J. Cent. South Univ. (2019) 26: 3057−3065 DOI: https://doi.org/10.1007/s11771-019-4236-y
OMHT method for weak signal processing of GPR and its application in identification of concrete micro-crack
LING Tong-hua(凌同华)1, ZHANG Liang(张亮)1, 2, HUANG Fu(黄阜)1, GU Dan-ping(谷淡平)1, YU Bin(余彬)1, ZHANG Sheng(张胜)3
1. School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China; 2. Hunan Province Engineering Laboratory of Bridge Structure, Changsha University of Science and Technology, Changsha 410114, China; 3. College of Civil Engineering, Hunan City University, Yiyang 413000, China
© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract: In the light of the problem of weak reflection signals shielded by strong reflections from the concrete surface, the detection and the recognition of hidden micro-cracks in the shield tunnel lining were studied using the orthogonal matching pursuit and the Hilbert transform(OMHT method). First, according to the matching pursuit algorithm and the strong reflection-forming mechanism, and based on the sparse representation theory, a sparse dictionary, adapted to the characteristics of the strong reflection signal, was selected, and a matching decomposition of each signal was performed so that the weak target signal submerged in the strong reflection was displayed more strongly. Second, the Hilbert transform was used to extract multiple parameters, such as the instantaneous amplitude, the instantaneous frequency, and the instantaneous phase, from the processed signal, and the ground penetrating radar (GPR) image was comprehensively analyzed and determined from multiple angles. The results show that the OMHT method can accurately weaken the effect of the strong impedance interface and effectively enhance the weak reflected signal energy of hidden micro-crack in the shield tunnel segment. The resolution of the processed GPR image is greatly improved, and the reflected signal of the hidden micro-crack is easily visible, which proves the validity and accuracy of the analysis method.
Key words: orthogonal matching pursuit; Hilbert transform; shield tunnel; lining structure; hidden micro-crack
Cite this article as: LING Tong-hua, ZHANG Liang, HUANG Fu, GU Dan-ping, YU Bin, ZHANG Sheng. OMHT method for weak signal processing of GPR and its application in identification of concrete micro-crack [J]. Journal of Central South University, 2019, 26(11): 3057−3065. DOI: https://doi.org/10.1007/s11771-019-4236-y.
part of the shield tunnel support system, the quality 1 Introduction of the lining segment is directly related to the overall quality and safety of the tunnel. However, Because of the advantages of low impact on because of factors such as design, construction the surrounding environment, a wide range of technology, and cyclic load, segments of shield adaptability, safety and rapid construction, shield tunnels commonly exhibit defects, e.g., varying tunnel construction is increasingly favored in the degrees of cracks and water leakage. Particularly, infrastructures of various countries. As an important when hidden cracks appear in the lining structure,
Foundation item: Projects(51678071, 51608183) supported by the National Natural Science Foundation of China; Projects(CX2018B530, CX2018B531) supported by the Postgraduate Research and Innovation-funded Project of Hunan Province, China; Projects(16BCX13, 16BCX09) supported by Changsha University of Science and Technology, China Received date: 2018-07-24; Accepted date: 2019-07-04 Corresponding author: ZHANG Liang, Doctoral Candidate; Tel: +86-18974934275; E-mail: [email protected]; ORCID: 0000- 0002-7119-9315 3058 J. Cent. South Univ. (2019) 26: 3057−3065 they are more serious and difficult to address Most signals in nature (such as GPR signals) are because they are concealed, imperceptible and thus usually not sparse; therefore, they must be sparsely undetectable. The timely detection of concealed represented. Sparse representation means using a quality defects in lining structures is of great linear combination of notably few elements in a significance for maintaining the safety of tunnel dictionary to represent a signal. For example, signal engineering. x can be represented by the superposition of S basic Scholars have conducted much meaningful signal atoms φ as follows: s work in the detection and identification of typical S harmful geologic bodies. The research on the xDass (1) quality defects of shield tunnel lining mainly s1 focuses on the detection of the void and where a=[α1, α2, …, αs] is the sparse representation grouting-backfilling [1−6], and there is little coefficient of x in dictionary D=[φ1, φ2, …, φs]. In reference to the detection and recognition of the discrete-time and finite-length signal processing, a lining cracks, particularly the hidden micro-cracks dictionary is a matrix with dimensions N×S with
(width=0.5 mm). In the field of underground atomic φs as a column vector. When the number of engineering, the main research objects of cracking columns in the dictionary is greater than the number are rock and rock-like materials [7, 8]. Since the of rows (i.e., S>N), the dictionary is an micro-crack size is too small, the reflection signal is overcomplete dictionary. too weak, and the high- frequency electromagnetic 2.1.2 Basic principle of matching pursuit algorithm wave of the GPR is easily attenuated and dispersed The matching pursuit algorithm adaptively during the propagation process, it is notably decomposes the signal by decomposing the signal difficult to identify and locate the hidden cracks in in an overcomplete time-frequency atomic library the shield tunnel lining. (dictionary D); hence, the signal can be expressed In this work, a signal analysis method that as a linear combination of the matched time- combines the orthogonal matching pursuit with the frequency atoms [12]. A dictionary is a collection of Hilbert transform (OMHT method) is proposed and a series of time-frequency atoms; the commonly applied to identify micro-cracks in concrete used atomic library categories are Gabor atoms, structures. In the OMHT method, the original GPR chirplet atoms, and Ricker wavelet atoms. The image of the concrete with hidden water-filled specific mathematical principle of the classic micro-crack is processed to remove the strong reflection interference, which causes the weak matching pursuit algorithm is then expressed as follows [13−15]: signal submerged in the strong reflection to emerge. The processed energy-enhanced image is handled aDaxˆ=argmin 2 s.t. a (2) 2 0 by the Hilbert transform. Then, from the obtained where aˆ is the sparse representation coefficient of three-parameter diagrams of instantaneous the original signal; x is the GPR signal; δ is the amplitude, instantaneous phase and instantaneous sparse constraint coefficient in the matching pursuit frequency, the abnormal reflection signals of the algorithm; D is the sparse dictionary. hidden water micro-crack in the middle of the image can be clearly observed, which verifies the The matching pursuit algorithm reduces the effectiveness of the method in the identification of computational complexity using greedy techniques. hidden micro-cracks. It is an iterative algorithm that adopts the inner product as the correlation metric and selects the 2 Theories and algorithms atom most related to the residual signal in each iteration from the dictionary. Performing this action 2.1 Sparse representation theory and matching at each iteration increases the optimization of the pursuit algorithm approximation of signal x. The specific steps of the 2.1.1 Sparse representation of signals algorithm are as follows:
A signal that can be expressed as x=[x1, x2, …, 1) Initialization: Set the initial residual signal T 0 xN] , if there are only n (n< J. Cent. South Univ. (2019) 26: 3057−3065 3059 2) Inner product coefficient calculation: [16, 17]. Calculate the coefficient of the inner product of the Let the input signal be g(t), and the output 1 residual signal R x and all atoms in the dictionary, signal be expressed as g()t after the filtering of i i.e., Rx,; Hilbert frequency response of H(w). The Hilbert transform formula is as follows: 3) Inner product maximization: Maximize the inner product i arg max and record the 1 gt() gt () (5) t atomic subscript γi and the inner product coefficient αγ ; The GPR signals are represented as follows: i 4) Residual signal updating: Update the 1 residual signal using R=Ri+1xxi α φ ; ut() gt () igt () gt () igt () (6) γγii t 5) Rule for stopping the iterations: If the Formula (6) is the complex signal residual signal energy is less than the given representation of the real GPR signal g(t). i+1 2 threshold ξstop, i.e., R x stop , then stop The following feature parameter estimations iterating. Otherwise, let i=i+1 and return to Step 2. are defined from Formula (6) as follows: After N iterations, the signal x can be sparsely a) Instantaneous amplitude estimation: N 1 N decomposed as xxα φ R.When N 2 2 γγii A()tgtgt () () (7) i0 approaches infinity, RN x exponentially converges The instantaneous amplitude A(t) has a positive correlation with the square root of the total as limRN x 0 in the finite-dimensional signal N energy of the reflected signal at a given moment; space. The signal is then represented as follows: the variation of the correlation magnitude is related to the signal propagation distance, the dielectric x αφ (3) γγii constant difference, etc. i0 b) Instantaneous phase estimation: One of the keys to successfully removing the g()t strong reflection interference using the orthogonal (t ) arctan (8) g()t matching pursuit is the selection of the sparse dictionary D. If the dictionary is not properly The instantaneous phase reflects the continuity selected, the strong reflection signal cannot be of events. The phase will change at an abnormal effectively represented sparsely. The dictionary is position of the signal. The events may be composed of wavelet atoms. Currently, the most discontinuous when the phase change is severe. widely used wavelet types are the Morlet wavelet c) Instantaneous frequency estimation: and the Ricker wavelet. Since the Ricker wavelet d() tgt d1 () has the benefits of a simple form, a short time delay, ()t tan (9) ddtt gt () fast convergence, and high resolution in the reflection of weak signals, we select the Ricker The instantaneous frequency is the time wavelet to build the wavelet library. The time changing rate of the phase, which can reflect the domain expression of the Ricker wavelet is as physical property change of formation. follows: 222 222 3 Indoor test of detection of hidden w(t , fjj ) (1 2π f t )exp( f j t ) (4) micro-crack in concrete using GPR where w(,t fj ) is the Ricker wavelet; t is time; fj is the dominant frequency of the wavelet. 3.1 Indoor model pouring and electromagnetic wave velocity calibrating 2.2 Hilbert transform The shield tunnel lining is generally 0.3−0.4 m Independent parameter profiles such as the thickness [18−20]; therefore, the concrete model is instantaneous amplitude, instantaneous phase and designed to be h=0.3 m thick. Both the length and instantaneous frequency of the real GPR signal are the width of the concrete model are 0.6 m. The obtained after processing by the Hilbert transform sizes of the concrete model are 0.6 m×0.6 m×0.3 m. 3060 J. Cent. South Univ. (2019) 26: 3057−3065 According to Refs. [21−23], the impact of the crack the vibrator is used to level the surface and remove width in the reinforced-concrete lining on the shield air. A tagger with sizes of length×width× tunnel is divided into four grades. When the thickness=0.45 m×0.18 m×0.50 mm is vertically micro-crack width in the reinforced concrete lining inserted into the concrete model along the thickness is greater than or equal to 0.5 mm, the cracks direction to form a micro-crack defect with an seriously affect the health of the shield tunnels. We insertion depth of 0.25 m. After 2 d of maintenance, only detect and identify the critical crack defects use a jack to pull out the tagger, and then continue with serious impact on the tunnel operation; to maintain the model for 26 d. A flowchart for the therefore, the micro-cracks are designed to be complete pouring process of the concrete model is 0.5 mm wide. In the actual process of making crack, shown in Figure 2. The pouring process of the the micro-cracks are manufactured by inserting and concrete model is shown in Figure 3 and the extracting the tagger. The effect of the pulling force concrete model with a micro-crack after 28 d of and the swinging of the tagger will slightly increase curing is illustrated in Figure 4. the crack width when the tagger is pulled out. The electromagnetic wave velocity of the Therefore, the actual crack width is 0.6−0.7 mm. concrete model is calibrated using the GPR in the Table 1 shows the relevant parameters of the thickness direction of the concrete model after the concrete model, and Figure 1 shows the schematic model finished maintaining. The calibrated diagram of the model. electromagnetic wave velocity of the concrete The entire model is poured at once, and then, model is 0.09682 m/ns. Table 1 Concrete model parameters Strength grade Cement grade Concrete model size Micro-crack size Position of micro-crack Thickness×length×width Width×length×depth= Parallel to the left and right main C40 P.O 42.5 =0.3 m×0.6 m×0.6 m (0.6−0.7 mm)×18 cm×25 cm faces of the concrete model Figure 1 Schematic diagram of concrete model with cracks (Unit: mm) J. Cent. South Univ. (2019) 26: 3057−3065 3061 the center of the antenna is approximately 8 cm from the upper boundary of the model. The detection process is illustrated in Figure 5, and the original GPR image is shown in Figure 6. Figure 5 Test of GPR on a concrete model with a hidden micro-crack Figure 2 Flowchart of complete pouring process of concrete model Figure 6 Original image of GPR for concrete with hidden water-filled micro-crack Figure 3 Model construction process 4 Application of OMHT method in identification of concrete micro-crack The implementation of the OMHT method consists of two steps: the orthogonal matching pursuit and the Hilbert transform. First, the original GPR image is processed by orthogonal matching pursuit to remove the strong reflection interference. Figure 4 Plain concrete model with a micro-crack Then, the resulting signal, with the removal of strong reflection, is processed using a Hilbert 3.2 GPR micro-crack detection test transform to extract the three-parameter diagrams of High-frequency GPR is used to detect the the instantaneous amplitude, instantaneous phase hidden water micro-crack. The radar is an Italian and instantaneous frequency. With the three- RIS-K2 with an antenna dominant frequency of parameter diagrams, the abnormal reflection signals 1600 MHz, and the measurement mode is the wheel of the hidden water micro-crack in the middle of the test. The walking direction of the antenna is parallel image can be observed, and the GPR images are to the upper boundary of the concrete model, and clearer and more continuous. 3062 J. Cent. South Univ. (2019) 26: 3057−3065 4.1 Target signal enhancement based on orthogonal matching pursuit It is notably difficult to discern the hidden micro-crack in concrete from the GPR original image shown in Figure 6. Because strong energy reflection events occur on the concrete surface, the reflected signal of the weak object in the lower part of the image is submerged in the strong reflection, causing the weak signal of the hidden micro-crack to be difficult to recognize. The dominant frequency of the strong reflection interference in the concrete surface is calculated using the Hilbert transform, and the Figure 8 Comparison of root mean square(RMS) Ricker wavelet is expanded into a dictionary amplitude curves in strong reflection region before and according to the characteristics of the original after removal of strong reflection signal, then the original signal is processed using the method of orthogonal matching pursuit to matching pursuit, which indicates that the remove the strong reflection interference. The interference signal of the strong reflection region processed image is shown in Figure 7. has been effectively separated. Then, the energy percentage of the signals for 10 sampling points below the strong reflection layer to the bottom of the GPR image is separately extracted from Figures 6 and 7 to analyze the highlighting effect of the weak signal after the strong reflection signal has been removed. The change in energy percentage of the weak signal area is shown in Figure 9. The energy of the weak signal region is significantly improved and the weak signal is effectively enhanced after the orthogonal matching pursuit, thereby assisting in the detection Figure 7 GPR image of concrete with a hidden micro- and recognition of the weak object in the GPR crack after strong reflection has been removed image. The above analysis shows that the energy of In Figure 7, the weak signal, which was the weak signal in the image is effectively improved submerged in the strong reflection, appears after the after the orthogonal matching pursuit process, and strong reflection cophase signals are separated. However, it is not persuasive only from the image profile. To quantitatively describe the performance of the weak signal after the strong reflection interference is removed, the following procedure is applied. First, the root mean square attributes of the signals for 10 sampling points above and 10 sampling points below the strong reflection layer in Figure 6 are determined. Second, the signals at the corresponding positions in Figure 7 are extracted to analyze the effect before and after the removal of the strong reflection signal, as shown in Figure 8. In this figure, the energy of the strong reflection Figure 9 Energy ratio of faint signal before and after region significantly decreases after the orthogonal removal of strong reflection J. Cent. South Univ. (2019) 26: 3057−3065 3063 the abnormal reflection signal is found at approximately 5 ns in Figure 7 (shown by the red frame in the figure). This shows that the elimination of the strong reflection has a certain highlighting effect on the display of weak signals at the bottom. Because the micro-crack is thin, the weak signal cannot be readily observed, and the degree of recognition of an abnormal body is not high. It remains difficult to determine the presence of tiny defects in the concrete from Figure 7. 4.2 High-resolution image enhancement based on Hilbert transform After the orthogonal matching pursuit, the GPR image is processed using the Hilbert transform to extract parameters such as the instantaneous amplitude, instantaneous frequency and instantaneous phase, thus providing accurate analyses and judgments for the detected image. The results of the Hilbert Transform are illustrated in Figure 10. In Figure 10(a), the instantaneous amplitude spectrum of the GPR recording is shown. In this figure, there is a long strip amplitude anomaly, which is approximately identical to the distribution of the micro-crack at 5 ns (circled in red in the figure). According to the calibrated electromagnetic wave velocity, the calculated distance between the long strip wave anomaly and the side of the model is h=0.202 m, which coincides with the actual distance H=0.20 m of the hidden micro-crack, and it can be judged that the amplitude anomaly is the GPR reflection signal of the hidden micro-crack in the concrete. The instantaneous phase spectrum in Figure 10(b) shows a corresponding long strip phase anomaly at 5 ns. Compared with Figure 10(a), showing the instantaneous amplitude, the instantaneous phase diagram is clearer and more intuitive, and the left and right wings of the anomalous signal, which are caused by the reflection of the micro-crack boundary, are also readily observed. Figure 10(c) shows the instantaneous frequency spectrum of the GPR image; this diagram has a lower resolution than the instantaneous amplitude and instantaneous phase diagrams. However, a regular mutation of frequency exists at the position of 5 ns, which is Figure 10 Results of Hilbert Transform for GPR image: caused by the variation of the physical properties of (a) Instantaneous amplitude; (b) Instantaneous phase; concrete and crack. (c) Instantaneous frequency 3064 J. Cent. South Univ. (2019) 26: 3057−3065 The Hilbert transform method is used to 2293−2300. DOI: 10.19713/j.cnki.43-1423/u.2018.09.016. separate the instantaneous amplitude, instantaneous (in Chinese) phase and instantaneous frequency from the GPR [3] NOBES D C. Ground penetrating radar response from voids: A demonstration using a simple model [J]. Ndt & E signal to obtain the three-parameter information of International, 2017, 91: 47−53. DOI: 10.1016/j.ndteint.2017. the same detection profile. Instantaneous amplitude 05.007. spectrum and instantaneous phase spectrum can [4] CASSIDY N J, EDDIES R, DODS S. Void detection beneath intuitively reflect the change of physical parameters reinforced concrete sections: The practical application of of detection targets. The instantaneous frequency ground-penetrating radar and ultrasonic techniques [J]. Journal of Applied Geophysics, 2011, 74(4): 263−276. DOI: diagram is relatively low in resolution. The 10.1016/j.jappgeo.2011.06.003. resolution and interpretation reliability of GPR [5] KILIC G, EREN L. Neural network based inspection of images are improved by the mutual verification of voids and karst conduits in hydro–electric power station multiple parameter profiles. tunnels using GPR [J]. Journal of Applied Geophysics, 2018, 151: 194−204. DOI: 10.1016/j.jappgeo.2018.02.026. [6] LAI W L, DÉROBERT X, ANNAN P. A review of ground 5 Conclusions penetrating radar application in civil engineering: A 30-year journey from locating and testing to imaging and diagnosis A weak signal processing method for the [J]. Ndt & E International, 2018, 96: 58−78. DOI: GPR—OMHT method is introduced and applied to 10.1016/j.ndteint. 2017.04.002. detect and identify hidden micro-crack in concrete. [7] ZHOU X P, XIA E M, YANG H Q, QIAN Q H. Different crack sizes analyzed for surrounding rock mass around The main conclusions are as follows: underground caverns in Jinping I hydropower station [J]. 1) The OMHT method effectively reduces the Theoretical and Applied Fracture Mechanics, 2012, 57(1): shielding effect of strong reflection interference on 19−30. DOI: 10.1016/j.tafmec.2011.12.004. the weak signal and yields the three-parameter [8] ZHOU X P, LIAN Y J, WONG L N Y, BERTO F. information of a detection profile: instantaneous Understanding the fracture behavior of brittle and ductile amplitude, instantaneous phase and instantaneous multi-flawed rocks by uniaxial loading by digital image correlation [J]. Engineering Fracture Mechanics, 2018, 199: frequency. Using the three-parameter diagrams, the 438−460. DOI: 10.1016/j.engfracmech.2018.06.007. abnormal reflection signals of the hidden water [9] REBOLLO-NEIRA L. Cooperative greedy pursuit strategies micro-crack in the middle of the image are readily for sparse signal representation by partitioning [M]. Elsevier observed, and the GPR images are clearer and more North-Holland, Inc. 2016. continuous. [10] WANG Jun, ZHOU Si-chao, XIA Li-min. Human interaction recognition based on sparse representation of feature 2) The OMHT method for GPR weak-signal covariance matrices [J]. Journal of Central South University, processing readily improves the image resolution 2018, 25(2): 304−314. DOI: 10.1007/s11771-018-3738-3. and can identify the micro-crack defect in concrete [11] TANG Jing-tian, LI Guang, ZHOU Cong, LI Jin, LIU Xiao- structures. qiong, ZHU Hui-jie. Power-line interference suppression of 3) The OMHT method can analyze detection MT data based on frequency domain sparse decomposition signals with low signal-to-noise ratio. It also has [J]. Journal of Central South University, 2018, 25(9): 2150−2163. DOI: 10.1007/s11771- 018-3904-7. obvious advantages in eliminating strong reflection [12] MALLAT S G, ZHANG Z. Matching pursuits with interference and improving image resolution. It can time-frequency dictionaries [J]. IEEE Trans on Signal be used as a supplement for the GPR data Processing, 1993, 41(12): 3397−3415. DOI: 10.1109/78. processing. 258082. [13] GHOFRANI S, AMIN M G, ZHANG Y D. High-resolution direction finding of non-stationary signals using matching References pursuit [J]. Signal Processing, 2013, 93(12): 3466−3478. DOI: 10.1016/j.sigpro.2013.03. 016. [1] LIU Hao-ran, LING Tong-hua, LI Di-yuan, HUANG Fu, [14] ZHANG Zhao-heng, DING Jian-ming, WU Chao, LIU ZHANG Liang. A quantitative analysis method for GPR Jian-hui. Impulsive component extraction using shift- signals based on optimal biorthogonal wavelet [J]. Journal of invariant dictionary learning and its application to gear-box Central South University, 2018, 25(4): 879−891. DOI: bearing early fault diagnosis [J]. Journal of Central South 10.1007/s11771- 018-3791-y. University, 2019, 26(4): 824−838. DOI: [2] LING Tong-hua, ZHANG Liang, GU Dan-ping, LIU 10.1007/s11771-019-4052-4. Hao-ran, CAO Feng. Analysis of cracking mechanism and [15] STUDER D, HOFFMANN U, KOENIG T. From EEG bearing capacity of shield tunnel segment with voids behind dependency multichannel matching pursuit to sparse [J]. Journal of Railway Science and Engineering, 2018,15(9): topographic EEG decomposition [J]. Journal of J. Cent. South Univ. (2019) 26: 3057−3065 3065 Neuroscience Methods, 2006, 153(2): 261−275. DOI: [20] KATEBI H, REZAEI A H, HAJIALILUE-BONAB M, 10.1016/j.jneumeth.2005.11.006. TARIFARD A. Assessment the influence of ground [16] YU Zhi-xiong, XUE Gui-yu, ZHOU Chuang-bing. stratification, tunnel and surface buildings specifications on Application of complex signal analysis to process ground shield tunnel lining loads (by FEM) [J]. Tunnelling and penetrating radar data [J]. Chinese Journal of Rock Underground Space Technology incorporating Trenchless Mechanics & Engineering, 2005, 24(5): 798−802. DOI: Technology Research, 2015, 49: 67−78. DOI: 10.3321/j.issn:1000-6915.2005. 05.011. (in Chinese) 10.1016/j.tust.2015.04.004. [17] SAHOO S, BISWAL P, DAS T, SABUT S. De-noising of [21] LI Biao. Structure of mining tunnel detection and evaluation ECG signal and QRS detection using hilbert transform and of the service performance [D]. Chongqing: Chongqing adaptive thresholding [J]. Procedia Technology, 2016, 25: Jiaotong University, 2014. (in Chinese) 68−75. DOI: 10.1016/j.protcy.2016.08.082. [22] LAI Jin-xing, QIU Jun-ling, PAN Yun-peng, CAO Xiao-jun, [18] SCHOTTE K, NAGY W, NUTTENS T, WULF A D, LIU Chi, FAN Hao-bo. Comprehensive monitoring and BOGAERT P V. Impact of tidal level fluctuations on the analysis of segment cracking in shield tunnels [J]. Modern structural behaviour of a segmental tunnel lining [J]. Tunnelling Technology, 2015, 52(2): 186−191. DOI: Tunnelling & Underground Space Technology, 2017, 64: 10.13807/j.cnki.mtt.2015.02.028. (in Chinese) 184−208. DOI: 10.1016/j.tust. 2017.01.014. [23] LI C Q, BAJI H, MAHMOODIAN M, YANG W. Risk-cost [19] YANG Chun-shan. Influence of construction when the tunnel optimized maintenance strategy for tunnel structures [C]// is first excavation followed by working well on tunnel International Conference on Smart Infrastructure and deformation and control countermeasures [D]. Guangzhou: Construction. 2016. South China University of Technology, 2015. (in Chinese) (Edited by YANG Hua) 中文导读 地质雷达弱信号处理 OMHT 法及其在混凝土微裂缝识别中的应用 摘要：针对混凝土表面强反射等原因造成的异常强反射信号屏蔽下部微弱目的信号的问题，采用正交 贪婪匹配追踪与希尔伯特变换相结合的方法(OMHT 法)进行了盾构隧道衬砌隐伏微裂缝的检测与识 别。首先，依据匹配追踪算法和强反射形成机理，基于稀疏表示理论，选取了与强反射信号特征相适 应的稀疏字典，对每道信号进行匹配分解处理，使淹没于强反射中的微弱目标体信号得到较好的展示。 其次，对处理后的信号进行希尔伯特变换提取雷达剖面瞬时振幅、瞬时频率和瞬时相位等多个参数信 息，从多个角度对地质雷达图像进行综合分析和判断。结果表明，OMHT 法可精准弱化强阻抗界面的 影响，并有效增强管片隐伏微裂缝弱反射信号能量。处理后的地质雷达图像分辨率明显提高，隐伏微 裂缝反射信号清晰可见，证明了该分析技术对隐伏微裂缝识别的有效性和准确性。 关键词：正交匹配追踪；希尔伯特变换；盾构隧道；衬砌结构；隐伏微裂缝