用模拟数据训练无监督模型，提升卫星海面高度观测的精细结构还原能力
Simulation-informed deep learning for enhanced SWOT observations of fine-scale ocean dynamics
- 结合真实观测与仿真数据，通过小波感知神经度量无监督去噪
- 在SWOT数据上显著去除噪声，保留细尺度特征优于现有方法
- 无需大量标注数据，适合海洋数据同化与超分辨率等场景
细尺度海洋过程对海洋动力学至关重要，但受卫星与现场观测限制，难以准确捕捉。表面水与海洋地形（SWOT）任务提供高分辨率海面高度（SSH）数据，但噪声常掩盖细尺度结构。现有方法在噪声数据下表现不佳，或需大量有监督训练，限制了其在真实观测中的应用。本文提出SIMPGEN（仿真引导的生成集合网络度量与先验），一种融合真实SWOT观测与仿真参考数据的无监督对抗学习框架。SIMPGEN利用小波感知神经度量区分噪声与清洁场，指导真实的SSH重构。应用于SWOT数据时，SIMPGEN有效去除噪声，比现有神经方法更好地保留细尺度特征。该鲁棒的无监督方法不仅提升了SWOT SSH数据的解读能力，也展现出在海洋数据同化与超分辨率中的广泛应用潜力。
原文摘要 · Abstract (English)
Oceanic processes at fine scales are crucial yet difficult to observe accurately due to limitations in satellite and in-situ measurements. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution Sea Surface Height (SSH) data, though noise patterns often obscure fine scale structures. Current methods struggle with noisy data or require extensive supervised training, limiting their effectiveness on real-world observations. We introduce SIMPGEN (Simulation-Informed Metric and Prior for Generative Ensemble Networks), an unsupervised adversarial learning framework combining real SWOT observations with simulated reference data. SIMPGEN leverages wavelet-informed neural metrics to distinguish noisy from clean fields, guiding realistic SSH reconstructions. Applied to SWOT data, SIMPGEN effectively removes noise, preserving fine-scale features better than existing neural methods. This robust, unsupervised approach not only improves SWOT SSH data interpretation but also demonstrates strong potential for broader oceanographic applications, including data assimilation and super-resolution.
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