1887
Volume 14 Number 2
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

Even though ground penetrating radar data signal processing has already been studied by many researchers, more research is needed and expected from automatic ground penetrating radar data analysis. An automatic band‐pass filtering procedure can lead to sufficient real‐time data interpretation as signal buried in noise could be amplified. Ground penetrating radar traces are highly non‐stationary, requiring time‐varying processing techniques. An algorithm, based on self‐inverse filtering, which is a special case of inverse filtering, was implemented. It is a ground penetrating radar trace filtering approach and is implemented by applying inverse filtering in each time sample in the time‐frequency domain. Applied on a synthetic trace, this algorithm performed better than a stationary band‐pass filter and empirical mode decomposition family methods, whereas its application on real ground penetrating radar data from two different sites enhanced reflections buried in noise without the need to test different high‐frequency band stops and with minimum distortion of the signal and the initial temporal resolution of the data.

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/content/journals/10.3997/1873-0604.2015025
2015-04-01
2024-03-28
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References

  1. BailiJ., LahouarS., HergliM., Al‐QadiI. and BesbesK.2009. GPR signal denoising by discrete wavelet transform. NDT & E International42, 696–703.
    [Google Scholar]
  2. BattistaB.M., AddisonA.D. and KnappC.C.2009. Empirical mode decomposition operator for dewowing GPR data. Journal of Environmental & Engineering Geophysics14, 163–169.
    [Google Scholar]
  3. BerkhoutA.J.1977. Least‐squares inverse filtering and wavelet deconvolution. Geophysics42, 1369–1383.
    [Google Scholar]
  4. CanalesL.L.1984. Random noise reduction. 54th SEG Annual International Meeting, Expanded Abstracts, Session: S10.1.
    [Google Scholar]
  5. CarpentierS., HorstmeyerH., GreenA., DoetschJ. and CosciaI.2010. Semiautomated suppression of above‐surface diffractions in GPR data. Geophysics75, J43–J50.
    [Google Scholar]
  6. ClaerboutJ.F.1976. Fundamentals of Geophysical Data Processing.McGraw‐Hill: New York, NY.
    [Google Scholar]
  7. DonohoD.1981. On minimum entropy deconvolution. In: Applied Time Series Analysis II (ed. D.F.Findley ), pp. 565–608. Academic Press.
    [Google Scholar]
  8. EconomouN. and BenedettoF.2014. Development of advanced GPR data processing techniques. Project 3.5 Progress Report, second yearly meeting of the COST Action TU1208 “Civil Engineering Applications of Ground Penetrating Radar”, EGU2014, Vienna, Austria.
    [Google Scholar]
  9. EconomouN. and VafidisA.2010. Spectral balancing of GPR data using time‐variant bandwidth in t‐f domain. Geophysics75(3), J19–J27.
    [Google Scholar]
  10. EconomouN. and VafidisA.2011. Deterministic deconvolution for GPR data in t‐f domain. Near Surface Geophysics9(5), 427–433.
    [Google Scholar]
  11. EconomouN. and VafidisA.2012. GPR data time varying deconvolution by kurtosis maximization. Journal of Applied Geophysics81, 117–121.
    [Google Scholar]
  12. EconomouN., VafidisA., BenedettoF. and AlaniA.2015. GPR data processing techniques. In: Civil Engineering Applications of Ground Penetrating Radar. Springer, ISBN: 978‐3‐319‐04812‐3.
    [Google Scholar]
  13. GulunayN.1986. FX deconvolution and complex Wiener prediction filter. 56th SEG Annual International Meeting, Expanded Abstracts, Session: POS2.10.
    [Google Scholar]
  14. JengY, LiY., ChenC. and ChienH.2009. Adaptive filtering of random noise in near‐surface seismic and ground‐penetrating radar data. Journal of Applied Geophysics68, 36–46.
    [Google Scholar]
  15. KimJ., ChoS. and YiM.2007. Removal of ringing noise in GPR data by signal processing. Journal of Geosciences11, 75–81.
    [Google Scholar]
  16. LiJ., ChenL., XiaS., XuP. and LiuF.2014. A complete ensemble empirical mode decomposition for GPR signal time‐frequency analysis. Proceedings of SPIE 9077, Radar Sensor TechnologyXVIII, 90770C, May 29, doi:10.1117/12.2050432.
    [Google Scholar]
  17. LiuG., ChenX., DuJ. and WuK.2012. Random noise attenuation using f‐x regularized nonstationary autoregression. Geophysics77(2), V61–V69.
    [Google Scholar]
  18. ManatakiM., VafidisA. and SarrisA.2014. Application of empirical mode decomposition methods to ground penetrating radar data. First Break32, 67–71.
    [Google Scholar]
  19. MargraveG.1998. Theory of nonstationary linear filtering in the Fourier domain with application to time‐variant filtering. Geophysics63(1), 244–259.
    [Google Scholar]
  20. MohapatraS. and McMechanA.2014. Prediction and subtraction of coherent noise using a data driven time shift: a case study using field 2D and 3D GPR data. Journal of Applied Geophysics111, 312–319.
    [Google Scholar]
  21. NuzzoL. and QuartaT.2004. Improvement in GPR coherent noise attenuation using t‐p and wavelet transforms. Geophysics69(3), 789802.
    [Google Scholar]
  22. StockwellR.G., MansinhaL. and LoweR.P.1996. Localization of the complex spectrum: the S‐transform. IEEE Transactions on Signal Processing44, 998–1001.
    [Google Scholar]
  23. TsofliasG.2008. GPR imaging of dual‐porosity rocks: insights to fluid flow. The Leading Edge27, 1436–1445.
    [Google Scholar]
  24. TsofliasG., HalihanT. and SharpJ.2001. Monitoring pumping test response in a fractured aquifer using ground penetrating radar. Water Resources Research37, 1221–1229.
    [Google Scholar]
  25. TsofliasG. and SharpJ.1998. Three‐dimensional hydrogeologic characterization of fractured carbonate aquifers using ground‐penetrating radar. Gulf Coast Association of Geological Societies TransactionsXLVIII, 439–447.
    [Google Scholar]
  26. TzanisA.2010. matGPR Release 2: A freeware MATLAB® package for the analysis & interpretation of common and single offset GPR data. FastTimes15(1), 17–43.
    [Google Scholar]
  27. Van der BaanM.2012. Bandwidth enhancement: inverse Q filtering or time‐varying Wiener deconvolution Geophysics77, V133–V142.
    [Google Scholar]
  28. TzanisA.2013. Detection and extraction of orientation‐and‐scale‐dependent information from two‐dimensional GPR data with tuneable directional wavelet filters. Journal of Applied Geophysics89, 48–67, doi: 10.1016/j.jappgeo.2012.11.007.
    [Google Scholar]
  29. TzanisA.2014. Signal enhancement and geometric information retrieval from 2D GPR data with multi‐scale, orientation‐sensitive filtering methods. First Break32, 91–98.
    [Google Scholar]
  30. XieX., ZengC. and WangZ.2013. GPR signal enhancement using band‐pass and K‐L filtering: a case study for the evaluation of grout in a shielded tunnel. Journal of Geophysical Engineering10, doi: 10.1088/1742‐2132/10/3/034003.
    [Google Scholar]
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