Ocean fronts, widespread across the global ocean, cause abrupt shifts in physical properties such as temperature, salinity, and sound speed, significantly affecting underwater acoustic communication and detection. While past research has concentrated on qualitative analysis and small-scale research on ocean front sections, a comprehensive analysis of ocean fronts’ characteristics and their impact on underwater acoustics is lacking. This study employs high-resolution reanalysis data and in situ observations to accurately identify ocean fronts, sound speed structures, and acoustic propagation features from over six hundred thousand Kuroshio Extension Front (KEF) sections. Utilizing marine big data statistics and machine learning evaluation metrics such as out-of-bag (OOB) error and Shapley values, this study quantitatively assesses the variations in sound speed structures across the KEF and their effects on acoustic propagation shifts. This study’s key findings reveal that differences in sound speed structure are significantly correlated with KEF strength, with the channel axis depth and conjugate depth increasing with front strength, while the thermocline intensity and depth excess decrease. Acoustic propagation features in the KEF environment exhibit notable seasonal variations.

Mesoscale eddies are pivotal oceanographic phenomena affecting marine environments. Accurate and stable identification of these eddies is essential for advancing research on their dynamics and effects. Current methods primarily focus on identifying Cyclonic and Anticyclonic eddies (CE, AE), with anomalous eddy identification often requiring secondary analyses of sea surface height anomalies and eddy center properties, leading to segmented data interpretations. This study introduces a deep learning model integrating multi-source fusion data with a Squeeze-and-Excitation (SE) attention mechanism to enhance the identification accuracy for both normal and anomalous eddies. Comparative ablation experiments validate the model’s effectiveness, demonstrating its potential for more nuanced, multi-source, and multi-class mesoscale eddy identification. This approach offers a promising framework for advancing mesoscale eddy identification through deep learning.

In response to the demand for high-precision acoustic support under the condition of limited data, this study utilized high-resolution reanalysis data and in situ observation data to extract the Kuroshio Extension Front (KEF) section through front-line identification methods. By combining the parameterized oceanic front model and the statistical features of big data, the parameterized oceanic front was reconstructed. A proxy dataset was generated using the Latin hypercube sampling method, and the sound speed reconstruction model based on the PIX2PIX model was trained and validated using single sound speed profiles at different positions of the oceanic front, combined with the parameterized oceanic front model. The experimental results show that the proposed sound speed reconstruction model can significantly improve the reconstruction accuracy by introducing the parameterized front model as an additional input, especially in the shallow-water area. The mean absolute error (MAE) of the full-depth sound speed reconstruction for this model is 0.63~0.95 m·s⁻¹, and the structural similarity index (SSIM) is 0.76~0.78. The MAE of the sound speed section within a 1000 m depth is reduced by 6.50~37.62%, reaching 1.95~3.31 m·s⁻¹. In addition, the acoustic support capabilities and generalization of the model were verified through ray tracing models and in situ data. This study contributes to advancing high-precision acoustic support in data-limited oceanic environments, laying a solid groundwork for future innovations in marine acoustics.

Accurately capturing the Kuroshio’s main axis and its multiscale frontal variations remains challenging due to the constraints of traditional fixed-section extraction methods. Here, we develop an adaptive iterative tracking algorithm utilizing high-resolution reanalysis data (2002–2024) that dynamically adjusts search directions and cross-sections via local velocity vectors, integrated with a dynamic step size and two-dimensional validation. Applying a multiscale variability decomposition framework across four key regions reveals distinct spatiotemporal dynamics. The North Equatorial Current bifurcation zone exhibits a significant strengthening trend driven by seasonal zonal and decadal meridional flows. Conversely, the Kuroshio east of Taiwan is dominated by high-frequency mesoscale processes (~70%) with a semi-annual cycle and no long-term trend. The East China Sea front maintains a highly stable seasonal meridional signal (25%). Crucially, the Luzon Strait intrusion shows a significant long-term weakening trend (~0.0029 m·s⁻¹·a⁻¹, p < 0.01), characterized by eastward strengthening and northward weakening, with ENSO significantly modulating its seasonal cycle. This approach substantially reduces systematic extraction errors compared to traditional fixed-section methods, as independently verified using satellite SST frontal gradients (median deviation < 0.2°), providing critical observational evidence for understanding western boundary current–marginal sea interactions and their dynamical responses under global warming.

The special eddy field of mesoscale vortices plays an important role in the global shipping process. The statistical morphology of mesoscale vortices observed via global satellites and the numerical simulation of the ocean are applied to the simulation of computational fluid dynamics, which can more truly reflect the influence of mesoscale vortices on the motion characteristics of underwater vehicles. In this paper, the ALE (Arbitrary Lagrangian–Eulerian) finite element method is used to simulate the random vortex of a submarine in three dimensions (horizontal x, vertical z, height y) and establish quantitative submarine movement characteristics. Our results show that with an increase in mesoscale vortex strength, the effects on the submarine’s speed and displacement increase, but the overall effect is still limited. In the 300 m transmission simulation, the velocity effect is within ±2 m/s, and the displacement effect is within 4 m. The simulation results can be applied to the route optimization algorithm of underwater vehicle automatic navigation and provide a reference for energy consumption calculations and route safety evaluations.