Forward modelling on GPR responses of subsurface air voids

Luo, Tess X.H.; Lai, Wallace Wai-Lok; Giannopoulos, Antonios

doi:10.1016/j.tust.2020.103521

Cited by 35 publications

(7 citation statements)

References 23 publications

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“…Thus, it can be seen that the response mode of the void is closely related to the width; most notably, the response area at the top of the void and the form of the reflected signal at the edge. This result was also observed by Luo et al [39] through forward simulation and experiments. The reason for this change is the different ratios of void spread to the radar footprint.…”

Section: Influence Of Void Widthsupporting

confidence: 86%

Evaluation of Void Defects behind Tunnel Lining through GPR forward Simulation

Bao

Shen

et al. 2022

Sensors

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Voids, a common defect in tunnel construction, lead to the deterioration of the lining structure and reduce the safety of tunnels. In this study, ground-penetrating radar (GPR) was used in tunnel lining void detection. Based on the finite difference time domain (FDTD) method, a forward model was established to simulate the process of tunnel lining void detection. The area of the forward image and the actual void area was analyzed based on the binarization method. Both the plain concrete and reinforced concrete lining with various sizes of air-filled and water-filled voids were considered. The rationality of the model was verified by measured data. It was observed that the response mode of voids can be hyperbolic, bowl-shaped, and strip-shaped, and this depends on the void’s width. Compared with the air-filled voids, water filling increases the response range of the voids and produces a virtual image. Although the diffracted wave caused by a steel bar will bring about significant interference to the void response, the center position of the voids can be accurately located using 3D GPR.

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Section: Influence Of Void Widthsupporting

confidence: 86%

Evaluation of Void Defects behind Tunnel Lining through GPR forward Simulation

Bao

Shen

et al. 2022

Sensors

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“…Currently, common forward numerical simulation methods for GPR include the finite element method, method of moments, and finite‐difference time domain (FDTD) method. The FDTD method is the most important numerical simulation method for solving EM field problems and has wide applicability (Davidson, 2010; Giannopoulos, 2005; Li et al., 2019; Luo et al., 2020; Soldovieri et al., 2007). Simultaneously, this method is advantageous in that it saves calculation cost and is simple and intuitive (Giannopoulos, 2005; Yang et al., 2021).…”

Section: Methodsmentioning

confidence: 99%

GPR velocity correction method in transversely heterogeneous media based on CMP data

Ling,

Qian,

Zhang

et al. 2023

Near Surface Geophysics

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Determining of ground‐penetrating radar (GPR) velocity has always been a critical problem. The GPR velocity estimation method based on common midpoint (CMP) data has been widely used because of its simplicity. However, we found that in sediment investigation and soil assessment, transversal heterogeneity is universal, which violates the basic assumption of velocity estimation through CMP data. Due to the rapid change of underground media and the limitation of the scope of some surveying areas, the CMP survey line will inevitably be selected in the area where the velocity changes laterally, making it difficult to obtain accurate velocity. To address this problem, we propose a velocity correction method. First, we determined the characteristics of CMP data and the corresponding velocity spectra acquired in transversely heterogeneous media through numerical simulations. Subsequently, we found that the simulated CMP data could determine the location of changes in the underground medium and that the velocity obtained from the semblance analysis could be corrected according to the location where the medium changes laterally. We then used models wherein the thickness, relative permittivity, and proportion of abnormal parts varied independently or simultaneously to verify the proposed velocity correction method. The results show that our method can control the GPR velocity error within 3.44% and the precision is about 0.002 m/ns. Finally, we conducted a sediment investigation experiment on a channel bar in the lower reaches of the Yarlung Zangbo River. We determined the interface at which the sediment changed transversely and obtained the corresponding electromagnetic (EM) velocity using the proposed method. This study provides a reliable method for determining the GPR velocity in transversal heterogeneous media, which is of great significance for various practical applications.This article is protected by copyright. All rights reserved

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“…We used gprMax (Giannopoulos, 2005;Warren et al, 2016) simulator that solves Maxwell's equations, either in 2D or three-dimensional (3D) space, using the finite-difference time-domain method (Taflove & Hagness, 2005). The software incorporates many advanced modelling features and has become the de facto simulation tool for GPR, having been used successfully over many years by many researchers in academia and industry (Diamanti et al, 2017;Angelis et al, 2018;Jonard et al, 2019;Alani et al, 2020;Hamran et al, 2020;Luo et al, 2020;Giannakis et al, 2021).…”

Section: Synthetic Datamentioning

confidence: 99%

The effects of receiver arrangement on velocity analysis with multi‐concurrent receiver GPR data

Angelis

Warren

Diamanti

et al. 2022

Near Surface Geophysics

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Determining subsurface electromagnetic (EM) wave velocity is critical for Ground-penetrating radar (GPR) data analysis, as velocity is used for the time-to-depth conversion, and hence leads to obtaining the precise location of the objects of interest. Currently, the way to acquire detailed subsurface EM wave velocity models involves employing multi-offset GPR surveys, such as wideangle reflection-refraction (WARR), in conjunction with normal moveout (NMO) based velocity analysis. Traditionally, these surveys are carried out using two separate transducers and were therefore time-consuming and had limited uptake. Recent advances in GPR hardware have allowed the development of novel systems with multi-concurrent sampling receivers, which enable rapid and dense acquisition of WARR data. These additional receivers increase the overall size, weight, and cost of the system. Therefore, we investigated the effects of receiver arrangement on NMO-based velocity analysis, and considered reducing the overall number of transducers, whilst maintaining satisfactory velocity spectra resolution, and hence, obtaining detailed stacking velocity models as well as improved stacked reflection sections. We used both simulated data from complex threedimensional (3D) models as well as field data and examined different numbers and positions of receivers in different environments. Our results show that velocity spectra resolution can be maintained within acceptable limits whilst reducing the number of receivers from a configuration with seven equally spaced receivers, to a sparse configuration of four receivers. Thus, being able to decrease the number of receivers used by these new GPR systems will reduce both the total system weight and cost, and hopefully, increase their adoption for GPR surveys.

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Forward modelling on GPR responses of subsurface air voids

Cited by 35 publications

References 23 publications

Evaluation of Void Defects behind Tunnel Lining through GPR forward Simulation

Evaluation of Void Defects behind Tunnel Lining through GPR forward Simulation

GPR velocity correction method in transversely heterogeneous media based on CMP data

The effects of receiver arrangement on velocity analysis with multi‐concurrent receiver GPR data

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