A model-based inversion algorithm for controlled-source electromagnetic data

In this paper, we present the parametric inversion algorithm (PIA), which uses a priori information on the geometry to reduce the number of unknown parameters and improve the quality of the reconstructed conductivity image. The PIA adopts the Gauss-Newton minimization method, with nonlinear constraints and regularization for the unknown parameters. It also employs a line search approach to guarantee the reduction of the cost function after each iteration. The forward modeling simulation is a two-and-half dimensional (2.5D) finite-difference solver, and the parameters that govern the location and the shape of a reservoir include the depth and the location of the user-defined nodes for the boundary of the region. The unknown parameter that describes the physical property of the region is the electrical conductivity.