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Smoothing‐based compressed state K alman filter for joint state‐parameter estimation: Applications in reservoir characterization and CO 2 storage monitoring
Author(s) -
Li Y. J.,
Kokkinaki Amalia,
Darve Eric F.,
Kitanidis Peter K.
Publication year - 2017
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2016wr020168
Subject(s) - smoothing , data assimilation , kalman filter , nonlinear system , mathematical optimization , computer science , algorithm , ensemble kalman filter , optimization problem , covariance , control theory (sociology) , extended kalman filter , mathematics , artificial intelligence , statistics , physics , control (management) , quantum mechanics , meteorology , computer vision
The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman‐filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one‐step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. We present the smoothing‐based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high‐dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO 2 storage applications, one‐step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real‐time characterization and monitoring of large‐scale hydrogeological systems with sharp moving fronts.