SIAM Journal on Applied Mathematics
The use of the L-curve in the regularization of discrete ill-posed problems
SIAM Journal on Scientific Computing
Using the L-curve for determining optimal regularization parameters
Numerische Mathematik
Annealing of Iterative Stochastic Schemes
SIAM Journal on Control and Optimization
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
A well-conditioned estimator for large-dimensional covariance matrices
Journal of Multivariate Analysis
Reservoir Simulation: Mathematical Techniques in Oil Recovery (CBMS-NSF Regional Conference Series in Applied Mathematics)
Shrinkage algorithms for MMSE covariance estimation
IEEE Transactions on Signal Processing
Iterative Wiener filters for image restoration
IEEE Transactions on Signal Processing
Journal of Computational Physics
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Parameter estimation for subsurface flow models is an essential step for maximizing the value of numerical simulations for future prediction and the development of effective control strategies. We propose the iterative stochastic ensemble method (ISEM) as a general method for parameter estimation based on stochastic estimation of gradients using an ensemble of directional derivatives. ISEM eliminates the need for adjoint coding and deals with the numerical simulator as a blackbox. The proposed method employs directional derivatives within a Gauss-Newton iteration. The update equation in ISEM resembles the update step in ensemble Kalman filter, however the inverse of the output covariance matrix in ISEM is regularized using standard truncated singular value decomposition or Tikhonov regularization. We also investigate the performance of a set of shrinkage based covariance estimators within ISEM. The proposed method is successfully applied on several nonlinear parameter estimation problems for subsurface flow models. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates.