Electromagnetic inverse scattering methods have been extensively developed and applied to retrieve geometrical and geophysical information in hydrocarbon exploration. Recently, the marine controlled-source electromagnetic (CSEM) technology has attracted much attention for its capability in directly detecting thin hydrocarbon reservoirs [1, 2]. The approach is based on comparing the electric field amplitude as a function of the source-receiver offset with a similar measurement for a non-hydrocarbon bearing reservoir [1]. The presence of hydrocarbon raises the amplitude of the measured electric field indicating the existence and to some degree determining the horizontal location of the hydrocarbon zone, however with this approach it is difficult to know the reservoir's depth and shape. A more rigorous approach to address this type of application is the full nonlinear electromagnetic inversion. In such an approach the investigation domain is usually subdivided into pixels, and by using an optimization process the location, the shape and the conductivity of the reservoir are reconstructed. The optimization process adopts the Gauss-Newton minimization method and various types of regularization to obtain good conductivity images. The weighted L{sub}2-norm regularization [3] has shown to be able to retrieve reasonably good conductivity image. However, the reconstructed boundaries and conductivity value of the imaged objects are still not sufficiently good. Nevertheless, this pixel-based inversion (PBI) approach can provide some rough information on the location, the shape and the conductivity of the hydrocarbon reservoir. 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 (see [4] for detail descriptions). The forward modeling simulation is a two-and-half dimensional (2.5D) finite-difference solver [3], 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.
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