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.

翻译：精细尺度海洋过程至关重要，但由于卫星和现场观测手段的限制，其准确观测存在困难。表面水与海洋地形（SWOT）任务提供了高分辨率的海面高度（SSH）数据，然而噪声模式常掩盖精细尺度结构。现有方法难以处理含噪数据，或需要大量有监督训练，限制了其在真实观测中的有效性。我们提出了SIMPGEN（面向生成式集成网络的仿真信息度量与先验），这是一种无监督对抗学习框架，将真实SWOT观测与仿真参考数据相结合。SIMPGEN利用小波信息神经度量区分含噪与洁净场，从而指导生成逼真的SSH重建结果。应用于SWOT数据时，SIMPGEN能有效去除噪声，在保持精细尺度特征方面优于现有神经方法。这一鲁棒的无监督方法不仅提升了SWOT SSH数据的解译能力，也展现出在数据同化和超分辨率等更广泛海洋学应用中的巨大潜力。