2001
DOI: 10.1190/1.1444893
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Nonlinear conjugate gradients algorithm for 2-D magnetotelluric inversion

Abstract: We investigate a new algorithm for computing regularized solutions of the two-dimensional 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 … Show more

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Cited by 1,169 publications
(666 citation statements)
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“…The relative weight given to the data fit and the model smoothness can be controlled by a trade-off parameter (τ), which controls the inversion result (Rodi and Mackie, 2001). In other words, τ can be viewed as a sensitivity parameter, which essentially controls the RMS value between the data and the model.…”
Section: Two-dimensional Mt Inversionmentioning
confidence: 99%
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“…The relative weight given to the data fit and the model smoothness can be controlled by a trade-off parameter (τ), which controls the inversion result (Rodi and Mackie, 2001). In other words, τ can be viewed as a sensitivity parameter, which essentially controls the RMS value between the data and the model.…”
Section: Two-dimensional Mt Inversionmentioning
confidence: 99%
“…TE and TM modes data (apparent resistivity and phase) were subjected to regularized 2D inversion of Rodi and Mackie (2001) for a frequency range of 1030-0.01 Hz, along the three profiles, AA , BB and CC in Fig. 1.…”
Section: Two-dimensional Mt Inversionmentioning
confidence: 99%
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“…4, label D). For 2-D inversion, we used the regularized non-linear conjugate gradient (NLCG) algorithm described by Rodi & Mackie (2001). This algorithm is implemented in the WinGLink software package.…”
Section: -D I N V E R S I O N O F T H E M T Datamentioning
confidence: 99%
“…where g = (Kα Kβ ) T , Kα and Kβ are the gradients found in eqs (9) and (10) and sorted into row vectors, C is a pre-conditioner (here we use it as a smoothing function), and scalar η i is defined as in for example, Rodi & Mackie (2001) as…”
Section: Derivation Of the Sicp-wemva Optimizationmentioning
confidence: 99%