An iteratively reweighted algorithm for sparse reconstruction of subsurface flow properties from nonlinear dynamic data
L Li, B Jafarpour - arXiv preprint arXiv:0911.2270, 2009 - arxiv.org
L Li, B Jafarpour
arXiv preprint arXiv:0911.2270, 2009•arxiv.orgIn 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.
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.
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.
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