用扩散对抗网络填补稀疏浮标数据中的海洋盐度空缺
OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories
- 构建扩散对抗框架，融合物理先验知识建模盐度变化
- 在稀疏浮标轨迹下实现高精度盐度插补，优于传统方法
- 适合海洋学研究者与气候模型开发者使用
海洋盐度在洋流、气候和海洋生态系统中起着关键作用，但其观测数据常呈稀疏、不规则且带有噪声，尤其在基于浮标的数据库中更为明显。传统方法如遥感和最优插值依赖线性与平稳性假设，受限于云层遮蔽、传感器漂移及卫星重访周期长等问题。尽管机器学习模型具备灵活性，但在严重数据稀疏情况下表现不佳，且缺乏将物理协变量合理融入模型的系统方法。本文提出海洋盐度插补系统（OASIS），一种新颖的扩散对抗框架，旨在解决上述挑战，通过生成式建模在稀疏浮标轨迹基础上实现高保真盐度填补。
原文摘要 · Abstract (English)
Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy, especially in drifter-based datasets. Traditional approaches, such as remote sensing and optimal interpolation, rely on linearity and stationarity, and are limited by cloud cover, sensor drift, and low satellite revisit rates. While machine learning models offer flexibility, they often fail under severe sparsity and lack principled ways to incorporate physical covariates without specialized sensors. In this paper, we introduce the OceAn Salinity Imputation System (OASIS), a novel diffusion adversarial framework designed to address these challenges.
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