A method and system for predicting ocean sound velocity fields

By constructing a causal network graph and using a KAN network to learn the evolution dynamics of the ocean sound velocity field, combined with future-guided learning and dynamic feedback calibration, the accuracy and efficiency problems of ocean sound velocity field prediction in existing technologies are solved, achieving high-precision and high-efficiency ocean sound velocity field prediction.

CN121599079BActive Publication Date: 2026-06-30SUN YAT SEN UNIV +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SUN YAT SEN UNIV
Filing Date
2025-12-02
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve high-precision and high-efficiency prediction of ocean sound velocity fields. Limited by data acquisition constraints, static and lagging models, or computational resource challenges, it is difficult to achieve a good balance between accuracy and efficiency.

Method used

By quantitatively diagnosing key driving factors from historical and real-time marine environmental data, constructing a causal network graph, using the KAN network to learn the evolution dynamics of the ocean sound velocity field, and performing dynamic feedback calibration during the training phase and the online prediction phase, the prediction of the ocean sound velocity field is achieved.

Benefits of technology

It improves the accuracy and timeliness of ocean sound speed prediction, and can maintain high precision and robustness in complex and ever-changing marine environments, adapting to dynamic changes.

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Abstract

This application discloses a method and system for predicting ocean sound velocity fields, relating to the field of data processing technology. The method includes: quantitatively diagnosing key driving factors affecting the evolution of the ocean sound velocity field in a target sea area from marine environmental data, and constructing a causal network graph based on the information flow and structural equation model of the key driving factors to obtain a core feature set; decomposing the ocean sound velocity field sequence data in the core feature set into spatiotemporally coherent mode data through dynamic mode decomposition, and then mapping it into low-dimensional spatiotemporal features; using a KAN network to learn the evolution dynamics of the ocean sound velocity field based on the low-dimensional spatiotemporal features, and outputting the ocean sound velocity field prediction results for future times; using future-guided learning in the training phase of the KAN network, using real observation data at a future set time as auxiliary supervision signals; and using a dynamic feedback mechanism in the online prediction phase to calibrate the ocean sound velocity field prediction results based on real-time observation data. This application can improve the accuracy of ocean sound velocity prediction.
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