OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories
Machine Learning
2025-09-01 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2508.21570,
title = {OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories},
author = {Bo Li and Yingqi Feng and Ming Jin and Xin Zheng and Yufei Tang and Laurent Cherubin and Alan Wee-Chung Liew and Can Wang and Qinghua Lu and Jingwei Yao and Shirui Pan and Hong Zhang and Xingquan Zhu},
journal= {arXiv preprint arXiv:2508.21570},
year = {2025}
}
Comments
CIKM 2025 Accepted