Sequential data assimilation with multiple nonlinear models and applications to subsurface flow

JOURNAL OF COMPUTATIONAL PHYSICS

卷:346

Article

Yang, Lun¹  Narayan, Akil^2,3  Wang, Peng¹ 

[1] Beihang Univ, Dept Math & Syst Sci, Beijing 100191, Peoples R China

[2] Univ Utah, Dept Math, Salt Lake City, UT 84112 USA

[3] Univ Utah, SCI Inst, Salt Lake City, UT USA

关键词: Data assimilation;    Extended Kalman filter;    Ensemble Kalman filter;    Particle filter;    Uncertainty quantification;   

DOI  :  10.1016/j.jcp.2017.06.026

来源: Elsevier

【 摘 要 】

Complex systems are often described with competing models. Such divergence of interpretation on the system may stem from model fidelity, mathematical simplicity, and more generally, our limited knowledge of the underlying processes. Meanwhile, available but limited observations of system state could further complicateone's prediction choices. Over the years, data assimilation techniques, such as the Kalman filter, have become essential tools for improved system estimation by incorporating both models forecast and measurement; but its potential to mitigate the impacts of aforementioned model-form uncertainty has yet to be developed. Based on an earlier study of Multi-model Kalman filter, we propose a novel framework to assimilate multiple models with observation data for nonlinear systems, using extended Kalman filter, ensemble Kalman filter and particle filter, respectively. Through numerical examples of subsurface flow, we demonstrate that the new assimilation framework provides an effective and improved forecast of system behavior. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free