LIU Zong-hui, WU Yi-fan, LIU Bao-dong, LIU Mao-mao, LAN Ri-yan, SUN Huai-feng. Research on the interference elimination method of GPR signal for tunnel geological prediction[J]. Chinese Journal of Engineering, 2020, 42(3): 390-398. DOI: 10.13374/j.issn2095-9389.2019.04.12.001
Citation: LIU Zong-hui, WU Yi-fan, LIU Bao-dong, LIU Mao-mao, LAN Ri-yan, SUN Huai-feng. Research on the interference elimination method of GPR signal for tunnel geological prediction[J]. Chinese Journal of Engineering, 2020, 42(3): 390-398. DOI: 10.13374/j.issn2095-9389.2019.04.12.001

Research on the interference elimination method of GPR signal for tunnel geological prediction

  • Ground-penetrating radar (GPR) has been used in a wide range of shallow detection applications, such as underground geological mapping, highway detection, and hydrogeology survey. In recent years, GPR has been most widely utilized in tunnel geological prediction because it has the advantages of high resolution, intuitionistic results, and fast scanning. In addition, GPR signal is a typical nonstationary and time-varying signal, with its electromagnetic wave exhibiting strong absorption attenuation and dispersion as it propagated in complex surrounding rock. At the same time, the GPR response is often characterized by a weak signal and a strong interference because of numerous system interferences in the tunnel detection environment, which lead to difficulties in data processing and interpretation. Therefore, interference elimination is always a difficult problem when GPR is applied to tunnel geological prediction. In this study, through the introduction of shearlet transform (ST) to GPR signal processing, an adaptive thresholding method is proposed to eliminate random interference on the basis of the energy difference between effective and interference signals in the shearlet domain at different scales and directions. The advantages of this method in random interference removal are verified by forward simulation data. On this basis, the interference signal, as well as its energy proximity and frequency anomaly, common in advanced tunnel geological prediction is taken as an example to illustrate the effect of wavelet transform (WT) on its removal. In this manner, WT and ST are combined to suppress interference. First, WT is used to separate abnormal frequency interference. Then, ST based on the adaptive thresholding method is used to suppress random interference. The results of practical engineering cases of karst cave detection in the field show that the method proposed in this study can remove the interference signal, retain the effective signal, and highlight the abnormal geological area on the basis of the processed waveform stacking diagram to improve the interpretation accuracy of GPR data.
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