Computer Model Calibration using the Ensemble Kalman Filter
| Autor: | Michael Tobis, Steve Price, Katrin Heitmann, David Higdon, Charles S. Jackson, Salman Habib, James R. Gattiker, Earl Lawrence, Matthew T. Pratola |
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| Rok vydání: | 2012 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences Calibration (statistics) Estimation theory Computer science Applied Mathematics Covariance Computer experiment Statistics - Computation Bayesian statistics Methodology (stat.ME) Data assimilation Modeling and Simulation Econometrics Ensemble Kalman filter Uncertainty quantification Algorithm Statistics - Methodology Physics::Atmospheric and Oceanic Physics Computation (stat.CO) |
| DOI: | 10.48550/arxiv.1204.3547 |
| Popis: | The ensemble Kalman filter (EnKF) (Evensen, 2009) has proven effective in quantifying uncertainty in a number of challenging dynamic, state estimation, or data assimilation, problems such as weather forecasting and ocean modeling. In these problems a high-dimensional state parameter is successively updated based on recurring physical observations, with the aid of a computationally demanding forward model that prop- agates the state from one time step to the next. More recently, the EnKF has proven effective in history matching in the petroleum engineering community (Evensen, 2009; Oliver and Chen, 2010). Such applications typically involve estimating large numbers of parameters, describing an oil reservoir, using data from production history that accumulate over time. Such history matching problems are especially challenging examples of computer model calibration since they involve a large number of model parameters as well as a computationally demanding forward model. More generally, computer model calibration combines physical observations with a computational model - a computer model - to estimate unknown parameters in the computer model. This paper explores how the EnKF can be used in computer model calibration problems, comparing it to other more common approaches, considering applications in climate and cosmology. Comment: 20 pages; 11 figures |
| Databáze: | OpenAIRE |
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