Nonlinear conjugate gradients algorithm for 2-D magnetotelluric inversion

By William L. Rodi, R. Mackie. 2001

1,425 citations recorded by the source corpus.

We investigate a new algorithm for computing regularized solutions of the 2-D magnetotelluric inverse problem. The algorithm employs a nonlinear conjugate gradients (NLCG) scheme to minimize an objective function that penalizes data residuals and second spatial derivatives of resistivity. We compare this algorithm theoretically and numerically to two previous algorithms for constructing such “minimum‐structure” models: the Gauss‐Newton method, which solves a sequence of linearized inverse problems and has been the standard approach to nonlinear inversion in geophysics, and an algorithm due to Mackie and Madden, which solves a sequence of linearized inverse problems incompletely using a (linear) conjugate gradients technique. Numerical experiments involving synthetic and field data indicate that the two algorithms based on conjugate gradients (NLCG and Mackie‐Madden) are more efficient than the Gauss‐Newton algorithm in terms of both computer memory requirements and CPU time needed to find accurate solutio...

Subfields: Geophysics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/519f6fc50d6da103865f61c9d9752c58210a67e9

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In Ocean Engineering

Citations in the corpus

The electrical structure of the Slave craton (2003, methodology): “The models were all obtained using Mackie’s RLM2DI code (Rodi and Mackie, 2001), as implemented within the Geotools package, fitting both the strike-parallel (TE-mode) and strike-perpendicular (TM-mode) MT responses simultaneously.”
A comparison of the Gauss-Newton and quasi-Newton methods in resistivity imaging inversion (2002, positive): “The gradient of the objective function (Rodi and Mackie, 2001) is given by: The Gauss–Newton method involves the solution of the following system of equations (Sasaki, 1989; Oldenburg and Li, 1994): where p i is the perturbation vector to the model parameters.”