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

LIU Zong-hui; WU Yi-fan; LIU Bao-dong; LIU Mao-mao; LAN Ri-yan; SUN Huai-feng

doi:10.13374/j.issn2095-9389.2019.04.12.001

Volume 42 Issue 3

Mar. 2020

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Engineering > 2020 > 42(3): 390-398

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

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

PDF( 1486 KB)

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

doi: 10.13374/j.issn2095-9389.2019.04.12.001

1.
Guangxi Xinfazhan Communications Group Co Ltd., Nanning 530028, China
2.
College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
3.
School of Civil Engineering, Shandong University, Jinan 250061, China
4.
Nanning Pipe Gallery Construction Investment Co., Ltd., Nanning 530219, China

More Information

Corresponding author: E-mail: 47573043@qq.com
Received Date: 2019-04-12
Publish Date: 2020-03-01

Abstract

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.
- tunnel geological prediction,
- ground-penetrating radar,
- shearlet transform,
- wavelet transform,
- interference suppression

FullText(HTML)

References(19)

References

[1]	薛翊國, 李術才, 蘇茂鑫, 等. 隧道施工期超前地質預報實施方法研究. 巖土力學, 2011, 32(8):2416 doi: 10.3969/j.issn.1000-7598.2011.08.028 Xue Y G, Li S C, Su M X, et al. Study of geological prediction implementation method in tunnel construction. Rock Soil Mech, 2011, 32(8): 2416 doi: 10.3969/j.issn.1000-7598.2011.08.028
[2]	高永濤, 徐俊, 吳順川, 等. 基于GPR反射波信號多維分析的隧道病害智能辨識. 工程科學學報, 2018, 40(3):293 Gao Y T, Xu J, Wu S C, et al. An intelligent identification method to detect tunnel defects based on the multidimensional analysis of GPR reflections. Chin J Eng, 2018, 40(3): 293
[3]	Jol H M. Ground Penetrating Radar: Theory and Applications. Elsevier, 2009
[4]	楊光, 劉敦文. 石英砂巖體的地質雷達波頻譜特征. 工程科學學報, 2015, 37(11):1397 Yang G, Liu D W. Frequency spectrum characteristic of quartz sandstone rock mass with GPR. Chin J Eng, 2015, 37(11): 1397
[5]	柳剛, 李術才, 薛翊國, 等. 基于小波變換的雷達低信噪比信號處理技術及應用研究. 工程勘察, 2009, 37(9):85 Liu G, Li S C, Xue Y G, et al. GPR signal processing approach under low signal to noise ratio based on wavelet transforms and its application. Geotech Invest Surv, 2009, 37(9): 85
[6]	Bao Q Z, Li Q C, Chen W C. GPR data noise attenuation on the curvelet transform. Appl Geophys, 2014, 11(3): 301 doi: 10.1007/s11770-014-0444-2
[7]	Gan L, Zhou L, You X G, et al. The instantaneous frequency extraction of GPR B-scan data based on HHT method // 2012 International Conference on Machine Learning and Cybernetics (ICMLC). Xi’an, 2012: 982
[8]	Ouadfeul S A, Aliouane L. Multiscale analysis of noisy 3D GPR data using the directional continuous wavelet transform // 2012 14th International Conference on Ground Penetrating Radar (GPR). Shanghai, 2012: 257
[9]	李才明, 王良書, 徐鳴潔, 等. 基于小波能譜分析的巖溶區探地雷達目標識別. 地球物理學報, 2006, 49(5):1499 doi: 10.3321/j.issn:0001-5733.2006.05.030 Li C M, Wang L S, Xu M J, et al. Objects recognition of ground penetrating radar in karst regions using wavelet energy spectrum analysis. Chin J Geophys, 2006, 49(5): 1499 doi: 10.3321/j.issn:0001-5733.2006.05.030
[10]	Addison A D, Battista B M, Knapp C C. Improved hydrogeophysical parameter estimation from empirical mode decomposition processed ground penetrating radar data. J Environ Eng Geophys, 2009, 14(4): 171 doi: 10.2113/JEEG14.4.171
[11]	Chen C S, Jeng Y. Nonlinear data processing method for the signal enhancement of GPR data. J Appl Geophys, 2011, 75(1): 113 doi: 10.1016/j.jappgeo.2011.06.017
[12]	Zhang Z Y, Zhang X D, Yu H Y, et al. Noise suppression based on a fast discrete curvelet transform. J Geophys Eng, 2010, 7(1): 105 doi: 10.1088/1742-2132/7/1/009
[13]	朱自強, 朱賀, 魯光銀, 等. 基于Curvelet變換的隧道裂隙水GPR數據處理研究. 物探化探計算技術, 2014, 36(5):571 doi: 10.3969/j.issn.1001-1749.2014.05.10 Zhu Z Q, Zhu H, Lu G Y, et al. Processing of GPR data in tunnel fissure water based on Curvelet transform. Comput Tech Geophys Geochem Explor, 2014, 36(5): 571 doi: 10.3969/j.issn.1001-1749.2014.05.10
[14]	周輪, 李術才, 許振浩, 等. 隧道施工期超前預報地質雷達異常干擾識別及處理. 隧道建設, 2016, 36(12):1517 doi: 10.3973/j.issn.1672-741X.2016.12.017 Zhou L, Li S C, Xu Z H, et al. Interpretation and treatment of interfering factors in advance geological prediction by ground penetrating radar of tunnel construction. Tunnel Constr, 2016, 36(12): 1517 doi: 10.3973/j.issn.1672-741X.2016.12.017
[15]	劉成明, 王德利, 王通, 等. 基于Shearlet變換的地震隨機噪聲壓制. 石油學報, 2014, 35(4):692 doi: 10.7623/syxb201404009 Liu C M, Wang D L, Wang T, et al. Random seismic noise attention based on the Shearlet transform. Acta Petrol Sin, 2014, 35(4): 692 doi: 10.7623/syxb201404009
[16]	劉成明, 王德利, 胡斌, 等. Shearlet域稀疏約束地震數據重建. 吉林大學學報: 地球科學版, 2016, 46(6):1855 Liu C M, Wang D L, Hu B, et al. Seismic date interpolation based on sparse in Shearlet domain. J Jilin Univ Earth Sci Ed, 2016, 46(6): 1855
[17]	Guo K, Labate D. The construction of smooth Parseval frames of shearlets. Math Modell Nat Phenom, 2013, 8(1): 82 doi: 10.1051/mmnp/20138106
[18]	Easley G, Labate D, Lim W Q. Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal, 2008, 25(1): 25 doi: 10.1016/j.acha.2007.09.003
[19]	張先武, 高云澤, 方廣有. 帶有低通濾波的廣義S變換在探地雷達層位識別中的應用. 地球物理學報, 2013, 56(1):309 doi: 10.6038/cjg20130132 Zhang X W, Gao Y Z, Fang G Y, et al. Application of generalized S transform with low-pass filtering to layer recognition of Ground Penetrating Radar. Chin J Geophys, 2013, 56(1): 309 doi: 10.6038/cjg20130132