Water Science and Engineering 2017, 10(1) 25-35 DOI:   http://dx.doi.org/10.1016/j.wse.2017.03.009  ISSN: 1674-2370 CN: 32-1785/TV

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Data assimilation
Hydraulic parameter estimation
Ensemble H-infinity filter
Ensemble Kalman filter
Hydraulic conductivity
Tong-chao Nan
Ji-chun Wu
Article by Tong-chao Nan
Article by Ji-chun Wu

Application of ensemble H-infinity filter in aquifer characterization and comparison to ensemble Kalman filter

Tong-chao Nan a,b, Ji-chun Wu a,b,*

aState Key Laboratory of Pollution Control and Resources Reuse, Nanjing University, Nanjing 210093, China
bDepartment of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210093, China


Though the ensemble Kalman filter (EnKF) has been successfully applied in many areas, it requires explicit and accurate model and measurement error information, leading to difficulties in practice when only limited information on error mechanisms of observational instruments for subsurface systems is accessible. To handle the uncertain errors, we applied a robust data assimilation algorithm, the ensemble H-infinity filter (EnHF), to estimation of aquifer hydraulic heads and conductivities in a flow model with uncertain/correlated observational errors. The impacts of spatial and temporal correlations in measurements were analyzed, and the performance of EnHF was compared with that of the EnKF. The results show that both EnHF and EnKF are able to estimate hydraulic conductivities properly when observations are free of error; EnHF can provide robust estimates of hydraulic conductivities even when no observational error information is provided. In contrast, the estimates of EnKF seem noticeably undermined because of correlated errors and inaccurate error statistics, and filter divergence was observed. It is concluded that EnHF is an efficient assimilation algorithm when observational errors are unknown or error statistics are inaccurate.

Keywords Data assimilation   Hydraulic parameter estimation   Ensemble H-infinity filter   Ensemble Kalman filter   Hydraulic conductivity   Robustness  
Received 2016-10-11 Revised 2017-01-02 Online: 2017-01-31 
DOI: http://dx.doi.org/10.1016/j.wse.2017.03.009

This work was supported by the National Natural Science Foundation of China (Grant No. 41602250), and the Project of Hydrogeological Investigation at a 1:50 000 Scale in the Lake-Concentrated Areas of the Northern Ordos Basin of the China Geological Survey (Grant No. DD20160293).

Corresponding Authors: Ji-chun Wu
Email: jcwu@nju.edu.cn
About author:


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