Review on the Three-Dimensional Inversion of Magnetotelluric Date

MT 자료의 3차원 역산 개관

  • Kim Hee Joon (Department of Environmental Exploration Engineering, Pukyong National University) ;
  • Nam Myung Jin (School of Civil, Urban & Geosystem Engineering Seoul National University) ;
  • Han Nuree (School of Civil, Urban & Geosystem Engineering Seoul National University) ;
  • Choi Jihyang (School of Civil, Urban & Geosystem Engineering Seoul National University) ;
  • Lee Tae Jong (Korea Institute of Geoscience and Mineral Resources) ;
  • Song Yoonho (Korea Institute of Geoscience and Mineral Resources) ;
  • Suh Jung Hee (School of Civil, Urban & Geosystem Engineering Seoul National University)
  • 김희준 (부경대학교 환경탐사공학과) ;
  • 남명진 (서울대학교 지구환경시스템공학부) ;
  • 한우리 (서울대학교 지구환경시스템공학부) ;
  • 최지향 (서울대학교 지구환경시스템공학부) ;
  • 이태종 (한국지질자원연구원) ;
  • 송윤호 (한국지질자원연구원) ;
  • 서정희 (서울대학교 지구환경시스템공학부)
  • Published : 2004.08.01


This article reviews recent developments in three-dimensional (3-D) magntotelluric (MT) imaging. The inversion of MT data is fundamentally ill-posed, and therefore the resultant solution is non-unique. A regularizing scheme must be involved to reduce the non-uniqueness while retaining certain a priori information in the solution. The standard approach to nonlinear inversion in geophysis has been the Gauss-Newton method, which solves a sequence of linearized inverse problems. When running to convergence, the algorithm minimizes an objective function over the space of models and in the sense produces an optimal solution of the inverse problem. The general usefulness of iterative, linearized inversion algorithms, however is greatly limited in 3-D MT applications by the requirement of computing the Jacobian(partial derivative, sensitivity) matrix of the forward problem. The difficulty may be relaxed using conjugate gradients(CG) methods. A linear CG technique is used to solve each step of Gauss-Newton iterations incompletely, while the method of nonlinear CG is applied directly to the minimization of the objective function. These CG techniques replace computation of jacobian matrix and solution of a large linear system with computations equivalent to only three forward problems per inversion iteration. Consequently, the algorithms are efficient in computational speed and memory requirement, making 3-D inversion feasible.


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