Application of Image Processing Techniques to GPR Data for the Reliability Improvement in Subsurface Void Analysis

지표레이더(GPR) 탐사자료를 이용한 지하공동 분석 시 신뢰도 향상을 위한 영상처리기법의 활용

  • Kim, Bona (Department of Earth Resources and Environmental Engineering, Hanyang University) ;
  • Seol, Soon Jee (Department of Earth Resources and Environmental Engineering, Hanyang University) ;
  • Byun, Joongmoo (Department of Earth Resources and Environmental Engineering, Hanyang University)
  • 김보나 (한양대학교 자원환경공학과) ;
  • 설순지 (한양대학교 자원환경공학과) ;
  • 변중무 (한양대학교 자원환경공학과)
  • Received : 2017.02.09
  • Accepted : 2017.03.15
  • Published : 2017.05.31


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.


Supported by : 한국에너지기술평가원(KETEP)


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