arXiv cs.AI· Sen Jia, Siqi Wei, Junyu Wang, Zijun Lv, Pan Liu, Meng Xu, Xiyou Fu, Yuxi Sun, Qiang Liu, Shuyu Zhang, Lin Mu·· 13 小时前AI 评分23
YingLan：面向异构观测统一表征学习的多模态海洋基础模型
YingLan: A Multimodal Ocean Foundation Model for Unified Representation Learning from Heterogeneous Observations
AI 导读
研究团队提出多模态海洋基础模型 YingLan，融合物理属性条件动态编码、时空增量耦合（STIC）与海洋状态演化感知预训练，统一处理卫星影像、网格海洋场和浮标观测等异构数据。
正文 · 原文
Authors:Sen Jia, Siqi Wei, Junyu Wang, Zijun Lv, Pan Liu, Meng Xu, Xiyou Fu, Yuxi Sun, Qiang Liu, Shuyu Zhang, Lin Mu
Abstract:Multimodal ocean modeling remains challenging due to substantial heterogeneity in data structures, physical semantics, and spatiotemporal characteristics across satellite imagery, gridded oceanographic fields, and buoy observations. Existing approaches largely focus on task-specific ocean modeling, while learning transferable representations from heterogeneous observations remains an open problem. To address this challenge, we propose YingLan, a multimodal ocean foundation model that integrates physics-attribute-conditioned dynamic encoding, Spatiotemporal Increment Coupling (STIC), and ocean-state evolution-aware pretraining. Specifically, the dynamic encoding mechanism generates adaptive input projections conditioned on physical semantics and observation characteristics, enabling flexible representation of heterogeneous ocean variables. STIC models multiscale spatiotemporal dependencies through cross-scale interactions of latent temporal increments. The pretraining strategy further combines masked observation reconstruction with temporal evolution objectives to facilitate the learning of coherent ocean-state representations across different prediction horizons. YingLan is implemented using MindSpore and trained on Huawei Ascend 910B NPUs, demonstrating its compatibility with domestic AI computing infrastructure. In a seven-day sea surface salinity (SSS) forecasting task conducted in the Shenzhen coastal waters, YingLan achieves an average RMSE of 0.687508 PSU, corresponding to relative error reductions of 0.58%, 5.31%, and 12.42% compared with LangYa, XiHe, and GloNet2, respectively. These results provide initial evidence of the model's effectiveness in regional ocean forecasting, while broader cross-task and cross-regional evaluations remain necessary to assess its generalization capabilities.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.29313 [cs.LG] |
| (or arXiv:2610.29313v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29313 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sen Jia [view email]
[v1]
Wed, 21 Oct 2026 07:09:14 UTC (4,052 KB)
来源：arXiv cs.AI · arxiv.org