A quasi-analytical boundary condition for three-dimensional finite difference electromagnetic modeling

Authors

  • Salah Mehanee,

    1. Consortium for Electromagnetic Modeling and Inversion, Department of Geology and Geophysics, University of Utah, Salt Lake City, Utah, USA
    2. Now at Geophysics Department, Faculty of Science, Cairo University, Giza, Egypt.
    Search for more papers by this author
  • Michael Zhdanov

    1. Consortium for Electromagnetic Modeling and Inversion, Department of Geology and Geophysics, University of Utah, Salt Lake City, Utah, USA
    Search for more papers by this author

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, which is a special form of the extended Born approximation. We call this new condition a quasi-analytical boundary condition (QA BC). This approach helps to reduce the size of the modeling domain without losing the accuracy of calculation. As a result, a larger number of grid cells can be used to describe the anomalous conductivity distribution within the modeling domain. The developed numerical technique allows application of a very fine discretization to the area with anomalous conductivity only because there is no need to move the boundaries too far from the inhomogeneous region, as required by the traditional Dirichlet or Neumann conditions for anomalous field with boundary values equal to zero. Therefore this approach increases the efficiency of FD modeling of the EM field in a medium with complex structure. We apply the QA BC and the traditional FD (with large grid and zero BC) methods to complicated models with high resistivity contrast. The numerical modeling demonstrates that the QA BC results in 5 times matrix size reduction and 2–3 times decrease in computational time.

Ancillary