Sequential data assimilation for a subsurface flow model with the ensemble Kalman filter

Uncertainties in hydraulic parameters of a subsurface flow model can lead to differences between simulation results and real observations, making it difficult to obtain a reliable model. In this study, the ensemble Kalman filter (EnKF) is used to identify hydraulic parameters for a subsurface flow model of an existing underground structure. The performance of the EnKF is assessed with synthetic experiments. The results indicate that the EnKF is capable of accurate estimation of hydraulic parameters. Further, a data assimilation using real field measurements showed promising results and demonstrated applicability of the EnKF. In addition, limitations of the subsurface flow model to achieve successful data assimilation are identified.