• Proceedings of the KSEEG Conference
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• 2002.04a
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• pp.440-443
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• 2002

• Geophysics and Geophysical Exploration
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• v.2 no.1
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• pp.1-7
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• 1999

We investigated the weathering characteristics of On-Yang gneiss by means of geological survey and Ground Penetrating Radar(GPR). The results of geological survey and boring show the two sets of vertical joint and horizontal joint developed by foliation which is composed of salic and melanic layers. GPR section evidently shows foliation direction and differential weathering due to discontinuity and mineral composition of metamorphic rock. The GPR section for instantaneous phase attribute based on complex trace analysis evidently shows continuity and foliation direction of metamorphic rock. The strong reflection amplitude which is derived from the banded structure of weathered rock can be incorrectly interpreted as a reflection of bedrock. The depth of rock basement should be estimated from the overall exploration result such as boring, seismic method, and electrical resistivity method.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.32-40
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• 2021

Underground information is difficult to visually check, which can lead to a huge accident in the event of a safety accident. Recently, the Ministry of Land, Infrastructure and Transport intends to reduce safety accidents caused by the aging or damage of underground facilities through the Special Act on Underground Safety Management. GPR is increasingly being used as a technology to acquire information in underground spaces that are difficult to see with the naked eye. However, GPR's location information is corrected by checking images of CCTV and GPS information acquired during exploration. This method has an average error of about 2 meters. In this works, We used LiDAR to calibrate the GPR information and found that the error was reduced from at least 7cm to up to 40cm. If accurate GPR information collected in the future is analyzed quickly using AI, etc., it will be able to collect and utilize underground information faster than it is now to secure safety.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.111-116
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• 2005

2-D and 3-D resistivity surveys were carried out at the Deok-In2 levee during the period of arid and rainy seasons to assess the waterproof effectiveness of sheet pile and grouting sections and detect the location of pipings. Inverted resistivity sections clearly indicated the boundaries of sheet pile and grouting sections and the locations of pipings observed at the ground surface. Besides, GPR survey was carried out to verify the rear cavity of culvert in levee which is thought to be the major cause of levee breakdown, But the quality of GPR data was very poor due to the steel reinforcements buried in the culvert. Because it is not easy to apply various geophysical surveys upon concrete structures, newly designed hydraulic response test was proposed to assess the continuity of rear cavity of culvert in this study.

• 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.

• Korean Journal of Heritage: History & Science
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• v.43 no.3
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• pp.306-333
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• 2010

Previous studies on Wolseong fortress have focused on capital system of Silla Dynasty and on the recreation of Wolseong fortress due to the excavations in and around Wolseong moat. Since the report on the Geographical Survey of Wolseong fortress was published and GPR survey in Wolseong fortress was executed as a trial test in 2004, the academic interest in the site has now expanded to the inside of the fortress. From such context, the preliminary research on the fortress including geophysical survey had been commenced. GPR survey had been conducted for a year from March, 2007. The principal purpose of the recent 3D GPR survey was to provide visualization of subsurface images of the entire Wolseong fortress area. In order to obtain 3D GPR data, dense profile lines were laid in grid-form. The total area surveyed was $112,535m^2$. Depth slice was applied to analyse each level to examine how the layers of the remains had changed and overlapped over time. In addition, slice overlay analysis methodology was used to gather reflects of each depth on a single map. Isolated surface visualization, which is one of 3D analysis methods, was also employed to gain more in-depth understanding and more accurate interpretations of the remain The GPR survey has confirmed that there are building sites whose archaeological features can be classified into 14 different groups. Three interesting areas with huge public building arrangement have been found in Zone 2 in the far west, Zone 9 in the middle, and Zone 14 in the far east. It is recognized that such areas must had been used for important public functions. This research has displayed that 3D GPR survey can be effective for a vast area of archaeological remains and that slice overlay images can provide clearer image with high contrast for objects and remains buried the site.

• 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.20 no.2
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• pp.61-71
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• 2017

Recently, ground-penetrating radar (GPR) surveys have been actively carried out for precise subsurface void investigation because of the rapid increase of subsidence in urban areas. However, since the interpretation of GPR data was conducted based on the interpreter's subjective decision after applying only the basic data processing, it can result in reliability problems. In this research, to solve these problems, we analyzed the difference between the events generated from subsurface voids and those of strong diffraction sources such as the buried pipeline by applying the edge detection technique, which is one of image processing technologies. For the analysis, we applied the image processing technology to the GRP field data containing events generated from the cavity or buried pipeline. As a result, the main events by the subsurface void or diffraction source were effectively separated using the edge detection technique. In addition, since subsurface voids associated with the subsidence has a relatively wide scale, it is recorded as a gentle slope event unlike the event caused by the strong diffraction source recorded with a sharp slope. Therefore, the directional analysis of amplitude variation in the image enabled us to effectively separate the events by the subsurface void from those by the diffraction source. Interpretation based on these kinds of objective analysis can improve the reliability. Moreover, if suggested techniques are verified to various GPR field data sets, these approaches can contribute to semiautomatic interpretation of large amount of GPR data.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.