Abstract
Many geoacoustic models are used to establish the relationship between the physical and acoustic properties of sediments. In this work, Bayesian inversion and model selection techniques are applied to compare combinations of three geoacoustic models and corresponding scattering models—the fluid model with the effective density fluid model (EDFM), the grain-shearing elastic model with the viscosity grain-shearing (VGS(λ)) model, and the poroelastic model with the corrected and reparametrized extended Biot–Stoll (CREB) model. First, the resolution and correlation of parameters for the three models are compared based on estimates of the posterior probability distributions (PPDs), which are obtained by Bayesian inversion using the backscattering strength data. Then, model comparison and selection techniques are utilized to assess the matching degree of model predictions and measurements qualitatively and to ascertain the Bayes factors in favor of each quantitatively. These studies indicate that the fluid and poroelastic models outperform the grain-shearing elastic model, in terms of both parameter resolution and the ability to produce predictions in agreement with measurements for sandy sediments. The poroelastic model is considered to be the best, as the inversion based on it can provide more highly resolved information of sandy sediments. Finally, the attempt to implement geoacoustic inversion with different models provides a relatively feasible remote sensing scheme for various types of sediments under unknown conditions, which needs further validation.
Highlights
As the main means of underwater investigation, acoustic remote sensing techniques have been a research hotspot over the last decades
We introduce a similar comparison for underwater remote sensing by combining the geoacoustic models with corresponding scattering models, using scattering strength measurements to carry out inversion and model selection
The two-dimensional characteristics obtained by the inversion of the fluid model are presented in Figure 5, where the two-dimensional marginal posterior probability distributions (PPDs) are shown on the top right, and parameter correlations are shown on the bottom left
Summary
As the main means of underwater investigation, acoustic remote sensing techniques have been a research hotspot over the last decades. In order to obtain geoacoustic properties of sediments, model-based inversion of acoustic data is a more effective and feasible means compared with grab samples [2]. In acoustic remote sensing, scattering models of water–seabed interface are necessary to calculate observations such as scattering strength in model-based inversion, since the speed and attenuation of sound in sediments cannot be directly measured from a distance. Bayesian model selection techniques are used to evaluate the degree of matching between model predictions and measurements, which are backscattering strengths in this paper This is not the first time that Bayesian techniques have been applied to underwater acoustics. We introduce a similar comparison for underwater remote sensing by combining the geoacoustic models with corresponding scattering models, using scattering strength measurements to carry out inversion and model selection.
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