Flexible iterative ensemble smoother for calibration of perfect and imperfect models
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ORIGINAL PAPER
Flexible iterative ensemble smoother for calibration of perfect and imperfect models Muzammil Hussain Rammay1 · Ahmed H. Elsheikh1 · Yan Chen2 Received: 8 November 2019 / Accepted: 29 September 2020 © The Author(s) 2020
Abstract Iterative ensemble smoothers have been widely used for calibrating simulators of various physical systems due to the relatively low computational cost and the parallel nature of the algorithm. However, iterative ensemble smoothers have been designed for perfect models under the main assumption that the specified physical models and subsequent discretized mathematical models have the capability to model the reality accurately. While significant efforts are usually made to ensure the accuracy of the mathematical model, it is widely known that the physical models are only an approximation of reality. These approximations commonly introduce some type of model error which is generally unknown and when the models are calibrated, the effects of the model errors could be smeared by adjusting the model parameters to match historical observations. This results in a bias estimated parameters and as a consequence might result in predictions with questionable quality. In this paper, we formulate a flexible iterative ensemble smoother, which can be used to calibrate imperfect models where model errors cannot be neglected. We base our method on the ensemble smoother with multiple data assimilation (ES-MDA) as it is one of the most widely used iterative ensemble smoothing techniques. In the proposed algorithm, the residual (data mismatch) is split into two parts. One part is used to derive the parameter update and the second part is used to represent the model error. The proposed method is quite general and relaxes many of the assumptions commonly introduced in the literature. We observe that the proposed algorithm has the capability to reduce the effect of model bias by capturing the unknown model errors, thus improving the quality of the estimated parameters and prediction capacity of imperfect physical models. Keywords Ensemble smoother · Calibration of imperfect model · Model error
1 Introduction Bayesian inversion is a generic inference framework that is widely adopted for calibration of mathematical models while accounting for different types/sources of uncertainties. In the Bayesian framework, the prior model parameters belief or probability is updated by integrating the observed data to obtain the posterior probability. Several algorithms could be used to generate samples from the posterior distribution of the model parameters. Among those,
Muzammil Hussain Rammay
[email protected]; [email protected] Ahmed H. Elsheikh [email protected] 1
Heriot–Watt University, Edinburgh, UK
2
Total Geoscience Research Centre, Aberdeen, UK
Markov chain Monte Carlo (MCMC) is an exact method for sampling. However, MCMC can be computationally expensive due to the large number of iterations (number of sampling steps) needed to reach convergence and the sequential nature of the method. Ens
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