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Volume 70, Issue 6
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

The Fisher–Yates random shuffling algorithm combined with the finite‐difference time‐domain method is proposed to construct a fine grid model for the forward simulation of ground‐penetrating radar in mixed media. First, the finite‐difference time‐domain method was used to divide the coarse grid model into several fine grid models by conforming to the boundary conditions of different media, and the corresponding dielectric parameters were assigned to Yee cells in each fine grid model. Then, the Fisher–Yates random shuffling algorithm was used to randomly scramble all Yee cells with equal probability, and the array of scrambled Yee cells was recombined into a coarse grid model. Finally, the geoelectric model of mixed media was generated with the finite‐difference time‐domain method, and a ground‐penetrating radar image excited by electromagnetic wave pulses was obtained. To explore the characteristic signals and dielectric properties of the ground‐penetrating radar electromagnetic response in mixed media, image entropy theory was used to describe the ground‐penetrating radar image, and waveform analysis and wavelet transform mode maximum methods were used to analyse the single‐channel ground‐penetrating radar signal of the mixed media. The results showed that the Fisher–Yates random shuffling–finite‐difference time‐domain method can be used to construct a valid and stable fine grid model for simulating ground‐penetrating radar in mixed media. The model effectively inhibits electromagnetic attenuation and energy dissipation, and the wavelet transform mode maximum method explains the relative dielectric permittivity distribution of the mixed media. The findings of this study can be used as a theoretical basis for correcting radar parameters and interpreting images when ground‐penetrating radar is applied to mixed media.

© 2022 European Association of Geoscientists & Engineers. © 2022 European Association of Geoscientists & Engineers.
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2022-06-16
2024-04-26

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References

  1. Al‐Qadi, I.L., Xie, W., Jones, D.L. and Roberts, R. (2010) Development of a time–frequency approach to quantify railroad ballast fouling condition using ultrawide band ground‐penetrating radar data. International Journal of Pavement Engineering, 11, 269–279.
     [Google Scholar]
  2. Aorigele . (2020) Research on electrical characteristics of mixed media based on computer simulation technology. Journal of Physics: Conference Series, 1654, 012041.
     [Google Scholar]
  3. Artagan, S.S. and Borecky, V. (2020) Advances in the nondestructive condition assessment of railway ballast: a focus on GPR. NDT & E International115, 102290.
     [Google Scholar]
  4. Bai, H. and Sinfield, J.V. (2020) Improved background and clutter reduction for pipe detection under pavement using ground penetrating radar (GPR). Journal of Applied Geophysics172, 103918.
     [Google Scholar]
  5. Benedetto, A., Tosti, F., Bianchini Ciampoli, L. and D'Amico, F. (2017) An overview of ground‐penetrating radar signal processing techniques for road inspections. Signal Processing, 132, 201–209.
     [Google Scholar]
  6. Bianchini Ciampoli, L., Tosti, F., Brancadoro, M.G., D'Amico, F., Alani, A.M. and Benedetto, A. (2017) A spectral analysis of ground‐penetrating radar data for the assessment of the railway ballast geometric properties. NDT and E International, 90, 39–47.
     [Google Scholar]
  7. Bianchini Ciampoli, L., Tosti, F., Economou, N. and Benedetto, F. (2019) Signal processing of GPR data for road surveys. Geosciences9.
     [Google Scholar]
  8. Christ, A. (2005) Analysis and Improvement of the Numerical Properties of the FDTD Algorithm. Hartung‐Gorre.
     [Google Scholar]
  9. Farmani, A. (2019) Three‐dimensional FDTD analysis of a nanostructured plasmonic sensor in the near‐infrared range. Journal of the Optical Society of America B36, 401–407.
     [Google Scholar]
  10. Feng, D., Wang, X. and Zhang, B. (2018) Specific evaluation of tunnel lining multidefects by all‐refined GPR simulation method using hybrid algorithm of FETD and FDTD. Construction and Building Materials, 185, 220–229.
     [Google Scholar]
  11. Feng, D.S., Chen, J.W. and Wu, Q. (2014) A hybrid ADI‐FDTD subgridding scheme for efficient GPR simulation of dispersive media. Chinese J. Geophysics, 57, 1322–1334 (in Chinese).
     [Google Scholar]
  12. Feng, X., Ren, Q. and Liu, C. (2017) Quantitative imaging for civil engineering by joint full waveform inversion of surface‐based GPR and shallow seismic reflection data. Construction and Building Materials, 154, 1173–1182.
     [Google Scholar]
  13. Genz, A. (1992) Numerical computation of multivariate normal probabilities. Journal of Computational and Graphical Statistics, 1, 141–149.
     [Google Scholar]
  14. Ghasemi, F.S.A. and Abrishamian, M.S. (2007) A novel method for FDTD numerical GPR imaging of arbitrary shapes based on Fourier transform. NDT & E International, 40, 140–146.
     [Google Scholar]
  15. Hagness, S.C., Taflove, A. and Gedney, S.D. (2005) Finite‐difference time‐domain methods. In: Numerical Methods in Electromagnetics. Volume 13Special Volume (Handbook of Numerical Analysis). North Holland, pp. 199–315.
     [Google Scholar]
  16. Inan, U.S. and Marshall, R.A. (2011) Numerical Electromagnetics: The FDTD Method. Cambridge University Press.
     [Google Scholar]
  17. Jin‐Fa, L., Lee, R. and Cangellaris, A. (1997) Time‐domain finite‐element methods. IEEE Transactions on Antennas and Propagation, 45, 430–442.
     [Google Scholar]
  18. Lu, Y., Peng, S., Cui, X., Li, D., Wang, K. and Xing, Z. (2020) 3D FDTD anisotropic and dispersive modeling for GPR using rotated staggered grid method. Computers and Geosciences136, 104397.
     [Google Scholar]
  19. Metropolis, N. and Ulam, S. (1949) The Monte Carlo method. Journal of American Statistical Association, 44, 335–341.
     [Google Scholar]
  20. Michael, L., Sinclair, A. and Zuckerman, D. (1993) Optimal speedup of Las Vegas algorithms. Information on Processing Letters, 47, 173–180.
     [Google Scholar]
  21. Musanna, F. and Kumar, S. (2018) A novel fractional order chaos‐based image encryption using Fisher Yates algorithm and 3‐D cat map. Multimedia Tools and Applications, 78, 14867–14895.
     [Google Scholar]
  22. Muzy, J.F., Bacry, E. and Arneodo, A. (1993) Multifractal formalism for fractal signals: The structure‐function approach versus the wavelet‐transform modulus‐maxima method. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 47, 875–884.
     [Google Scholar]
  23. Ni, S.‐H., Huang, Y.‐H., Lo, K.‐F. and Lin, D.‐C. (2010) Buried pipe detection by ground penetrating radar using the discrete wavelet transform. Computers and Geotechnics, 37, 440–448.
     [Google Scholar]
  24. Ramli, K.N., Abd‐Alhameed, R.A. and Excell, P.S. (2014) Development of complex electromagnetic problems using FDTD subgridding in hybrid computational techniques. In: Engineering Tools, Techniques, and Tables, pp. 1 online resource. Nova Publishers.
     [Google Scholar]
  25. Ristic, A.V., Petrovacki, D. and Govedarica, M. (2009) A new method to simultaneously estimate the radius of a cylindrical object and the wave propagation velocity from GPR data. Computers & Geosciences, 35, 1620–1630.
     [Google Scholar]
  26. Soldovieri, F., Lopera, O. and Lambot, S. (2011) Combination of advanced inversion techniques for an accurate target localization via GPR for demining applications. IEEE Transactions on Geoscience and Remote Sensing, 49, 451–461.
     [Google Scholar]
  27. Teixeira, F.L. (2008) Time‐domain finite‐difference and finite‐element methods for maxwell equations in complex media. IEEE Transactions on Antennas and Propagation, 56, 2150–2166.
     [Google Scholar]
  28. Tsai, D.Y., Lee, Y. and Matsuyama, E. (2008) Information entropy measure for evaluation of image quality. Journal of Digital Imaging, 21, 338–347.
     [Google Scholar]
  29. Versaci, M. and Morabito, F.C. (2021) Image edge detection: a new approach based on fuzzy entropy and fuzzy divergence. International Journal of Fuzzy Systems, 23, 918–936.
     [Google Scholar]
  30. Vorobyev, O.B. (2014) Energy density and velocity of electromagnetic waves in lossy chiral medium. Journal of Optics16, 015701.
     [Google Scholar]
  31. Yadav, M., Gautam, P.R., Shokeen, V. and Singhal, P.K. (2017) Modern Fisher–Yates shuffling based random interleaver design for SCFDMA‐IDMA systems. Wireless Personal Communications, 97, 63–73.
     [Google Scholar]
  32. Zarei, S., Oskooi, B., Amini, N. and Dalkhani, A.R. (2016) 2D spectral element modelling of GPR wave propagation in inhomogeneous media. Journal of Applied Geophysics, 133, 92–97.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.13214
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Fine grid model for the dielectric characteristics of ground‐penetrating radar in mixed media
Geophysical Prospecting 70, 1052 (2022); https://doi.org/10.1111/1365-2478.13214
/content/journals/10.1111/1365-2478.13214
/content/journals/10.1111/1365-2478.13214
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  • Article Type: Research Article
Keyword(s): Finite difference methods; Ground‐penetrating radar; Numerical modelling; Signal processing; Wavelet

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