.org/10.1190/1.1444977

38. Sava, P. and S. Fomel, 2005. Riemannian Wavefield Extrapolation. Geophysics, 70, T45-T56. DOI: https://doi.org/10.1190/1.1925748

39. Sava, P. C., and Fomel, S., 2003. Angle-Domain Common-Image Gathers by Wavefield Continuation Methods. Geophysics, 68, 1065–1074. DOI: https://doi.org/10.1190/1.1581078

40. Savioli, G. B., Santos, J. E., Carcione, J. M., & Gei, D. (2017). A model for CO 2 storage and seismic monitoring combining multiphase fluid flow and wave propagation simulators. The Sleipner-field case. Computational Geosciences, 21, 223-239. DOI: https://doi.org/10.1007/s10596-016-9607-y

41. Sethi, H., Shragge, J., & Tsvankin, I. (2022). Tensorial elastodynamics for coupled acoustic/elastic anisotropic media: incorporating bathymetry. Geophysical Journal International, 228(2), 999-1014. DOI: https://doi.org/10.1093/gji/ggab374

42. Shragge Jeffrey Chilver, 2008. Riemannian wavefield extrapolation: Nonorthogonal coordinate systems. Geophysics, 73: T11-T21. DOI: https://doi.org/10.1190/1.2834879

43. Song, C., Alkhalifah, T., & Waheed, U. B. 2021. A versatile framework to solve the Helmholtz equation using physics-informed neural networks. Geophysical Journal International, 228(3), 1750-1762. DOI: https://doi.org/10.1093/gji/ggab434

44. Tu, N., and Herrmann, F. J., 2015. Fast imaging with surface-related multiples by sparse inversion. Geophysical Journal International, 201, 304–317. DOI: https://doi.org/10.1093/gji/ggv020

45. Wang, Y., Liang, W., Nashed, Z., Li, X., Liang, G., and Yang, C., 2014. Seismic modeling by optimizing regularized staggered-grid finite difference operators using a time-space-domain dispersion-relationship preserving method. Geophysics, 79, T277–T285. DOI: https://doi.org/10.1190/geo2014-0078.1

46. Wu, D., Yao, G., Cao, J., and Wang, Y., 2016. Least-squares RTM with L1 norm regularisation. Journal of Geophysics and Engineering, 13, 666–673. DOI: https://doi.org/10.1088/1742-2132/13/5/666

47. Xu, S., Zhang, Y., and Tang, B., 2011. 3D angle gathers from reverse time migration. Geophysics, 76, S77–S92. DOI: https://doi.org/10.1190/1.3536527

48. Yao Gang, 2013. Least-Squares Reverse-Time Migration. PhD thesis, Imperial College London (open access: http://hdl.handle.net/10044/1/14575). Appears in Collections: Earth Science and Engineering PhD theses. doi: 10.25560/14575. DOI: https://doi.org/10.3997/2214-4609.20130474

49. Yao, G., and Jakubowicz, H., 2012. Least-squares reverse-time migration: 74th Conference and Exhibition, EAGE, Expanded Abstracts, 1-5., X043. DOI: https://doi.org/10.1190/segam2012-1425.1

50. Yao, G., and Jakubowicz, H., 2016. Least-squares reverse-time migration in a matrix-based formulation. Geophysical Prospecting, 64, 611–621. DOI: https://doi.org/10.1111/1365-2478.12305

51. Yao, G., da Silva, N. V., & Wu, D. 2018. Forward modelling formulas for least-squares reverse-time migration. Exploration Geophysics, 49(4), 506-518. DOI: https://doi.org/10.1071/EG16157

52. Yao, G., Wu, D., and Debens, H. A., 2016. Adaptive finite difference for seismic wavefield modelling in acoustic media. Scientific Reports, 6, 30302. DOI: https://doi.org/10.1038/srep30302

53. Yee, K., 1966. Numerical solution of initial boundary value problems involving Maxwell's equations in isotropic media. IEEE Transactions on antennas and propagation, 14(3), 302-307. DOI: https://doi.org/10.1109/TAP.1966.1138693

54. Yilmaz, O., 2001. Seismic data analysis: Processing, inversion, and interpretation of seismic data. SEG. doi: 10.1190/1.9781560801580. DOI: https://doi.org/10.1190/1.9781560801580

55. Zeng, C., Dong, S., Mao, J., and Wang, B., 2014. Broadband Least-Squares Reverse Time Migration for Complex Structure Imaging: 84th Annual International Meeting, SEG, Expanded Abstracts, 3715–3719. DOI: https://doi.org/10.1190/segam2014-1283.1

56. Zhang, Y., Duan, L., and Xie, Y., 2015. A stable and practical implementation of least-squares reverse time migration. Geophysics, 80, V23–V31. DOI: https://doi.org/10.1190/geo2013-0461.1

57. Zhang, Y., Sun, J., and Gray, S., 2007. Reverse-time migration: amplitude and implementation issues. 77th Annual International Meeting, SEG, Expanded Abstracts, 2145–2149. DOI: https://doi.org/10.1190/1.2792912

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Published

2026-05-08

How to Cite

On the Steadfastness of the Least-Square Reverse-Time Migration Wavefield Extrapolation via 1st-Order Riemannian Axis Finite-Difference Solver. (2026). Conclusions in Engineering, 2(1), 42-56. https://doi.org/10.71107/dh3px571

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