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A new parameter estimation algorithm based on ensemble Kalman filter (EnKF) is developed. The developed algorithm combined with the proposed problem parametrization offers an efficient parameter estimation method that converges using very small ensembles and without any tuning parameters. The inverse problem is formulated as a sequential data integration problem. Gaussian Process Regression (GRP) is used to integrate the prior knowledge (static data). The search space is further parameterizeddoi:10.1007/s00477-012-0613-x fatcat:xetaxtduyncqtlylax2koerkxe