Research on the interference elimination method of GPR signal for tunnel geological prediction
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摘要: 受探測環境制約，隧道超前地質預報過程中探地雷達反射波往往具有“弱信號，強干擾”的特征，給數據處理和解譯帶來極大的困難。將剪切變換（shearlet變換，ST）引入探地雷達信號處理，根據有效信號和干擾信號在剪切域中不同尺度、不同方向上的能量差異，提出一種基于自適應閥值的隨機干擾去除方法，并通過正演模擬數據驗證了該方法在隨機干擾去除上的優勢；在此基礎上針對隧道超前地質預報中常見的能量接近、頻率異常干擾信號，以實際數據為例說明小波變換（WT）對其去除效果；從而進一步提出小波變換與剪切變換聯合干擾壓制方法，即首先使用小波變換對異常頻率干擾進行分離，然后采用基于自適應閥值的剪切變換對隨機干擾進行壓制。現場溶洞探測案例應用效果表明，本文所提出的方法能在去除干擾的同時很好地保留有效信號，根據處理后的波形堆積圖可以很好地凸顯地質異常區域，從而提高探地雷達資料解譯精度。Abstract: 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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圖 1 正演幾何模型. （a）圓形空洞；（b）方形空洞
Figure 1. Geometric model of forward simulation: (a) circular hole; (b) square hole
圖 2 正演模擬結果. （a）圓形空洞；（b）方形空洞
Figure 2. Forward simulation results: (a) circular hole; (b) square hole
圖 3 加噪后數據. （a）圓形空洞；（b）方形空洞
Figure 3. Data with random interference: (a) circular hole; (b) square hole
圖 4 小波變換處理結果. （a）圓形空洞；（b）方形空洞
Figure 4. Results after Wavelet transform processing: (a) circular hole; (b) square hole
圖 5 剪切變換處理結果. （a）圓形空洞；（b）方形空洞
Figure 5. Results after shearlet transform processing: (a) circular hole; (b) square hole
圖 6 第200道單道波數據去噪前后對比. （a）原始數據；（b）小波變換；（c）剪切變換
Figure 6. Comparison before and after denoizing of the 200th A-scan: (a) raw data; (b) wavelet transform; (c) shearlet transform
圖 7 頻率異常信號干擾消除前后灰度圖. （a）常規方法；（b）剪切變換；（c）小波變換
Figure 7. Image of before and after elimination of abnormal frequency signal interference: (a) conventional method; (b) shearlet transform; (c) wavelet transform
圖 8 頻率異常干擾消除前后單道波廣義S變換時頻分布. （a）常規方法；（b）剪切變換；（c）小波變換
Figure 8. Generalized S transform (GST) spectrogram of GPR A-scan before and after eliminating the interference: (a) conventional method; (b) shearlet; (c) wavelet
圖 12 去噪效果對比. （a）常規方法；（b）小波變換；（c）聯合算法
Figure 12. Comparison of different denoizing methods: (a) conventional method; (b) wavelet transform; (c) joint algorithm
圖 13 廣義S變換時頻分布結果對比. （a）常規方法；（b）小波變換；（c）聯合算法
Figure 13. Comparison of GST results obtained using different denoizing methods: (a) conventional method; (b) wavelet transform; (c) joint algorithm
表 1 小波變換與剪切變換處理前后信噪比、峰值信噪比、均方誤差對比表
Table 1. Comparison of SNR, PSNR and MSE before and after wavelet and shearlet transform processing
Model type SNR / dB PSNR MSE Noisy data WT ST Noisy data WT ST Noisy data WT ST Circular ?2.457 9.559 22.871 14.144 26.170 39.481 3.347 0.259 0.041 Square ?2.528 9.469 22.987 14.153 26.150 39.667 4.891 0.249 0.002 
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