3-D GPR visualization technique integrated with electric resistivity tomography for characterizing near-surface fractures and cavities in limestone

Gaballah, Mahmoud; Alharbi, T.

doi:10.1080/16583655.2022.2040242

Cited by 8 publications

(2 citation statements)

References 36 publications

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“…Typically, this involves the GPR technique as Considering that the geophysical response of complex geological bodies exhibits different physical properties due to the fact that such horizons are composed of a combination of layers with contrasting properties and are caused by a host of geological, geotechnical, and climatic factors, a multidisciplinary approach is often recommended to gather an in-depth understanding of the features. Typically, this involves the GPR technique as the primary method to determine the velocity and geometrical attributes of the subsur-face, while electrical resistivity tomography and seismic reflection and refraction often work as complementary methods [302][303][304][305][306][307][308][309], due to their limited resolutions and mediocre stratum-detection performance, in particular for the shallow subsurface.…”

Section: Gpr Applications In Geologymentioning

confidence: 99%

From Its Core to the Niche: Insights from GPR Applications

2022

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Thanks to its non-destructive, high-resolution imaging possibilities and its sensitivity to both conductive and dielectric subsurface structures, Ground-Penetrating Radar (GPR) has become a widely recognized near-surface geophysical tool, routinely adopted in a wide variety of disciplines. Since its first development almost 100 years ago, the domain in which the methodology has been successfully deployed has significantly expanded from ice sounding and environmental studies to precision agriculture and infrastructure monitoring. While such expansion has been clearly supported by the evolution of technology and electronics, the operating principles have always secured GPR a predominant position among alternative inspection approaches. The aim of this contribution is to provide a large-scale survey of the current areas where GPR has emerged as a valuable prospection methodology, highlighting the reasons for such prominence and, at the same time, to suggest where and how it could be enhanced even more.

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Section: Gpr Applications In Geologymentioning

confidence: 99%

From Its Core to the Niche: Insights from GPR Applications

2022

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“…This kind of detection process is blind, and the workload is large [4]. Ground penetrating radar (GPR) is a high-resolution and non-destructive electromagnetic detection technology for detecting near-surface [5]. It has been widely used in urban road [6] maintenance and cavity detection [7].…”

Section: Introductionmentioning

confidence: 99%

3D ground penetrating radar cavity identification algorithm for urban roads using transfer learning

Li¹,

Yang²,

Qiao³

et al. 2023

Meas. Sci. Technol.

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3D ground penetrating radar (GPR) is the main method for the detection of underground cavities in urban roads. The number of road cavity samples detected by 3D radar is small, whereas the intelligent identification model require a large number of learning samples for model training, resulting in inadequate model training. This causes the model to be less accurate in identifying cavities, resulting in many misses and misjudgments. Given the above problems, combined with the detection characteristics of the vertical, the horizontal, and the crossed slices obtained in one detection process of 3D GPR, a 3D GPR cavity intelligent recognition model based on model-based transfer learning is proposed. Firstly, a large amount of 3D GPR data of urban road models with cavities are obtained through forwarding simulation. And the intelligent recognition model was pre-trained on the cavity detection data on three types of slices respectively. Then, through model-based transfer learning, a small amount of real underground cavity data is used to speed up the convergence speed of model training and optimize the structural parameters. It breaks through the limitation of the insufficient number of cavity samples for 3D radar detection on the intelligent learning model training, reduces algorithm training costs, and improves identification accuracy.

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