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Abstract

SUMMARY

Empirical Mode Decomposition (EMD) is a relatively new technique for analysing non-linear and non-stationary time series. Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD) are noise assisted and adaptive methods based on EMD. Here, we compare the empirical mode decomposition methods using both synthetic and real GPR data. In particular we examine: (1) the separation of high frequency wavelets from the low frequency ones and (2) the noise level that yields better decomposition for EEMD and CEEMD. We also examine the capability of these decomposition methods to remove random and coherent noise on real GPR data.

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/content/papers/10.3997/2214-4609.20142098
2014-09-08
2024-04-19

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References

  1. Battista, B.M., Knapp, C., McGee, T. and Goebel, V.
    [2007] Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data. Geophysics, 72, H29–H37.
     [Google Scholar]
  2. Battista, B.M., Addison, A.D. and Knapp, C.C.
    [2009] Empirical Mode Decomposition Operator for Dewowing GPR Data. J. Environ. Eng. Geophys., 14, 163–169.
     [Google Scholar]
  3. Bekara, M. and van der Baan, M.
    [2009] Random and coherent noise attenuation by empirical mode decomposition. Geophysics, 74, V89–V98.
     [Google Scholar]
  4. Chen, C.-S. and Jeng, Y.
    [2011] Nonlinear data processing method for the signal enhancement of GPR data. J. Appl. Geophys., 75, 113–123.
     [Google Scholar]
  5. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C. and Liu, H.H.
    [1998] The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. Math. Phys. Eng. Sci., 454, 903–995.
     [Google Scholar]
  6. Jeng, Y. and Chen, C.-S.
    [2012] Subsurface GPR imaging of a potential collapse area in urban environments. Eng. Geol., 147–148, 57–67.
     [Google Scholar]
  7. Rilling, G., Flandrin, P. and Gonçalves, P.
    [2003] On empirical mode decomposition and its algorithms. Proceedings of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado, Italy.
     [Google Scholar]
  8. Spanoudakis, N.S., Manataki, M., Niniou-Kindeli, V. and Vafidis, A.P.
    [2011] GPR Imaging at Aptera Archaeological Site.
     [Google Scholar]
  9. Torres, M.E., Colominas, M.A., Schlotthauer, G. and Flandrin, P.
    [2011] A complete ensemble empirical mode decomposition with adaptive noise. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4144–4147.
     [Google Scholar]
  10. Wu, Z. and Huang, N.E.
    [2009] Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal., 1, 1–41.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20142098
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Evaluation of EMD-based Methods on GPR Data
Conference Proceedings 2014, 1 (2014); https://doi.org/10.3997/2214-4609.20142098
/content/papers/10.3997/2214-4609.20142098
/content/papers/10.3997/2214-4609.20142098
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