Trans-dimensional Bayesian geoacoustic inversion in shallow water

To deal with the inversion problem when the spatial structure of seafloor sediments is unknown, a trans-dimensional particle filter method is proposed in this paper, where the cross-spectral density of the pressure field is used to estimate the sediment layering structure and geoacoustic parameters. The simulation results show that the number of sediment layers and geoacoustic parameters can be effectively estimated using the proposed method, and the parallel particle calculation make this method more efficient than reversible jump Monte Carlo Markov chain (rjMCMC) sampling. A linear frequency-modulated signal received by a vertical line array in the South China Sea is processed using the proposed trans-dimensional particle filter. The inversion results of the sediment layers and geoacoustic parameters are similar to those acquired by rjMCMC inversion. The number of sediment layers and the posterior probability density of parameters can be effectively estimated using this method.