Title :
A model-based inversion algorithm for controlled-source electromagnetic data
Author :
Zhang, Yan ; Abubakar, Aria ; Habashy, Tarek M.
Author_Institution :
Schlumberger-Doll Res., Cambridge
Abstract :
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.
Keywords :
computational electromagnetics; finite difference methods; image reconstruction; minimisation; Gauss-Newton minimization method; controlled-source electromagnetic data; cost function; electrical conductivity; finite-difference solver; forward modeling simulation; image reconstruction; model-based inversion algorithm; nonlinear constraints; parametric inversion algorithm; user-defined nodes; Conductivity; Cost function; Electromagnetic modeling; Finite difference methods; Image reconstruction; Information geometry; Least squares methods; Minimization methods; Newton method; Recursive estimation;
Conference_Titel :
Antennas and Propagation Society International Symposium, 2007 IEEE
Conference_Location :
Honolulu, HI
Print_ISBN :
978-1-4244-0877-1
Electronic_ISBN :
978-1-4244-0878-8
DOI :
10.1109/APS.2007.4395867
