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

作　　者：Tong-chao Nan Ji-chun Wu 

机构地区：[1]State Key Laboratory of Pollution Control and Resources Reuse,Nanjing University,Nanjing 210093,China [2]Department of Hydrosciences,School of Earth Sciences and Engineering,Nanjing University,Nanjing 210093,China

出　　处：《Water Science and Engineering》2017年第1期25-35,共11页水科学与水工程（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.41602250);the Project of the China Geological Survey(Grant No.DD20160293)

摘　　要：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 in-struments 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.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 in-struments 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.

关 键 词：Data assimilation Hydraulic parameter estimation Ensemble H-Infinity filter Ensemble Kalman filter Hydraulic conductivity ROBUSTNESS 

分 类 号：TV131.6[水利工程—水力学及河流动力学]