High-precision habitat mapping can contribute to the identification and quantification of the human footprint on the seafloor. As a representative of seafloor habitats, seabed sediment classification is crucial for marine geological research, marine environment monitoring, marine engineering construction, and seabed biotic and abiotic resource assessment. Multibeam echo-sounding systems (MBES) have become the most popular tool in terms of acoustic equipment for seabed sediment classification. However, sonar images tend to consist of obvious noise and stripe interference. Furthermore, the low efficiency and high cost of seafloor field sampling leads to limited field samples. The factors above restrict high accuracy classification by a single classifier. To further investigate the classification techniques for seabed sediments, we developed a decision fusion algorithm based on voting strategies and fuzzy membership rules to integrate the merits of deep learning and shallow learning methods. First, in order to overcome the influence of obvious noise and the lack of training samples, we employed an effective deep learning framework, namely random patches network (RPNet), for classification. Then, to alleviate the over-smoothness and misclassifications of RPNet, the misclassified pixels with a lower fuzzy membership degree were rectified by other shallow learning classifiers, using the proposed decision fusion algorithm. The effectiveness of the proposed method was tested in two areas of Europe. The results show that RPNet outperforms other traditional classification methods, and the decision fusion framework further improves the accuracy compared with the results of a single classifier. Our experiments predict a promising prospect for efficiently mapping seafloor habitats through deep learning and multi-classifier combinations, even with few field samples.

Acoustic imaging systems are essential for underwater target recognition and localization, but forward-looking sonar (FLS) imagery faces challenges due to seabed variability, resulting in low resolution, blurred images, and sparse targets. To address these issues, we introduce RCF-YOLOv8, an enhanced detection framework based on YOLOv8, designed to improve FLS image analysis. Key innovations include the use of CoordConv modules to better encode spatial information, improving feature extraction and reducing misdetection rates. Additionally, an efficient multi-scale attention (EMA) mechanism addresses sparse target distributions, optimizing feature fusion and improving the network’s ability to identify key areas. Lastly, the C2f module with high-quality feature fusion (C2f-Fusion) optimizes feature extraction from noisy backgrounds. RCF-YOLOv8 achieved a 98.8% mAP@50 and a 67.6% mAP@50-95 on the URPC2021 dataset, outperforming baseline models with a 2.4% increase in single-threshold accuracy and a 10.4% increase in multi-threshold precision, demonstrating its robustness for underwater detection.

The occurrence characteristics of hydrates in the Shenhu area reflect a typical inhomogeneity in terms of spatial distribution. It is difficult to accurately describe the petrophysical properties of a reservoir using a petrophysical model considering a single cementation factor parameter. According to the analysis of a mathematical model and the estimation results of and , the unique structure of foraminiferal sediment particles provides opportunities for forming a diversified hydrate occurrence in the foraminiferal area. In areas where hydrates are thin and interbedded, hydrate reservoirs are generally three-phase media, with obvious thermoelastic properties. Therefore, the parameters of the three characteristic models of the pore-filling model, particle cementation model, and thermodynamic elastic model are all included in the correction model. The weights of the influence factors are then changed to realize an accurate description of the petrophysical characteristics of the correction model in different drilling areas and at different formation depths, reducing the limitations of using a single petrophysical model to describe the petrophysical characteristics of heterogeneous regions under the influence of multiple factors.

The geoacoustic and physical properties of the bottom, as well as spatial distribution, are crucial factors in analyzing the underwater acoustic field structure and establishing a geoacoustic model. Acoustic inversion has been widely used as an economical and effective method to obtain multi-parameters of the bottom. Compared with traditional inversion methods based on acoustic propagation models, acoustic backscattering models are more suitable for multi-parameter inversion, because they contain more bottom information. In this study, a Bayesian inversion method based on an acoustic backscattering model is proposed to obtain bottom multi-parameters, including geoacoustic parameters (the sound speed and loss parameter), partial physical parameters of the sediment, and statistical parameters of the seafloor roughness and sediment heterogeneity. The bottom was viewed as a kind of fluid medium. A high-frequency backscattering model based on fluid theory was adopted as the forward model to fit the scattering strength between the model prediction and the measured data. The Bayesian inversion method was used to obtain the posterior probability density (PPD) of the inversion parameters. Parameter estimation, uncertainty, and correlation were acquired by calculating the maximum a posterior (MAP), the mean values, the one-dimensional marginal distributions of the PPD, and the covariance matrix. Finally, the high-frequency bottom backscattering strength from the Quinault Range site was employed for inversion tests. The estimated values and uncertainties of various bottom parameters are presented and compared with the directly measured bottom parameters. The comparison results demonstrate that the method proposed herein can be used to estimate the sediment/water sound speed ratio, the sediment/water density ratio, and the spectral exponent of the roughness spectrum effectively and reliably.

The natural gas hydrate deposit area in the Shenhu area in north of the South China Sea (SCS) is an important position to carry out research on hydrate exploration and development. Strengthening the understanding of the gas hydrate accumulation and migration system in the Shenhu area will help to scientifically evaluate and guide the hydrate resource potential and exploration work in this area. Based on the space time slice analysis of coherent and instantaneous frequency attribute, the distribution position and importance of the two NWW-SEE deep faults and the splitting structure of SW direction angle are clarified; Two types of natural gas migration models in the study area are constructed: the deep fault control type and the splitting control type; The deep gas diffusion vortex is found, and it is inferred that there is a deep gas source between the two fault layers. It is clear that there are three migration modes of natural gas in the study area: free gas phase diffusion, water-soluble gas phase migration with water medium and buoyancy of independent gas phase; and the migration path and mode of natural gas was proposed in the study area.