1887
1st Australasian Exploration Geoscience Conference – Exploration Innovation Integration
  • ISSN: 2202-0586
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Abstract

As the big-data age arrives, the efficiency of three dimensional inversions of potential field data can be paid enough attention by scholars. A new inversion method is considered to deal with the large potential field data with a fast rate of convergence. Hence, in this paper, a fast inversion method is researched. And the gravity data is used as an example of potential field data to test the efficiency of our inversion method. To achieve this aim, the study region will be divided into huge amounts of rectangular prisms with unknown constant physical properties of rock. The traditional smooth inversion method is the main principle, and a new Barzilai-Borwein iterative algorithm is applied to ensure the rapid rate of convergence of the inversion method. To compare the rate of convergence of the BB (Barzilai-Borwein) iterative algorithm, the iterative gradient descent algorithm and the iterative conjugate gradient algorithm are used as the compared algorithms. To test high efficiency of the new fast developed inversion method, the large synthetic gravity data are performed. The contrast analysis results can easily reflect the high efficiency of our new inversion method. The great practical value of our inversion method is expounded by a real gravity data. Therefore, the new inversion method may have a great influence on the potential field data inversion.

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/content/journals/10.1071/ASEG2018abP021
2018-12-01
2026-01-21

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References

  1. Abedi, M., Gholami, A., Norouzi, G. H., and Fathianpour, N., 2013. Fast inversion of magnetic data using Lanczos bidiagonalization method: Journal of Applied Geophysics, v. 90, no. 6, p. 126-137.
  2. Barbosa, V. C. F., and Silva, J. B., 1994. Generalized compact gravity inversion: Geophysics, v. 59, no. 1, p. 57-68.
  3. Barzilai, J., and Borwein, J. M., 1988. Two point step-size methods: Ima Journal on Numerical Analysis.
  4. Bosch, M., 2001. Joint inversion of gravity and magnetic data under lithologic constraints: Leading Edge, v. 20, no. 8.
  5. Chen, Z., Meng, X., Guo, L., and Liu, G., 2012. GICUDA: A parallel program for 3D correlation imaging of large scale gravity and gravity gradiometry data on graphics processing units with CUDA: Computers & Geosciences, v. 46, no. 3, p. 119-128.
  6. Cuma, M., and Zhdanov, M. S., 2014. Massively parallel regularized 3D inversion of potential fields on CPUs and GPUs: Computers & Geosciences, v. 62, p. 80-87.
  7. Dias, F. J. S. S., 2009. 3D gravity inversion through an adaptive learning procedure: Geophysics, v. 74, no. 3, p. I9.
  8. Dutra, A. C., and Marangoni, Y. R., 2009. Gravity and magnetic 3D inversion of Morro do Engenho complex, Central Brazil: Journal of South American Earth Sciences, v. 28, no. 2, p. 193-203.
  9. Dransfield, M., and Zeng, Y., 2009. Airborne gravity gradiometry: Terrain corrections and elevation error: Geophysics, v. 74, no. 5, p. 37.
  10. Dransfield, M. H., and Christensen, A. N., 2013. Performance of airborne gravity gradiometers: The Leading Edge, v. 32, no. 8, p. 908-922.
  11. Hammer, S., 1982. Airborne gravity is here: Geophysics, v. 1, no. 5, p. 213-223.
  12. Hammond, S., and Murphy, C., 2003. Air-FTG™: Bell Geospace’s airborne gravity gradiometer, a description and case study: ASEG Preview, v. 105, p. 24-26.
  13. Lee, J. B., 2001. FALCON gravity gradiometer technology: Exploration Geophysics, v. 32, no. 4, p. 247-250.
  14. Meng, Z., Li, F., Xu, X., Huang, D., and Zhang, D., 2016a. Fast inversion of gravity data using the symmetric successive over-relaxation (SSOR) preconditioned conjugate gradient algorithm: Exploration Geophysics.
  15. Meng, Z., Li, F., Zhang, D., Xu, X., and Huang, D., 2016b. Fast 3D inversion of airborne gravity-gradiometry data using Lanczos bidiagonalization method Journal of Applied Geophysics, v. 132, p. 211-228.
  16. Tikhonov, A. N., and Arsenin, V. Y., 1977. Solutions of Ill-Posed Problems, v. 32, no. 144, p. 491-491.
  17. Wang, Y., and Ma, S., 2007. Projected Barzilai-Borwein method for large-scale nonnegative image restoration: Inverse Problems in Science and Engineering, v. 15, no. 6, p. 559-583.
  18. Wang, Y., and Yang, C., 2010. Accelerating migration deconvolution using a nonmonotone gradient method: Geophysics, v. 75, no. 4, p. S131-S137.
  19. Zhdanov, M. S., 2002. Geophysical inverse theory and regularization problems, Elsevier.
  20. Zhdanov, M. S., 2009. New advances in regularized inversion of gravity and electromagnetic data: Geophysical Prospecting, v. 57, no. 4, p. 463-478.
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  • Article Type: Research Article
Keyword(s): Barzilai-Borwein iterative algorithm; fast convergence; the inversion of potential field data

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