A fast algorithm for estimating posterior probability distributions of unknown parameters based on support vector machine in matched-field statistical inversion

The goal of matched-field statistical inversion is to derive the posterior probability distributions (PPD) of unknown seafioor parameters from the measured ocean acoustic data.However,traditional approaches to estimate the PPD such as exhaustive searching,Markov Chain Monte Carlo sampling and nearest neighborhood interpolation approximate algorithm are all computationally slow and not suitable for the practical applications.To remedy their drawback, this paper proposed a new fast algorithm based on support vector machine.Our proposed algorithm simplifies the heavy and complex procedure for calculating the posterior probability by forward model and requires less computational time. The numerical and experimental examples validate the proposed algorithm for low-dimensional matched-field inversion.