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Applicability Analysis on Estimation of Spectral Induced Polarization Parameters Based on Multi-objective Optimization

다중목적함수 최적화에 기초한 광대역 유도분극 변수 예측 적용성 분석

  • Kim, Bitnarae (Department of Energy & Mineral Resources Engineering, Sejong University) ;
  • Jeong, Ju Yeon (Department of Energy & Mineral Resources Engineering, Sejong University) ;
  • Min, Baehyun (Department of Climate and Energy Systems Engineering, Ewha Womans University) ;
  • Nam, Myung Jin (Department of Energy & Mineral Resources Engineering, Sejong University)
  • 김빛나래 (세종대학교 에너지자원공학과) ;
  • 정주연 (세종대학교 에너지자원공학과) ;
  • 민배현 (이화여자대학교 에너지자원공학과) ;
  • 남명진 (세종대학교 에너지자원공학과)
  • Received : 2021.10.12
  • Accepted : 2022.08.16
  • Published : 2022.08.31

Abstract

Among induced polarization (IP) methods, spectral IP (SIP) uses alternating current as a transmission source to measure amplitudes and phase of complex electrical resistivity at each source frequency, which disperse with respect to source frequencies. The frequency dependence, which can be explained by a relaxation model such as Cole-Cole model or equivalent models, is analyzed to estimate SIP parameters from dispersion curves of complex resistivity employing multi-objective optimization (MOO). The estimation uses a generic algorithm to optimize two objective functions minimizing data misfits of amplitude and phase based on Cole-Cole model, which is most widely used to explain IP relaxation effects. The MOO-based estimation properly recovered Cole-Cole model parameters for synthetic examples but hardly fitted for the real laboratory measures ones, which have relatively smaller values of phases (less than about 10 mrad). Discrepancies between scales for data misfits of amplitude and phase, used as parameters of MOO method, and it is in necessity to employ other methods such as machine learning, which can deal with the discrepancies, to estimate SIP parameters from dispersion curves of complex resistivity.

유도분극(induced polarization; IP) 탐사 중 광대역 혹은 빛띠(spectral) IP (SIP) 탐사법에서는 교류 전류를 송신원으로 하였을 때 나타나는 매질의 진동수에 따른 복소전기비저항의 크기와 위상을 측정하며, 진동수에 따라 값이 변화하는 복소전기비저항의 분산 혹은 이완 반응을 분석하게 된다. 이때 분산곡선은 등가회로 모델과 같은 이완 모델을 통해 설명할 수 있는데, 다중목적함수 최적화 기법을 적용하여 분산곡선에서 SIP 이완모델의 변수들을 예측해보았다. SIP 이완현상을 설명하기 위해 가장 많이 이용되는 Cole-Cole 모델 계열의 변수를 구하기 위해 크기 오차와 위상 오차를 최소화하는 두 가지 목적함수로 설정하고 다중목적함수를 최적화하기 위해 유전 알고리듬을 이용하였다. 다중목적함수 최적화 기법을 이용한 Cole-Cole 모델 변수 구하기는 수치 모델에 대해서는 잘 구해졌으나 기존에 보고된 SIP 실내실험 자료에 피팅할 경우, 주로 위상 크기가 작을 때(약 10 mrad 이하) 피팅이 맞지 않는 경우가 많았다. 이는 다중목적함수로 사용하는 크기와 위상의 자료 오차 사이에 스케일이 맞지 않아 발생하는 한계로 추정되며, 향후 복소전기비저항의 분산 곡선에서 SIP 변수를 예측하기 위해 이러한 한계를 극복할 수 있는 기계 학습 등 다양한 기법들에 대한 연구가 필요할 것으로 판단된다.

Keywords

Acknowledgement

본 연구는 환경부(2018002440005, 지중환경오염·위해관리기술개발사업)와 2022년도 국토교통부의 재원으로 국토교통과학기술진흥원(RS-2022-00143644, 오일 생산플랜트의 패키지화 설계 및 통합실증 기술개발)으로부터 지원받아 수행된 연구입니다.

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