• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.211-228
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• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.

• International Journal of Highway Engineering
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• v.19 no.5
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• pp.69-75
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• 2017

PURPOSES : The purpose of this study is to evaluate different types of Ground Penetrating Radar (GPR) testing for characterizing the road cavity detection. The impulse and step-frequency-type GPR tests were conducted on a full-scale testbed with an artificial void installation. After analyzing the response signals of GPR tests for detecting the road cavity, the characteristics of each GPR response was evaluated for a suitable selection of GPR tests. METHODS : Two different types of GPR tests were performed to estimate the limitation and accuracy for detecting the cavities underneath the asphalt pavement. The GPR signal responses were obtained from the testbed with different cavity sizes and depths. The detection limitation was identified by a signal penetration depth at a given cavity for impulse and step-frequency-type GPR testing. The unique signal characteristics was also observed at cavity sections. RESULTS : The impulse-type GPR detected the 500-mm length of cavity at a depth of 1.0 m, and the step-frequency-type GPR detected the cavity up to 1.5 m. This indicates that the detection capacity of the step-frequency type is better than the impulse type. The step-frequency GPR testing also can reflect the howling phenomena that can more accurately determine the cavity. CONCLUSIONS :It is found from this study that the step-frequency GPR testing is more suitable for the road cavity detection of asphalt pavement. The use of step-frequency GPR testing shows a distinct image at the cavity occurrences.

• Corrosion Science and Technology
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• v.3 no.6
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• pp.262-264
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• 2004

Ground Penetrating Radar (GPR) has been used to image inside concrete specimens embedded with steel bars and delamination. An imaging algorithm has been developed to improve measurement output generated from a commercial radar system. For the experiments, laboratory size concrete specimens are made with the dimensions of $1,000mm(W){\times}1,000mm(L){\times}250mm(D)$. The results have shown improved output of the radar measurements compared to commercially available processing methods.

• Economic and Environmental Geology
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• v.57 no.1
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• pp.17-23
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• 2024

Ground penetrating radar (GPR) survey is a geophysical method that utilizes electromagnetic waves reflecting from a boundary where the electromagnetic property changes. As the frequency of the antenna is about 25 MHz ~ 1 GHz, it is effective to acquire high resolution images of underground pipe, artificial structure, underground cavity, and underground structure. In this study, we analyzed the change of signals reflected from the same underground objects according to the arrangement of transceiver antennas used in ground penetrating radar survey. The antenna used in the experiment was 200 MHz, and the survey was performed in the vertical direction across the sewer and the parallel direction along the sewer to the sewer buried under the road, respectively. A total of five antenna array methods were applied to the survey. The most used arrangement is when the transmitting and receiving antennas are all perpendicular to the survey line (PR-BD). The PR-BD arrangement is effective when the object underground is a horizontal reflector with an angle of less than 30°, such as the sewer under investigation. In this case study, it was confirmed that the transmitter and receiver antennas perpendicular to the survey line (PR-BD) are the most effective way to show the underground structure. In addition, in the case where the transmitting and receiving antennas are orthogonal to each other (XPOL), no specific reflected wave was observed in both experiments measured across or parallel to the sewer. Therefore, in the case of detecting undiscovered objects in the underground, the PR-BD array method in which the transmitting and receiving antennas are aligned in the direction perpendicular to the survey line taken as a reference and the XPOL method in which the transmitting and receiving antennas are orthogonal to each other are all used, it can be effective to apply both of the above arrangements after setting the direction to 45° and 135°.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.346-353
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• 2005

Track substructure(ballast, subgrade) should have sufficient strength and adequate stiffness to fully support track superstructure(rail, fastener, sleeper). Vertical support stiffness of track comes from the sufficient thickness, adequate strength and stiffness of material of substructure layers. Since the vertical support stiffness of track substructure is closely related with the track geometry, the evaluation of the stiffness is very important to understand the track states. This paper introduces the system, which are composed of Ground Penetrating Radar(GPR), Portable Ballast Sampler(PBS), and Light Falling Weight Deflectometer(LFWD), to evaluate substructure condition and summarizes the field test results performed with the reliable system.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.12a
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• pp.169-180
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• 1999

Ground penetrating radar(GPR) uses radio waves to detect buried objects in any non-metallic material. Initially it was used to detect structures in ice. GPR has evolved to include the penetration of soils, rocks and man-made structures. GPR uses a sensitive detector to record weak radio waves reflected from objects embedded in the material under investigation. In this study, the GPR is applied to outside plant telecommunication facilities such as cable tunnels, manholes and underground conduits and model experiments to obtain radar characteristics. The thickness and soundness of tunnel lining can be evaluated, and the location of rebars and steel ribs can also be found effectively. The location of underground conduits as well as manholes can be found and the results of GPR give good coincidence with design drawings. In order to investigate the tunnel lining, the GPR mounted vehicle is developed and it is proved that the vehicle can save time and manpower.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.59-66
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• 2005

The main purpose of this paper is to describe a highly efficient common mid-point (CMP) data acquisition method for ground-penetrating radar (GPR) surveying, which is intended to widen the application of GPR. The most important innovation to increase the efficiency of CMP data acquisition is continuous monitoring of the GPR antenna positions, using a real-time kinematic Global Positioning System (RTK-GPS). Survey time efficiency is improved because the automatic antenna locating system that we propose frees us from the most time-consuming process-deployment of the antenna at specified positions. Numerical experiments predicted that the data density and the CMP fold would be increased by the increased efficiency of data acquisition, which results in improved signal-to-noise ratios in the resulting data. A field experiment confirmed this hypothesis. The proposed method makes GPR surveys using CMP method more practical and popular. Furthermore, the method has the potential to supply detailed groundwater information. This is because we can convert the spatially dense dielectric constant distribution, obtained by using the CMP method we describe, into a dense physical value distribution that is closely related to such groundwater properties as water saturation.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.49-55
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• 2009

This paper describes a three-dimensional ground penetrating radar (GPR) survey carried out around a levee of the Ara River in Saitama, Japan, where deformation of the ground was observed after heavy rainfall associated with the typhoon of September 2007. The high-density 3D GPR survey was conducted as a series of closely adjacent four directional sets of 2D surveys at an area surrounding vertical cracks on the paved road caused by deformations induced by heavy rain. The survey directions of the 2D surveys were 0, 90, 45, and -45 degrees with respect to the paved road and the intervals between lines were less than 0.5 m. The 3D subsurface structure was accurately imaged by the result of data processing using Kirchhoff-type 3D migration. As a result, locations and vertical continuities of the heavy rainfall induced cracks in the paved road were clearly imaged. This will be a great help in considering the generation mechanisms of the cracks. Moreover, the current risk of a secondary disaster was found to be low, as no air-filled cavities were detected by the 3D GPR survey.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.77-84
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• 2009

Ground penetrating radar (GPR) is an effective tool for detecting shallow subsurface targets. In many GPR applications, these targets are veiled by the strong waves reflected from the ground surface, so that we need to apply a signal processing technique to separate the target signal from such strong signals. A pulse-compression technique is used in this research to compress the signal width so that it can be separated out from the strong contaminated clutter signals. This work introduces a filter algorithm to carry out pulse compression for GPR data, using a Wiener filtering technique. The filter is applied to synthetic and field GPR data acquired over a buried pipe. The discrimination method uses both the reflected signal from the target and the strong ground surface reflection as a reference signal for pulse compression. For a pulse-compression filter, reference signal selection is an important issue, because as the signal width is compressed the noise level will blow up, especially if the signal-to-noise ratio of the reference signal is low. Analysis of the results obtained from simulated and field GPR data indicates a significant improvement in the GPR image, good discrimination between the target reflection and the ground surface reflection, and better performance with reliable separation between them. However, at the same time the noise level slightly increases in field data, due to the wide bandwidth of the reference signal, which includes the higher-frequency components of noise. Using the ground-surface reflection as a reference signal we found that the pulse width could be compressed and the subsurface target reflection could be enhanced.