Deep learning-based high-resolution reconstruction of MASNUM wave data in the northern South China Sea

He Yingqiang; Jin Quan; Jiang Longyu; Jiang Xingjie; Wang Shuo; Zhou Jian; Zou Zhongshui; Zhu Xueming; Zhang Shouwen

doi:10.12284/hyxb20250123

Volume 47 Issue 12

Dec. 2025

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Article Navigation > Haiyang Xuebao > 2025 > 47(12): 185-197

He Yingqiang，Jin Quan，Jiang Longyu, et al. Deep learning-based high-resolution reconstruction of MASNUM wave data in the northern South China Sea[J]. Haiyang Xuebao，2025, 47(12)：185–197 doi: 10.12284/hyxb20250123

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Deep learning-based high-resolution reconstruction of MASNUM wave data in the northern South China Sea

doi: 10.12284/hyxb20250123

1.
School of Marine Sciences, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
2.
College of Marine Science and Technology, Hainan Tropical Ocean University, Sanya 572022, China
3.
Ocean College, Zhejiang University, Zhoushan 316021, China
4.
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China

Received Date: 2025-09-03
Rev Recd Date: 2025-12-04
Publish Date: 2025-12-31

Abstract

Abstract

Ocean waves generally refer to wave phenomena in the ocean. Under extreme conditions, wave heights can exceed 20 meters. Waves are closely related to atmospheric motion, ocean dynamics, thermodynamic processes, and the marine environment. To address the issues of high computational load and slow speed in wave numerical models under high-resolution topography, this study utilizes MASNUM wave model data and conducts high-resolution reconstruction research for waves in the northern South China Sea based on deep learning algorithms. Through comprehensive performance evaluation of traditional linear interpolation methods and various deep learning algorithms—Convolutional Neural Networks, Generative Adversarial Networks, and diffusion models for image reconstruction—in high-resolution wave data reconstruction, results show that compared to traditional linear interpolation methods, deep learning algorithms perform better in uncovering the physical variation patterns of wave data. Furthermore, the diffusion model for image reconstruction significantly outperforms both convolutional neural networks and generative adversarial networks, achieving a comprehensive average root mean square error of merely 0.0103 meters. This finding substantiates the reliability of the reconstructed high-resolution wave data and provides a novel methodological framework for establishing advanced high-resolution ocean wave data reconstruction models.
- deep learning,
- super resolution,
- numerical wave models

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References

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