2004
DOI: 10.1029/2004rs003029
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A quasi‐analytical boundary condition for three‐dimensional finite difference electromagnetic modeling

Abstract: [1] Numerical modeling of the quasi-static electromagnetic (EM) field in the frequency domain in a three-dimensional (3-D) inhomogeneous medium is a very challenging problem in computational physics. We present a new approach to the finite difference (FD) solution of this problem. The FD discretization of the EM field equation is based on the balance method. To compute the boundary values of the anomalous electric field we solve for, we suggest using the fast and accurate quasi-analytical (QA) approximation, w… Show more

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Cited by 14 publications
(4 citation statements)
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References 31 publications
(74 reference statements)
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“…Mehanee and Zhdanov [79] used the fast QA approximation [133] (see subsection 5.3.2 for description of QA) to compute the boundary values of the anomalous electric field. These precomputed values are then used as boundary conditions for the FD modeling based on the balance method.…”
Section: Boundary Conditionsmentioning
confidence: 99%
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“…Mehanee and Zhdanov [79] used the fast QA approximation [133] (see subsection 5.3.2 for description of QA) to compute the boundary values of the anomalous electric field. These precomputed values are then used as boundary conditions for the FD modeling based on the balance method.…”
Section: Boundary Conditionsmentioning
confidence: 99%
“…However, in the case of the quasi-static EM field, which is the subject of our research, it is difficult to use the model of EM waves and their reflection from the boundaries because the field propagates according to the diffusion law. That is why the original PML ABC, developed for the FD time domain EM field, has found little application in modeling the quasi-static EM field used in geophysical applications [79].…”
Section: Boundary Conditionsmentioning
confidence: 99%
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“…Potential field techniques have various successful applications in exploration geophysics [1][2][3][4][5][6][7][8][9][10]. One of the most essential applications of the magnetic dataset is to define the location and depth of magnetic bodies.…”
Section: Introductionmentioning
confidence: 99%