Object Recognition of Ground Penetrating Radar in Karst Regions Using Wavelet Energy Spectral Analysis

Li, Cai‐Ming; Wang, Liangshu; Xu, Mingjie; Yuan-sheng, Liu; Zhang, Shan‐Fa

doi:10.1002/cjg2.958

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The simulation of the FDTD method was used to assist the map interpretation of GPR, which ensured the construction quality of the metro tunnel and reduced the potential operational risk [14]. Fourier time-frequency analysis [15][16], wavelet analysis [17][18], and Hilbert-Huang transform [19] have been proposed to analyze GPR detection data, and many feasible ideas have also been introduced.…”

Section: State Of the Artmentioning

confidence: 99%

Wavelet Packet Analysis of Ground–Penetrating Radar Simulated Signal for Tunnel Cavity Fillings

Zhang¹,

Li²,

Fu³

et al. 2018

JESTR

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Quality defects, such as noncompactness, cavity, and water leakage, typically appear behind tunnel linings given the lack of standardization and loose management in tunnel construction. Thus, a method for acquiring characteristic information from tunnel lining cavity fillings using frequency band energy of wavelet packet transform (FBEWPT) was proposed in this study to master the reflection characteristics of ground-penetrating radar (GPR) signal. A forward simulation of tunnel lining cavity fillings, including air, wet clay, silt, and water, was explored through finite-difference time-domain method. The spectrum characteristics of tunnel cavity fillings were analyzed from time, frequency, and time-frequency domains. Results demonstrate that amplitude reflection clearly occurs at the inhomogeneous interface when GPR electromagnetic waves collide with the tunnel lining cavity fillings. However, the degree of attenuations is different considering the various dielectric constants of objects. These relative dielectric constants reveal the reflection interface position and other information between lining and fillings. With the increase in the difference in relative dielectric constants for lining cavity fillings, the attenuation of FBEWPT for GPR signals increasingly strengthens, and the variance and standard deviation of the FBEWPT for GPR signals constantly decrease, thereby reflecting the strong reflection and attenuation of GPR signals that appear in the propagation process. This study provides a favourable reference for obtaining GPR signal spectrum interpretation for tunnel lining cavity fillings in actual projects.

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Section: State Of the Artmentioning

confidence: 99%

Wavelet Packet Analysis of Ground–Penetrating Radar Simulated Signal for Tunnel Cavity Fillings

Zhang¹,

Li²,

Fu³

et al. 2018

JESTR

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“…For the vicinity of the tunnel working surface that is a narrow detection space including busy construction operations, GPR is the best detection equipment for unfavorable geological conditions, such as karst caves, faults, joint fissures, weak fracture zones, and water-bearing structures. GPR has been employed in the advanced geological prediction and detection of tunnels [35,36]. From research literature and data, we determine that the detection personnel generally process the GPR signals by using the algorithms of static correction, gain, offset, and filtering included in the GPR software and then analyzed the amplitude, phase, and frequency changes in the GPR profile to infer and explain unfavorable geological bodies [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Quantitative Recognition of Filler Materials Ahead of a Tunnel Face via Time-Energy Density Analysis of Wavelet Transforms

Zhang

et al. 2022

Minerals

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Advanced geological prediction of tunnels has become an indispensable task to ensure the safety and effectiveness of tunnel construction before excavation in karst areas. Geological disasters caused by unfavorable geological conditions, such as karst caves, faults, and broken zones ahead of a tunnel face, are highly sudden and destructive. Determining how to predict the spatial location and geometric size of unfavorable geological bodies accurately is a challenging problem. In order to facilitate a three-dimensional quantitative analysis of the filler material ahead of the tunnel face, a biorthogonal wavelet with short support, linear phase, and highly matching waveform of ground penetrating radar (GPR) wavelet is constructed by lifting a simple and general initial filter on the basis of lifting wavelet theory. A method for a time–energy density analysis of wavelet transforms (TEDAWT) is proposed in accordance with the biorthogonal wavelet. Fifteen longitudinal and horizontal survey lines are used to detect void fillers of different heights. Then, static correction, DC bias, gain, band-pass filtering, and offset processing are performed in the original GPR profile to enhance reflected signals and converge diffraction signals. A slice map of GPR profile is generated in accordance with the relative position of longitudinal and horizontal survey lines in space. The wavelet transform analysis of a single-channel signal of each survey line is performed by adopting the TEDAWT method because of the similar rule of the single-channel signal of GPR on the waveform overlay and the ability of the constructed wavelet basis to highlight the time-frequency characteristics of GPR signals. The characteristic value points of the first and second interfaces of the void fillers can be clearly determined, and the three-dimensional spatial position and geometric sizes of different void fillers can be obtained. Therefore, the three-dimensional visualization of GPR data is realized. Results show that the TEDAWT method has a good practical application effect in the quantitative identification of void fillers, which provides a basis for the interpretation of advanced geological prediction data of tunnels and for the construction decision.

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“…The disadvantage of this method is that when various targets exist, the mutual interference between target echo waves will considerably affect the classification results, and the needed target signals will entail higher requirements in signal to noise ratio (SNR) and energy loss. With the development of image processing technology, the main methods currently adopted are image processing methods, such as neural network [3], wavelet analysis [4], template matching [5] and other pattern recognition methods such as support vector machine (SVM) [8], target identification methods based on image formation [6], and methods based on signal energy detection [7]. Only the image processing methods with numerous training samples can yield better results.…”

Section: Introductionmentioning

confidence: 99%

A New Underground Target Identification Method Based on Ground Penetrating Radar Map

Lian

2011

AMM

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In this paper, we proposed a punctuated target identification method for underground homogeneous medium based on dielectric constant inversion algorithm. Its main idea is that, initially, Hough transform is used to locate the target characterized by hyperbola in a radar profile map, then a layer-by-layer waveform inversion algorithm is used to invert the dielectric constant on the location where the target lies. To guarantee the correctness of inversion, the transmission beam method is adopted to obtain initial parameters and calibration factors required by inversion. Through numerical analysis of the dielectric constant of the target, the classification of multiple targets in an underground medium can be confirmed. This method not only overcomes the failure of the traditional phase-comparison method to distinguish different kinds of targets in the same region, but also overcomes the limitation of the image processing method, in which it only classifies the target in a coarse-grained manner. Experimental results show that this method has many advantages, such as fine-grained classification, high precision, and multiple target identification, in the identification of underground targets.

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Object Recognition of Ground Penetrating Radar in Karst Regions Using Wavelet Energy Spectral Analysis

Cited by 6 publications

References 11 publications

Wavelet Packet Analysis of Ground–Penetrating Radar Simulated Signal for Tunnel Cavity Fillings

Wavelet Packet Analysis of Ground–Penetrating Radar Simulated Signal for Tunnel Cavity Fillings

Three-Dimensional Quantitative Recognition of Filler Materials Ahead of a Tunnel Face via Time-Energy Density Analysis of Wavelet Transforms

A New Underground Target Identification Method Based on Ground Penetrating Radar Map

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