We present the model-based inversion algorithm, which uses a priori information on the geometry to reduce the number of unknown parameters and improve the quality of the reconstructed conductivity image. This model-based inversion approach can be also used to refine the conductivity image that we obtained using the pixel-based inversion algorithm. The model-based inversion approach 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 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. We will show some numerical examples to illustrate the advantageous of using this model-based inversion approach.


Article metrics loading...

Loading full text...

Full text loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error