Parameter identification of an unconfined aquifer using extended Kalman filter

Abstract

A method using extended Kalman filter (EKF) and cubic spline is proposed to identify the aquifer parameters of an unconfined aquifer system. Neuman's model combined with EKF, using the interpolated drawdown data produced by cubic spline, can optimally determine the parameters for unconfined aquifers through the recursive filtering process. The proposed method can quickly identify the parameters, using only part of observed drawdown data, and achieves good accuracy. Thus, the lengthy pumping test may be shortened. In addition, the comparisons of results among using conventional graphical methods, nonlinear least-squares combined with finite-difference Newton's method (NLN), and EKF are presented. The EKF is shown to allow a wider range of initial guess values, though the results are slightly less accurate than those by NLN, but much more accurate than those by graphical methods. Finally, the identification process of specific yield reflects the effect of gravity drainage on the drawdown curve and conforms to the physical nature of an unconfined aquifer, when determining the aquifer parameters.