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This paper implements a weighted ensemble Kalman filter for tracking time-evolving sound speed profiles. The approach first updates the particles following the procedure of the ensemble Kalman filter and then resamples the updated particles according to their importance weights. The weights are evaluated by the classical geoacoustic inversion likelihood obtained from the Bartlett power objective functions. Example illustrates that the new method outperforms the ensemble with a comparable computational load.


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