OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916925227925504 |
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| author | Li, Bo Feng, Yingqi Jin, Ming Zheng, Xin Tang, Yufei Cherubin, Laurent Liew, Alan Wee-Chung Wang, Can Lu, Qinghua Yao, Jingwei Pan, Shirui Zhang, Hong Zhu, Xingquan |
| author_facet | Li, Bo Feng, Yingqi Jin, Ming Zheng, Xin Tang, Yufei Cherubin, Laurent Liew, Alan Wee-Chung Wang, Can Lu, Qinghua Yao, Jingwei Pan, Shirui Zhang, Hong Zhu, Xingquan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21570 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories Li, Bo Feng, Yingqi Jin, Ming Zheng, Xin Tang, Yufei Cherubin, Laurent Liew, Alan Wee-Chung Wang, Can Lu, Qinghua Yao, Jingwei Pan, Shirui Zhang, Hong Zhu, Xingquan Machine Learning 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. |
| title | OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.21570 |