Bayesian geoacoustic inversion of self-noise in shallow water

The geoacoustic inversion problem based on tow-ship noise(self-noise) in shallow water is studied,and a Bayesian inversion method based on adaptive importance sampling is proposed to evaluate the inversion uncertainty.The tonal components at low frequencies of self-noise are utilized in the inversion.And the mixed gaussian function is used to propose the acoustic field samples adaptively to cover the high-probability distributed area of the parameters.The Posterior Probability Densities(PPDs) of parameters are estimated iteratively and effectively from the statistical analysis of acoustic field samples.The results of simulations show that,the water depth,seabed parameters and array parameters can be estimated accurately in self-noise inversion.Besides,the proposed method provides effective evaluations of PPDs while requiring significantly less acoustic field samples than Fast Gibbs Sampler(FGS).The method is verified through the processing of sea-trial data in a shallow water environment,and the geoacoustic parameters are inverted.The result shows that,the predicted Transmission Loss(TL) is consistent with the variations of spectral line intensity along the array,demonstrating the reliability of the inversion results.