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Inversion of Resistivity Data using Data-weighting

자료 가중을 통한 전기비저항 탐사 자료의 역산

  • Cho, In-Ky (Division of Geology and Geophysics, Kangwon National University) ;
  • Lee, Keun-Soo (Division of Geology and Geophysics, Kangwon National University) ;
  • Kim, Yeon-Jung (Division of Geology and Geophysics, Kangwon National University) ;
  • Yoon, Dae-Sung (Division of Geology and Geophysics, Kangwon National University)
  • 조인기 (강원대학교 지질.지구물리학부) ;
  • 이근수 (강원대학교 지질.지구물리학부) ;
  • 김연정 (강원대학교 지질.지구물리학부) ;
  • 윤대성 (강원대학교 지질.지구물리학부)
  • Received : 2015.01.21
  • Accepted : 2015.02.23
  • Published : 2015.02.28

Abstract

All the resistivity data contain various kinds of noise. The major sources of noise in DC resistivity measurement are high contact resistance, measurement errors, and sporadic background noise. Thus, it is required to measure data noise to accurately interpret resistivity data. Reciprocal measurements can provide a measure of data precision and noise. In this study, we proposed a data-weighting method from reciprocity measurement. Furthermore, a data-weighting method using both the reciprocity error and data-misfit in the inversion process was studied. Applying the data-weighting method to the inversion of 3D resistivity data, it was confirmed that local anomalies are slightly suppressed in the final inversion results.

전기비저항 탐사 자료는 다양한 잡음을 포함하고 있다. 즉 전기비저항 자료는 높은 접촉저항, 장비의 측정 오차 및 주변의 불규칙한 전기적 잡음에 의해 영향을 받는다. 전기비저항 탐사 자료의 올바른 해석을 위해서는 이들 잡음의 정확한 추정이 요구된다. 이 연구에서는 상반성 시험을 통하여 추정된 잡음을 역산시 자료 가중에 반영하는 방법론을 제안하였다. 또한 역산시 현장 자료와 이론 자료 사이의 적합 오차와 상반성 오차를 분석하고, 상반성 오차와 적합 오차를 모두 이용하는 자료 가중법을 제안하였다. 현장 자료에 제안된 자료 가중법을 적용한 결과 통상적인 역산 결과에 비하여 국지적 이상대의 출현 빈도가 감소하는 것을 확인할 수 있었다.

Keywords

References

  1. An, D. K., and Cho, I. K., 2009, Inversion of resistivity data considering data noise, Journal of the Korean Society of Mineral and Energy Resources, 46(5), 546-552.
  2. Binley, A., Ramirez, A., and Daily, W., 1995, Regularised image reconstruction of noisy electrical resistance tomography data, The 4th workshop of the European Concerted Action on Process Tomography, Bergen, Norway.
  3. Constable, S. C., Parker, R. L., and Constable, C. G., 1987, Occam's inversion: a practical algorithm for generating smooth models from EM sounding data, Geophysics, 52, 289-300. https://doi.org/10.1190/1.1442303
  4. Kim, J. H., 2014, Admittance inversion of GPR data, 4D and HD Geophysics Workshop, KIGAM, Korea.
  5. LaBrecque, D., J., Milletto, M., Daily, W., Ramirez, A., and Owen, E., 1996, The effects of noise on Occam's inversion of resistivity tomography data, Geophysics, 61, 538-548. https://doi.org/10.1190/1.1443980
  6. Menke, W., 1984, Geophysical data analysis: Discrete inverse theory, Academic Press Inc.
  7. Slater, L., Binley, A., Cassiani, G., Birken, R., and Sandberg, S., 2002, A 3D study solute transport in a large experimental tank, Journal of Applied Geophysics, 49, 211-229. https://doi.org/10.1016/S0926-9851(02)00124-6
  8. Slater, L., Binley, A. M., Daily, W., and Johnson, R., 2000, Cross-hole electrical imaging of a controlled saline tracer injection, Journal of Applied Geophysics, 44, 85-102. https://doi.org/10.1016/S0926-9851(00)00002-1
  9. Yi, M. J., Kim, J. H., and Chung, S. H., 2003, Enhancing the resolving power of least-squares inversion with active constraint balancing, Geophysics, 68, 931-941. https://doi.org/10.1190/1.1581045