Sequential geoacoustic inversion and source tracking using Ensemble Kalman-particle filter

The estimation of geoacoustic parameters of such a varying field can be reformulated as a sequential filter track problem. Sequential Bayesian filtering is widely applied in ocean geoacoustic inversion such as ensemble Kalman filter and particle filter. We introduce the ensemble Kalman particle filter to a sequential geoacoustic inversion problem in shallow water. This filter combines the advantages of particle filter and ensemble Kalman filter so its ability of tracking dynamical geoacoustic parameters is improved. The sequential filtering method is demonstrated using simulated data with matched-field inversion method in a changing environment.