  In this paper, we present a practical algorithm based on sparsity
regularization to effectively solve nonlinear dynamic inverse problems that are
encountered in subsurface model calibration. We use an iteratively reweighted
algorithm that is widely used to solve linear inverse problems with sparsity
constraint known as compressed sensing to estimate permeability fields from
nonlinear dynamic flow data.
