1887
Volume 72, Issue 2
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Forward modelling of potential field data is an important part of optimization algorithms used to invert large datasets such as those involving rugged terrain or borehole data. Two‐dimensional fast Fourier transform modelling with a prism or a dipole is one of the most efficient methods compared to the forward modelling in the space domain. However, the exact solution of a prismatic source is limited to the case of a half‐space with the computation of data on a horizontal datum above the topography. Starting from the three‐dimensional Fourier forward modelling analytical formulation for a prism, an integration according to the wavenumber is accomplished which allowed to find a two‐dimensional Fourier exact analytical formulation outside, at the interfaces of, and inside a prism for all potential field components. This new formulation requires the calculation of only four integrals. The gravity and magnetic fields are computed with this two‐dimensional fast Fourier transform formulation in the entire domain and compared with the analytical space domain and the three‐dimensional fast Fourier transform formulations. From the three‐dimensional calculated field, each component can be interpolated with the tri‐linear interpolation method along a borehole or on a drape surface simulating an airborne survey. Based on experiments demonstrated in this work, the two‐dimensional formulation in the Fourier domain gave accurate results with greater speed of execution in comparison to modelling in the space domain. The forward modelling method is tested on real gravity data from the north of Alberta (Canada).

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/content/journals/10.1111/1365-2478.13427
2024-01-30
2025-05-12
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References

  1. Astic, T. & Oldenburg, D. (2019) A framework for petrophysically and geologically guided geophysical inversion using a dynamic Gaussian mixture model prior. Geophysical Journal International, 219, 989–2012.
    [Google Scholar]
  2. Beleggia, M. & DeGraef, M. (2003) On the computation of the demagnetization tensor field for an arbitrary particle shape using a Fourier space approach. Journal of Magnetism and Magnetic Materials, 263, L1–L9.
    [Google Scholar]
  3. Bhattacharyya, B. (1965) Two‐dimensional harmonic analysis as a tool for magnetic interpretation. Geophysics, 30(5), 829–857.
    [Google Scholar]
  4. Bhattacharyya, B. (1966) Continuous spectrum of the total‐magnetic‐field anomaly due to a rectangular prismatic body. Geophysics, 31(1), 97–121.
    [Google Scholar]
  5. Bhattacharyya, B. & Navolio, M. (1976) A fast Fourier transform method for rapid computation of gravity and magnetic anomalies due to arbitrary bodies. Geophysical Prospecting, 24, 633–649.
    [Google Scholar]
  6. Blakely, R. (1996) Potential Theory in gravity and magnetic applications, Cambridge University Press.
    [Google Scholar]
  7. Boulanger, O. & Chouteau, M. (2001) Constraints in 3D gravity inversion. Geophysical Prospecting, 49, 265–280.
    [Google Scholar]
  8. Chen, L. & Liu, L. (2019) Fast and accurate forward modelling of gravity field using prismatic grids. Geophysical Journal International, 216, 1062–1071.
    [Google Scholar]
  9. Dai, S., Chen, Q., Li, K. & Ling, J. (2022) The forward modeling of 3D gravity and magnetic potential fields in space‐wavenumber domains on an integral method. Geophysics, 87(3), G83–G96.
    [Google Scholar]
  10. Dai, S.‐K., Zhao, D.‐D., Zhang, Q.‐J., Li, K., Chen, Q.‐R. & Wang, X.‐L. (2019) Three‐dimensional numerical modeling of gravity anomalies based on Poisson equation in space‐wavenumber mixed domain. Applied Geophysics, 15(3), 513–523.
    [Google Scholar]
  11. Farquharson, C. & Mosher, C. (2009) Three‐dimensional modelling of gravity data using finite differences. Journal of Applied Geophysics, 68, 417–422.
    [Google Scholar]
  12. Fedi, M. & Rapolla, A. (1999) 3‐D inversion of gravity and magnetic data with depth resolution. Geophysics, 64, 452–460.
    [Google Scholar]
  13. Fukushima, T. (2020) Speed and accuracy improvements in standard algorithm for prismatic gravitational field. Geophysical Journal International, 222, 1898–1908.
    [Google Scholar]
  14. Gallardo‐Delgado, L., Perez‐Flores, M. & Gomez‐Trevino, E. (2003) A versatile algorithm for joint 3D inversion of gravity and magnetic data. Geophysics, 68(3), 949–959.
    [Google Scholar]
  15. Geng, M., Huang, D.Yang, Q. & Liu, Y. (2014) 3D inversion of airborne gravity‐gradiometry data using cokriging. Geophysics, 79, G37–G47.
    [Google Scholar]
  16. Giraud, J., Ogarko, V., Lindsay, M., Pakyuz‐Charrier, E., Jessell, M. & Martin, R. (2019) Sensitivity of constrained joint inversions to geological and petrophysical input data uncertainties with posterior geological analysis. Geophysical Journal International, 218, 666–688.
    [Google Scholar]
  17. Gradshteyn, I. & Ryzhik, I. (2007) Table of integrals, series and products, 7th edition. Academic Press.
    [Google Scholar]
  18. Hogue, J., Renaut, R. & Vatankhah, S. (2020) A tutorial and open source software for the efficient evaluation of gravity and magnetic kernels. Computers and Geosciences, 144, 1–13.
    [Google Scholar]
  19. Lelièvre, P. & Oldenburg, D. (2006) Magnetic forward modelling and inversion for high susceptibility. Geophysical Journal International, 166, 76–90.
    [Google Scholar]
  20. Li, K., Chen, K.L., Chen, Q., Dai, S., Zhang, Q., Zhao, D. & Ling, J. (2018) Fast 3D forward modeling of the magnetic field and gradient tensor on an undulated surface. Applied Geophysics, 15(3), 500–512.
    [Google Scholar]
  21. Li, X. & Chouteau, M. (1998) Three‐dimensional gravity modelling in all space. Surveys in Geophysics, 19, 339–368.
    [Google Scholar]
  22. Li, Y. & Oldenburg, D. (1996) 3‐D inversion of magnetic data. Geophysics, 61, 394–408.
    [Google Scholar]
  23. Li, Y. & Oldenburg, D. (1998) 3‐D inversion of gravity data. Geophysics, 63, 109–119.
    [Google Scholar]
  24. Li, Y. & Oldenburg, D. (2003) Fast inversion of large‐scale magnetic data using wavelet transforms<br>and a logarithmic barrier method. Geophysical Journal International, 19, 251–265.
    [Google Scholar]
  25. Marcotte, D., Shamsipour, P., Coutant, O. & Chouteau, M. (2014) Inversion of potential fields on nodes for large grids. Journal of Applied Geophysics, 110, 90–97.
    [Google Scholar]
  26. Mosher, C. & Farquharson, C. (2013) Minimum‐structure borehole gravity inversion for mineral exploration: a synthetic modeling study. Geophysics, 78, G25–G39.
    [Google Scholar]
  27. Naidu, P.S. & Mathew, M.P. (1998) Analysis of geophysical potential fields– A digital signal processing approach, Elsevier.
    [Google Scholar]
  28. Nelson, B. (1988) Calculation of magnetic gradient tensor from total field gradient measurements and its application to geophysical interpretation. Geophysics, 53(7), 957–966.
    [Google Scholar]
  29. Ogarko, V., Giraud, J., Martin, R. & Jessell, M. (2021) Disjoint interval bound constraints using the alternating direction method of multipliers for geologically constrained inversion: application to gravity data. Geophysics, 86, G1–G11.
    [Google Scholar]
  30. Paoletti, V. Ialongo, S., Florio, G., Fedi, M. & Cella, F. (2021) Self‐constrained inversion of potential fields. Geophysical Journal International, 195, 854‐869.
    [Google Scholar]
  31. Parker, R. (1972) The rapid calculation of potential anomalies. Geophysical Journal of the Royal Astronomical Society, 31, 447–455.
    [Google Scholar]
  32. Pilkington, M. (1997) 3‐D magnetic imaging using conjugate gradients. Geophysics, 62, 1132–1142.
    [Google Scholar]
  33. Pilkington, M. (2009) 3‐D magnetic data‐space inversion with sparseness constraints. Geophysics, 74, L7–L15.
    [Google Scholar]
  34. Portniaguine, O. & Zhdanov, V. (1999) Focusing geophysical inversion images. Geophysics, 64, 874–887.
    [Google Scholar]
  35. Schwarz, K., Sideris, M. & Forsberg, R. (1990) The use of FFT techniques in physical geodesy. Geophysical Journal International, 110, 485–514.
    [Google Scholar]
  36. Shamsipour, P., Chouteau, M. & Marcotte, D. (2011) 3D stochastic inversion of magnetic data. Journal of Applied Geophysics, 73, 336–347.
    [Google Scholar]
  37. Sideris, M. & Tziavos, I. (1988) FFT evaluation and applications of gravity field convolution integrals with mean and point data. Bulletin of Geodesy, 62, 521–540.
    [Google Scholar]
  38. Sun, J. & Li, Y. (2016) Joint inversion of multiple geophysical data using guided fuzzy c‐means clustering. Geophysics, 63, ID37–ID57.
    [Google Scholar]
  39. Tandon, S., Belleggia, M., Zhu, Y. & De Graef, M. (2004) On the computation of the demagnetization tensor for uniformly magnetized particles of arbitrary shape. Part II: numerical approach. Journal of Magnetism and Magnetic Materials, 271, 27–38.
    [Google Scholar]
  40. Tontini, F., Cocchi, L. & Carmisciano, C. (2009) Rapid 3‐D forward model of potential fields with application to the Palinuro Seamount magnetic anomaly (southern Tyrrhenian Sea, Italy). Journal of Geophysical Research, 114, 1–17.
    [Google Scholar]
  41. Tontini, F., de Ronde, C., Yoerger, D., Kinsey, J. & Tivey, M. (2012) 3‐D focused inversion of near‐seafloor magnetic data with application to the Brothers volcano hydrothermal system, Southern Pacific Ocean, New Zealand. Journal of Geophysical Research, 117, 1–12.
    [Google Scholar]
  42. Tontini, F. (2012) Rapid interactive modeling of 3D magnetic anomalies. Computers & Geosciences, 48, 308–315.
    [Google Scholar]
  43. Uieda, L. & Barbosa, V. (2012) Robust 3D gravity gradient inversion by planting anomalous densities. Geophysics, 77, G55–G66.
    [Google Scholar]
  44. Vatankhah, S., Liu, S., Renaut, R., Hu, X. & Baniamerian, J. (2020) Improving the use of the randomized singular value decomposition for the inversion of gravity and magnetic data. Geophysics, 85, G93–G107.
    [Google Scholar]
  45. Vitale, A. & M.Fedi, M. (2014) Self‐constrained inversion of potential fields through a 3D depth weightings. Geophysics, 85(5), G143–G156.
    [Google Scholar]
  46. Wang, X., Zhao, D., Liu, J. & Zhang, Q. (2022) Efficient 2D modeling of magnetic anomalies using NUFFT in the Fourier domain. Pure and Applied Geophysics, 179, 2311–2325.
    [Google Scholar]
  47. Wu, L. (2016) Efficient modelling of gravity effects due to topographic masses using the Gauss‐FFT method. Geophysical Journal International, 205, 160–178.
    [Google Scholar]
  48. Wu, L. (2018) Efficient modeling of gravity fields caused by sources with arbitrary geometry and arbitrary density distribution. Survey in Geophysics, 39, 401–434.
    [Google Scholar]
  49. Wu, L. (2019) Fourier‐domain modeling of gravity effects caused by polyhedral bodies. Journal of Geodesy, 93, 635–653.
    [Google Scholar]
  50. Wu, L. (2021) Modified Parker's method for gravitational forward and inverse modeling using general polyhedral models. Journal of Geophysical Research: Solid Earth, 126(10), 1–38.
    [Google Scholar]
  51. Wu, L. & Chen, L. (2016) Fourier forward modeling of vector and tensor gravity fields due to prismatic bodies with variable density contrast. Geophysics, 81(1), G13–G26.
    [Google Scholar]
  52. Wu, L. & Tian, G. (2014) High‐precision Fourier forward modeling of potential fields. Geophysics, 79(5), G59–G68.
    [Google Scholar]
  53. Wu, L., Chen, L., Wu, B., Cheng, B. & Lin, Q. (2019) Improved Fourier modeling of gravity fields caused by polyhedral bodies with applications to asteroid Bennu and comet 67P/Churyumov‐Gerasimenko. Journal of Geodesy, 93, 1963–1984.
    [Google Scholar]
  54. Yuan, Y., Cui, Y. & Chen, B. (2022) Fast and high accuracy 3D magnetic anomaly forward modeling based on BTTB matrix (in Chinese). Chinese Journal of Geophysics, 65(3), 1107–1124.
    [Google Scholar]
  55. Zhang, Y. & Wong, Y.S. (2018) BTTB‐based numerical schemes for three‐dimensional gravity field inversion. Geophysical Journal International, 203, 243–256.
    [Google Scholar]
  56. Zhao, G., Chen, B., Chen, L., Liu, J. & Ren, Z. (2018) High‐accuracy 3D Fourier forward modeling of gravity field based on the Gauss‐FFT technique. Journal of Applied Geophysics, 150, 294–303.
    [Google Scholar]
  57. Zhou, Y., Wu, L., Wu, B., Cheng, B., Wang, H., Chen, L., Gao, S. & Lin, Q. (2020) Fourier‐domain modeling of gravity effects caused by a vertical polyhedral prism, with application to a water reservoir storage process. Geophysics, 85(6), G115–G127.
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): fast Fourier transform; gravity; magnetics; three‐dimensional modelling

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