Traditional bathymetry inversion methods that rely on an altimetry-derived gravity anomaly (GA) and/or a vertical gravity gradient anomaly (VGG) have been widely used for bathymetry prediction in the South China Sea. However, few studies attempt new methods to combine multisource gravity data to improve the accuracy of the bathymetry. In this study, we introduce a fully connected deep neural network (FC-DNN) to merge GA, VGG, and the deflection of vertical (DOV) to predict the bathymetry in the South China Sea. Single beam sounding depths were used as sample data for neural network training. Independent shipboard depths and GEBCO2023, topo_25.1, and ETOPO2022 models were applied as validation data. The assessment results showed that the FC-DNN model reached a high precision level with an STD of 49.20 m. More than 70% of the differences between the FC-DNN bathymetric model and other depth models were less than 100 m. Furthermore, the spectral analysis results showed that the FC-DNN bathymetry model has stronger energy in medium and short wavelengths than other models, which indicates that additional gravity field element DOVs can recover richer topographic signals in those particular bands.

Internal waves are crucial for understanding oceanographic parameters such as spatiotemporal distribution and energy transfer. They significantly impact ocean circulation, marine ecosystems, and offshore operations. However, studying internal waves is challenging due to their dynamic nature and the need for effective observation methods. This study investigated nonlinear internal solitary waves (ISWs) in the South China Sea using SSHa data from the SWOT satellite mission (Cycles 2 to 20). The distribution patterns and seasonal variations in ISWs were analyzed, revealing that ISWs are more frequently observed in summer while being rarely detected in winter. By combining SSHa observations with a Mode-1 vertical structure model, the isopycnal displacement, velocity fields, and energy characteristics of ISWs were reconstructed. The results show a maximum isopycnal displacement of 160 m at 400 m depth and peak kinetic energy near the surface (~2000 J/m³) and potential energy at a depth of around 300 m (~9000 J/m³). These findings highlight the vertical variability of ISWs and demonstrate the capability of SWOT data in capturing their fine-scale evolution, providing new opportunities for oceanic research and enhancing our understanding of internal waves’ impact on marine environments and ocean circulation.

Gravity-matching navigation is one of the important means for the covert navigation of underwater vehicles. The production and application of gravity reference maps as core key technologies have a very significant impact on the accuracy of underwater navigation. Firstly, this paper adopts the factor analysis method to obtain the comprehensive results of nine characteristic parameters such as the standard deviation and roughness of the gravity reference map by setting a range sliding window. Secondly, the TERCOM algorithm is introduced to conduct simulation verification calculations within the sliding window. After comparing and verifying with the comprehensive results of the factor analysis characteristic parameters, the limitations of statistical methods in the evaluation of the adaptability of gravity reference maps are analyzed. Thirdly, intelligent processing methods such as the learning vector quantization neural network algorithm and the extreme learning machine are proposed. The characteristic parameters of some sliding window gravity reference maps and the simulation verification results of the TERCOM algorithm are used as training samples to predict the adaptability evaluation effect of underwater gravity navigation for other sliding windows. The results show that the prediction results are generally in good agreement with the simulation verification results of the TERCOM algorithm. Compared with the learning vector quantization neural network algorithm, the extreme learning machine algorithm exhibits superior performance in terms of classification accuracy and computational efficiency.