Journal of Jilin University(Earth Science Edition) ›› 2017, Vol. 47 ›› Issue (6): 1855-1864.doi: 10.13278/j.cnki.jjuese.201706302

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Application of Shearlet Transform for Suppressing Random Noise in GPR Data

Wang Xiannan, Liu Sixin, Cheng Hao   

  1. College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China
  • Received:2017-03-26 Online:2017-11-26 Published:2017-11-26
  • Supported by:
    Supported by the Natural Science Foundation of China (41076076), the National High-Tech R&D of China (2013AA064603) and the Graduate Innovation Fund of Jilin University (2016201)

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Abstract: Random noise is one of the serious problems encountered during the GPR data processing. It directly influences the accuracy and reliability of the processing results. In order to remove the random noise effectively and retain the useful information, the authors propose a random noise suppression method in GPR data by using Shearlet transform. As a non-adaptive geometric-analysis method with multi-directions and multi-scales, Shearlet transform can be used to approximately represent the high-dimensional curves with singular points. In Shearlet domain, GPR data appear more sparsely. Through the threshold de-noising method, random noises can be suppressed effectively; so that the signal to noise ratio (SNR) is improved by 4 dB, and the useful information is retained to the maximum extent. The effectivity and accuracy of the Shearlet-transform threshold de-noising method are validated by the theoretical and practical data.

Key words: Shearlet transform, ground penetrating radar, random noise, hard threshold

CLC Number: 

  • P631.4
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