Transdimensional particle filtering for geoacoustic inversion applied to vertical and horizontal arrays

Abstract

In this study, a transdimensional particle filtering method is proposed to simultaneously estimate a geoacoustic model and its associated parameters, whose computational efficiency is improved through a mechanism of parallel calculations. The proposed method is first applied to seabed bottom loss derived from vertical array data collected in the Yellow Sea in 2016. The thickness and number of estimated sediment layers are close to those obtained in previous studies. Moreover, particle filtering has an intrinsic advantage in sequential processing, making it a straightforward method for mapping range-dependent geoacoustic properties of a horizontal array towed from a moving ship. Range-dependent inversion is conducted for a spatial varying environment, and the data are collected from an experiment conducted in the South China Sea in 2022. The inversion results suggest that the sediment layer and associated geoacoustic parameters can be effectively tracked and that the thickness and number of estimated sediment layers are consistent with those obtained by reversible jump Markov chain Monte Carlo method.