A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints
文献类型：期刊论文
| 作者 | Xu, Dongcan1,2; Liu, Yahao2,3; Kong, Yuan1 |
| 刊名 | JOURNAL OF MARINE SCIENCE AND ENGINEERING
 |
| 出版日期 | 2025-05-28 |
| 卷号 | 13期号:6页码:17 |
| 关键词 | deep learning ocean temperature ocean remote sensing South China Sea ConvLSTM |
| DOI | 10.3390/jmse13061061 |
| 通讯作者 | Liu, Yahao(yhliu@qdio.ac.cn) |
| 英文摘要 | The South China Sea, a vital marginal sea in tropical-subtropical Southeast Asia, plays a globally significant role in marine biodiversity and climate system dynamics. The accurate monitoring of its thermal structure is essential for ecological and climatic studies, yet retrieving subsurface temperature remains challenging due to complex ocean-atmosphere interactions. This study develops a Convolutional Long Short-Term Memory (ConvLSTM) neural network, integrating multi-source satellite remote sensing data, to reconstruct the Ocean Subsurface Temperature Structure (OSTS). To address the multiparameter complexity of temperature retrieval, physical constraints-particularly the heat budget balance of water bodies-are incorporated into the loss function. Experiments demonstrate that the physics-informed ConvLSTM model significantly improves the temperature estimation accuracy by simultaneously optimizing the physical consistency and predictive performance. The proposed approach advances ocean remote sensing by synergizing data-driven learning with thermodynamic principles, offering a robust framework for understanding the South China Sea's thermal variability. |
| WOS关键词 | SUBSURFACE ; SYSTEM ; OCEAN |
| 资助项目 | National Key Research and Development Program of China ; National Nature Sciences Foundation of China[42176014] ; [2022YFC2808304] |
| WOS研究方向 | Engineering ; Oceanography |
| 语种 | 英语 |
| WOS记录号 | WOS:001516749200001 |
| 出版者 | MDPI |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/202006]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Liu, Yahao |
| 作者单位 | 1.Shandong Univ Sci & Technol, Coll Math & Syst Sci, 579 Qianwangang Rd, Qingdao 266590, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao 266237, Peoples R China 3.Qingdao Marine Sci & Technol Ctr, Lab Ocean Dynam & Climate, Qingdao 266237, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Dongcan,Liu, Yahao,Kong, Yuan. A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints[J]. JOURNAL OF MARINE SCIENCE AND ENGINEERING,2025,13(6):17. |
| APA | Xu, Dongcan,Liu, Yahao,&Kong, Yuan.(2025).A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints.JOURNAL OF MARINE SCIENCE AND ENGINEERING,13(6),17. |
| MLA | Xu, Dongcan,et al."A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints".JOURNAL OF MARINE SCIENCE AND ENGINEERING 13.6(2025):17. |
入库方式： OAI收割
来源：海洋研究所
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