• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.26 no.3B
• /
• pp.266-272
• /
• 2001

본 논문에서는 지한 탐사용 레이더를 이용한 특정 산란체의 간단한 탐지 방법을 제안하였다. 기본 원리는 매설된 특정 산란체를 기준으로 하여 몇 가지 크기와 모양 및 전기적 특성이 유사한 산란체들로부터 발생되는 산란신호의 차이를 상관함수(correlation function)을 이용하여 탐지하는 것이다. 산란체가 매설된 지하매질로는 레이더의 동작 주파수에 다른 분산과 손실 등의 전기적 특성 변화를 시뮬레이션에 반영하기 위하여 다항 Debye 모델이 사용되었다. 지상 및 지하 매질에서의 3차원 전파(電波) 전파(傳播) 시뮬레이션을 위한 EDTD 방법을 사용하였다.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2004.05a
• /
• pp.810-813
• /
• 2004

한해 누수로 인하여 많은 양의 상수가 소실되고 있어 국가 재정낭비를 초래하고 있다. 이는 관의 노후화와 지반침하에 의한 파이프의 파손 둥 여러 요인에 의해 발생하는 것으로 누수를 예측하여 탐지하는 것은 매우 어렵다. 현재 누수를 탐지하는 방법은 여러 가지가 있지만 현실적인 제약과 경험자의 주관적인 판단에 의존하기 때문에 정확한 누수 위치를 찾을 수가 없었다. 본 논문에서는 종래의 누수탐지 방법의 단점을 획기적으로 보완하여 지하에 매설된 상수도 관망을 실시간으로 모니터링 할 수 있는 누수탐지 모니터링 시스템을 제안하였다 또한 제안된 시스템을 GIS상에서 실험함으로서 효율성과 정확성을 입증하였다.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.22 no.3
• /
• pp.275-283
• /
• 2002

To detect the position and depth of buried underground utilities, method of Ground Penetrating Radar(GPR) survey is the most commonly used. However, the skin-depth of GPR is very shallow, and in the places where subsurface materials are not homogeneous and are compose of clays and/or salts and gravels, GPR method has limitations in application and interpretation. The aim of this study is to overcome these limitations of GPR survey. For this purpose the site where the GPR survey is unsuccessful to detect the underground big pipes is selected, and soil tests were conducted to confirm the reason why GPR method was not applicable. Non-destructive high-frequency electromagnetic (HFEM) survey was newly developed and was applied in the study area to prove the effectiveness of this new technique. The frequency ranges $2kHz{\sim}4MHz$ and the skin depth is about 30m. The HFEM measures the electric field and magnetic field perpendicular to each other to get the impedance from which vertical electric resistivity distribution at the measured point can be deduced. By adopting the capacitive coupled electrodes, it can make the measuring time shorter, and can be applied to the places covered by asphalt an and/or concrete. In addition to the above mentioned advantages, noise due to high-voltage power line is much reduced by stacking the signals. As a result, the HFEM was successful in detecting the buried underground objects. Therefore this method is a promising new technique that can be applied in the lots of fields, such as geotechnical and archaeological surveys.

• Journal of Korean Society of societal Security
• /
• v.2 no.3
• /
• pp.39-46
• /
• 2009

A small loop-loop multi-frequency electromagnetic(EM) induction method is a useful technique to map a resistivity distribution efficiently and non-destructively. However, for quantitative interpretation and depth sounding, the quality of measured data is crucial. In this paper, we propose a bias correction of measured data by using background noise measurements to obtain reliable data, and propose an evaluation technique of apparent that can provide a resistivity image easily. We have performed small loop-loop EM measurements to detect water saturation in a man-made test site. The application of our proposed techniques to the measured data was successful.

• Economic and Environmental Geology
• /
• v.37 no.2
• /
• pp.185-193
• /
• 2004

A physical model experiment was conducted using a sand and gravel-filled tank model, to investigate the influence on the GPR response of LNAPL vapor phase effects in the unsaturated zone and of residual phase of LNAPL trapped in the saturated zone. Background measurements of GPR were made with only water in the tank using a fluctuating water table model. Gasoline was, then, injected into the bottom of the model tank to simulate a subsurface discharge from a leaking pipe or tank at depth, obtaining GPR data with rising and lowering of water table. Results from the experiment show the GPR sensitivity to the changes in the moisture content in the vadose zone and its effectiveness for monitoring minor fluctuation of the water table. The results also demonstrate a potential of GPR for monitoring possible vapor phase effects of volatile hydrocarbons in the vadose zone as a function of time, and for detecting the effects of residual phase of hydrocarbons in the water saturated system. In addition, the results provide the basis for a strategy that has the potential to successfully detect and delineate residual LNAPL contamination in the water-saturated system at field sites where the conditions are similar to those simulated in the physcial models described herein.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.22 no.5
• /
• pp.515-528
• /
• 2020

Underground concrete structures are usually designed to be used for decades, but in recent years, many of them are nearing their original life expectancy. As a result, it is necessary to promptly inspect and repair the structure, since it can cause lost of fundamental functions and bring unexpected problems. Therefore, personnel-based inspections and repairs have been underway for maintenance of underground structures, but nowadays, objective inspection technologies have been actively developed through the fusion of deep learning and image process. In particular, various researches have been conducted on developing a concrete crack detection algorithm based on supervised learning. Most of these studies requires a large amount of image data, especially, label images. In order to secure those images, it takes a lot of time and labor in reality. To resolve this problem, we introduce a method to increase the accuracy of crack area detection, improved by 0.25% on average by applying adversarial learning in this paper. The adversarial learning consists of a segmentation neural network and a discriminator neural network, and it is an algorithm that improves recognition performance by generating a virtual label image in a competitive structure. In this study, an efficient deep neural network learning method was proposed using this method, and it is expected to be used for accurate crack detection in the future.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.23 no.3
• /
• pp.227-236
• /
• 2003

Leaks in underground pipelines can cause social, environmental and economical problems. One of relevant countermeasures against leaks is to find and repair of leak points of the pipes. Leak noise is a good source to identify the location of leak points of the pipelines. Although there have been several methods to detect the leak location with leak noise, such as listening rods, hydrophones or ground microphones, they have not been so efficient tools. In this paper, acoustic emission (AE) sensors and accelermeters are used to detect leak locations which could provide all easier and move efficient method. Filtering, signal processing and algorithm of raw input data from sensors for the detection of leak location are described. A 120m-long pipeline system for experiment is installed and the results with the system show that the algorithm with the AE sensors and accelerometers offers accurate pinpointing of leaks. Theoretical analysis of sound wave propagation speed of water in underground pipes, which is critically important in leak locating, is also described.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2005.05a
• /
• pp.251-254
• /
• 2005

Microgravity survey was carried out in order to detect an abandoned mineshaft. We tested the feasibility of cavity detection by means of numerical modeling and applied microgravity survey to detecting an abandoned mineshaft in the vicinity of Hawson mines, Junnam. The result shows the response of mineshaft where we expected.

• Geophysics and Geophysical Exploration
• /
• v.26 no.4
• /
• pp.171-184
• /
• 2023

Machine learning (ML)-based cavity detection using a large amount of survey data obtained from vehicle-mounted ground penetrating radar (GPR) has been actively studied to identify underground cavities. However, only simple image processing techniques have been used for preprocessing the ML input, and many conventional seismic and GPR data processing techniques, which have been used for decades, have not been fully exploited. In this study, based on the idea that a cavity can be identified using diffraction, we applied ML-based diffraction separation to GPR data to increase the accuracy of cavity detection using the YOLO v5 model. The original ML-based seismic diffraction separation technique was modified, and the separated diffraction image was used as the input to train the cavity detection model. The performance of the proposed method was verified using public GPR data released by the Seoul Metropolitan Government. Underground cavities and objects were more accurately detected using separated diffraction images. In the future, the proposed method can be useful in various fields in which GPR surveys are used.

• Journal of Korean Society of societal Security
• /
• v.1 no.4
• /
• pp.73-81
• /
• 2008

Electromagnetic induction surveys are one of the useful methods to detect the location and buried depth of underground utilities by measuring horizontal and vertical magnetic fields. It can effectively detects single buried utility with the accuracy of within 20 cm. However when another utility is buried near to target one, the accuracy of utility location considerably decreases due to the distortion of magnetic fields caused from adjacent utility. This study shows the ways to verify the location and buried depth of target utility when magnetic fields does not show symmetric distribution due to adjacent another utility. Using Bluetooth wireless communication tools, we developed the way to records measured magnetic fields to handheld PDA. We investigated the criteria for minimum distance of two adjacent utilities to separate the individual responses through field model test.