The modern discrimination of sediment is based on acoustic intensity (backscatter) information from high-resolution multibeam echo-sounder systems (MBES). The backscattering intensity, varying with the angle of incidence, reveals the characteristics of seabed sediment. In this study, we propose a novel unsupervised acoustic sediment classification method based on the K-medoids algorithm using multibeam backscattering intensity data. In this method, we use the Lurton parameters model, which is the relationship between the backscattering intensity and incidence, to obtain the backscattering angle corresponding curve, and we use the genetic algorithm to fit the curve by the least-squares method. After extracting the four relevant parameters of the model when the ideal fitting effect was achieved, we input the characteristic parameters obtained from the fitting to the K-medoids clustering model. To validate the proposed classification method, we compare it with the self-organizing map (SOM) neural network classification method under the same parameter settings. The results of the experiment show that when the seabed sediment category is less than or equal to 3, the results of the K-medoids algorithm and the SOM neural network are approximately identical. As the sediment category increases, the SOM neural network shows instability, and it is impossible to see the clear boundaries of the seabed sediment, while the K-medoids category is 5 and the seabed sediment classification is correct. After comparing with field in situ seabed sediment sampling along the MBES survey line, the sediment classification method based on K-medoids is consistent with the distribution of the field sediment sampling. The classification accuracies for bedrock, sandy clay, and silty sand are all above 90%; those for gravel and clay are nearly 80%, and the overall accuracy reaches 89.7%.

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.