The propagation speed of internal waves is a fundamental parameter for understanding their physical mechanisms, dynamic behavior, and environmental impact. However, traditional estimation methods are typically based on numerical simulations or sparse in-situ observations, which limit their accuracy and scalability, and results in a significant scarcity of available phase speed datasets. To overcome these challenges, we propose a physics-informed and data-driven model for estimating internal wave phase speed from satellite imagery. The proposed model incorporates three key innovations: (1) the integration of theoretical equations (KdV, BO, and eKdV equations) as physical constraints to ensure consistency with real-world ocean dynamics; (2) the adoption of an adaptive ensemble learning framework that fuses data-driven and physical-informed features to improve model robustness and prediction accuracy; and (3) the introduction of a transfer learning strategy to mitigate discrepancies between theoretical predictions and observational real-world internal wave results. Experimental results demonstrate that the model achieves superior performance across varying water depths, with an average RMSE of 0.04 m/s, MRE of 2.5%, and R2 of 98.8% on the testing set. Additionally, the model was applied to the South China Sea, revealing a distinct propagation pattern: average phase speed initially increased (from 2.427 m/s to 2.53 m/s), then decreased (to 1.464 m/s), and subsequently increased again (to 1.703 m/s) as internal waves propagated westward across the Dongsha Islands and Hainan Island. The model was further validated on a global scale, achieving an average percentage error of 4.95%, confirming its scalability and generalization capability. This study presents an efficient and automated approach for accurately retrieving internal wave phase speed.
扫码关注我们
求助内容:
应助结果提醒方式:
