Integrating Multiple-Try DREAM(ZS) to Model-Based Bayesian Geoacoustic Inversion Applied to Seabed Backscattering Strength Measurements

Zou Zou,Li Li,Yu Yu,Qi Qi,Hou Hou,Zhai Zhai

doi:10.3390/jmse7100372

Abstract

The key to model-based Bayesian geoacoustic inversion is to solve the posterior probability distributions (PPDs) of parameters. In order to obtain PPDs more efficiently and accurately, the state-of-the-art Markov chain Monte Carlo (MCMC) method, multiple-try differential evolution adaptive Metropolis(ZS) (MT-DREAM(ZS)), is integrated to the inverse problem because of its excellent ability to fully explore the posterior space of parameters. The effective density fluid model (EDFM), which is derived from Biot–Stoll theory to approximate the poroelastic model, and the published field measurements of backscattering strength are adopted to implement the inversion. The results show that part of the parameters can be estimated close to the measured values, and the PPDs obtained by dual-frequency inversion are more concentrated than those of single-frequency inversion because of the use of more measured backscattering strength data. Otherwise, the comparison between the predicted backscattering strength of dual-frequency inversion results and Jackson’s prediction shows that the solutions of the inverse problem are not unique and may have multiple optimal values. Indeed, the difference between the two predictions is essentially the difference in the estimation of the contribution of volume scattering to the total scattering. Nevertheless, both results are reasonable due to the lack of measurement of volume scattering parameters, and the inversion results given by the posterior probabilities based on the limited measurements and the adopted model are still considered to be reliable.

Highlights

In the field of oceanography, high-frequency underwater sound is widely used in active sonar to detect underwater targets such as submarines, mines, underwater structures, and animals with a relatively adequate resolution [1,2]
Model-based Bayesian geoacoustic inversion is the process of obtaining parameters of sediment properties by Bayesian inversion based on a particular geoacoustic model and measured model outputs
The effective density fluid model (EDFM), which is a convenient approximation for the poroelastic model, and the published field measurements are adopted to implement the inversion

Summary

Introduction

In the field of oceanography, high-frequency underwater sound is widely used in active sonar to detect underwater targets such as submarines, mines, underwater structures, and animals with a relatively adequate resolution [1,2]. In order to further reduce the mismatch between poroelastic model prediction and measurements, the physics of grain–grain contact and multiple scattering losses were added, and the input parameters were adjusted to increase the efficiency of simulating multiple sediment types [14]. Such poroelastic models have inherent advantages over the fluid model for sandy sediment, they suffer from more computational cost. To solve this problem, an EDFM derived from Biot–Stoll theory was used to model acoustic scattering at the seabed–water interface [15]. As the fluid model does not include grain–grain contact by fiat, additional acoustic dispersion and attenuation are added to EDFM though additional physical mechanisms, which are the transfer of heat between the liquid and solid at low frequencies and the effect of granularity at high frequencies [18]

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