Extracting balanced geostrophic motions (BM) from sea surface height (SSH) observations obtained by wide-swath altimetry holds great significance in enhancing our understanding of oceanic dynamic processes at submesoscale wavelength. However, SSH observations derived from wide-swath altimetry are characterized by high spatial resolution while relatively low temporal resolution, thereby posing challenges to extract the BM from a single SSH snapshot. To address this issue, this paper proposes a deep learning model called the BM-UBM Network, which takes an instantaneous SSH snapshot as input and outputs the projection corresponding to the BM. Training experiments are conducted both in the Gulf Stream and South China Sea, and three metrics are considered to diagnose model's outputs. The favorable results highlight the potential capability of the BM-UBM Network to process SSH measurements obtained by wide-swath altimetry.

中文翻译：

---

从海面高度快照中提取平衡运动的深度学习方法

从宽幅测高获得的海面高度（SSH）观测中提取平衡地转运动（BM）对于增强我们对亚尺度波长海洋动力过程的理解具有重要意义。然而，源自宽测绘带测高的 SSH 观测具有空间分辨率高而时间分辨率相对较低的特点，从而对从单个 SSH 快照中提取地磁提出了挑战。为了解决这个问题，本文提出了一种称为 BM-UBM Network 的深度学习模型，该模型以瞬时 SSH 快照作为输入，并输出与 BM 对应的投影。在墨西哥湾流和南海进行了训练实验，并考虑了三个指标来诊断模型的输出。有利的结果凸显了 BM-UBM 网络处理通过宽测绘带测高获得的 SSH 测量值的潜在能力。