As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these methodologies fall short in adequately addressing multi-spatial coupling effects and spatiotemporal weighting, particularly in scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. Experimental results indicate that STA-ConvLSTM provides precise predictions of sound speed. Specifically, relative to the measured data, it achieved a root mean square error (RMSE) of approximately 0.57 m/s and a mean absolute error (MAE) of about 0.29 m/s. Additionally, when compared to single-dimensional spatial analysis, incorporating multi-spatial scale considerations yielded superior predictive performance.

Deep learning-based data-driven methods have gained significant attention in underwater acoustic source localization. However, their performance is often constrained by environmental disturbances and the scarcity of real-world underwater acoustic data. To address these issues, this paper presents a novel network termed MTCL-Net, a multi-task learning network that incorporates contrastive learning as an auxiliary task for underwater acoustic source ranging. A standard dataset and a perturbed dataset to simulate real underwater interferences are constructed based on known environmental parameters in this method. A Siamese dual-branch architecture is employed, where a contrastive learning task enables the automatic extraction of position-related features. The network jointly optimizes three tasks: source localization in perturbed environments, localization on the standard dataset, and position similarity discrimination, which improves the robustness and generalization ability. The experimental results on simulated and sea trial data demonstrate that MTCL-Net outperforms traditional matched field processing (MFP), single-task learning (STL), and multi-task learning based on depth–range (MTL-DR) methods in terms of mean absolute error (MAE) and probability of credible localization (PCL-10%). Specifically, on SWellEx-96 sea trial data, MTCL-Net achieves an MAE of 0.17 km and a PCL-10% of 90.36%. Moreover, the proposed method only needs a few samples for fine-tuning and shows strong practicality in uncertain marine environments.

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.

A large number of seabed depressions, covering an area of 2500 km² in the Xisha Massif of the South China Sea, are investigated using newly collected high-resolution acoustic data. By analyzing the morphological features and seismic attributes of the focused fluid flow system, five geological structures are recognized and described in detail, including pockmarks, volcanic mounds, pipes, faults, and forced folds. Pockmarks and volcanic mounds occur as clustered groups and their distributions are related to two large-scale volcanic zones with chaotic seismic reflections. Pipes, characterized by disordered seismic reflections, mainly occur within the focused fluid flow zone (FFFZ) and directly link with the large-scale deep volcano and its surrounding areas. Faults and fractures mainly occur along pipes and extend to the seafloor, commonly presenting lateral walls of mega-pockmarks. Forced folds are primarily clustered above volcanic zones and commonly restricted between faults or pipes, characterized by sediment deformations as indicated in seismic profiles. By comprehensive analysis of the above observations and a simplified simulation model, the volcanism-induced hydrothermal fluid activities are argued herein to contribute to these focused fluid flow structures. In addition, traces of suspected submarine instability disasters such as landslides have been found in this sea area, and more observational data will be needed to determine whether seafloor fluid flow zones can be used as a predictor of seafloor instability in the future.