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Review on the Three-Dimensional Inversion of Magnetotelluric Date  

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)
Publication Information
Geophysics and Geophysical Exploration / v.7, no.3, 2004 , pp. 207-212 More about this Journal
Abstract
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.
Keywords
3-D; MT; inversion; Gauss-Newton; Jacobian; CG;
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