An ensemble Kalman filter approach based on operator splitting for solving nonlinear Hammerstein type ill-posed operator equations

In this paper, we consider the application of the ensemble Kalman filter for some nonlinear operator equations. According to the structural feature of the nonlinear operator, we construct an iteration process and the corresponding linear observation operator. This construction puts our problem into the frame of 3DVar. Based on the linear observation operator, the ensemble Kalman filter approach is adopted to blend the data into the dynamics to obtain an approximation of the unknown parameter. The method is applied to an inverse potential problem and a nonlinear Fredholm integral equation. The numerical results are compared with Bayesian approach, classical regularization methods including Tikhonov regularization and Landweber iteration, which shows that the proposed algorithm is effective and competitive.