DOI: 10.5194/hess-20-3289-2016
Scopus记录号: 2-s2.0-84982085435
论文题名: A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology
作者: Ait-El-Fquih B ; , El Gharamti M ; , Hoteit I
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期: 8 起始页码: 3289
结束页码: 3307
语种: 英语
Scopus关键词: Aquifers
; Bandpass filters
; Estimation
; Groundwater
; Groundwater resources
; Kalman filters
; Molar mass
; Slip forming
; Uncertainty analysis
; Dual filtering strategy
; Ensemble Kalman Filter
; Ensemble Kalman filtering
; Numerical experiments
; Spatial and temporal distribution
; State and parameter estimations
; State parameter estimation
; Two Dimensional (2 D)
; Parameter estimation
; aquifer
; Bayesian analysis
; estimation method
; groundwater
; hydraulic conductivity
; hydraulic head
; hydrological modeling
; Kalman filter
; spatial distribution
; temporal distribution
英文摘要: Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface groundwater models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKFOSA. Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches. © 2016 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78761
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia; Mohn-Sverdrup Center for Global Ocean Studies and Operational Oceanography, Nansen Environmental and Remote Sensing Center (NERSC), Bergen, Norway
Recommended Citation:
Ait-El-Fquih B,, El Gharamti M,, Hoteit I. A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology[J]. Hydrology and Earth System Sciences,2016-01-01,20(8)