• Electronics and Telecommunications Trends
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• v.30 no.5
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• pp.22-27
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• 2015

도로함몰 사고 증가를 계기로 지하 구조물의 안전 관리 방법, 특히 비파괴 검사 장비에 대한 관심이 고조되고 있다. 지중투과 레이더 기술은 전자파의 투과, 반사 특성을 이용한 비파괴 검사 기술로써 투과 깊이, 투과 해상도 등 비파괴 검사 성능 측면에서 가장 주목받고 있는 기술이다. 본고에서는 지중투과 레이더 기술의 특징, 국외 제품 동향을 살펴본다.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Journal of the Korean GEO-environmental Society
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• v.19 no.8
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• pp.11-17
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• 2018

Recently, road subsidence has been increasing in urban areas, threatening the safety of citizens. In the lower part of the road, various road facilities such as water supply and drainage pipelines and telecommunication facilities are buried, and the deterioration of the facilities causes the road subsidence. Especially, in the case of old sewer which are attracting attention as a main cause of ground subsidence, the risk of subsidence is calculated indirectly through CCTV exploration. Currently, we are finding cavity through GPR exploration. However, it is difficult to find the sewer back cavity because it is explored from the surface of the road. Thus, the nondestructive cavity exploration techniques was investigated in this study and we confirmed the applicability through experiments on the test-bed. In this study a new quantitative method is proposed to detect the cavity around sewer.

• 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.

• Geophysics and Geophysical Exploration
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• v.22 no.3
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• pp.132-148
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• 2019

Recently, safety and environmental concerns have become major social issues. Especially, a special underground-safety law has been made and enacted to prevent ground subsidence around construction sites. For environmental problems, several researches have started or will start on characterization of contaminated sites, in-situ environmental remediation in subsurface, and monitoring of remediation results. As a part of the researches, geophysical surveys, which have been mainly applied to explore mineral resources, geological features or ground, are used to characterize not only contaminated areas but also fluid flow paths in subsurface environments. As a basic study for the application of geophysical surveys to detect contamination in subsurface, this paper analyzes previous researches to understand changes in geophysical properties of contaminated zones by various contaminants such as leachate, heavy metals, and non-adequate phase liquid (NAPL). Furthermore, this paper briefly introduces how geophysical surveys like direct-current electrical resistivity, induced polarization and ground penetration radar surveys can be applied to detect each contamination, before analyzing case studies of the applications in contaminated areas by NAPL, leachate, heavy metal or nitrogen oxides.