标题： 一种恢复不完整海洋流测量的运输方法 显示英文标题

标题： A transport method for restoring incomplete ocean current measurements

摘要： 遥感海洋数据常常导致测量区域的覆盖不完整。 这可能会限制数据的可解释性以及识别与海洋动力学相关的相干特征。 存在几种填补缺失海洋数据的方法，这些方法通常基于将测量值投影到基函数或统计模型上。 在此，我们使用一种受图像处理算法启发的信息传输方法。 该方法旨在通过传播和扩散特征的信息而不是场本身来恢复数据中的空白。 由于该方法不涉及拟合或投影，包含测量值的区域可以保持不变，并且该方法提供了对局部信息传递范围的控制。 该方法应用于高频雷达测量的海洋表层流数据。 这是一个相关应用，因为数据覆盖可能时有时无，填补数据空白对于数据的可用性至关重要。 考虑了两个具有不同空间尺度的区域的应用。 通过系统地屏蔽测量值并比较这些位置恢复的数据与实际测量值来测试该方法的准确性和鲁棒性。 这些结果表明，即使在局部缺失数据点占比较大时，恢复的速度误差也处于原始数据的固有误差范围内（例如，速度大小为$<10$%，速度方向为$<3$%）。 结果对模型参数相对不敏感，有助于为新应用预先选择默认参数。 显示英文摘要

摘要： Remote sensing of oceanographic data often yields incomplete coverage of the measurement domain. This can limit interpretability of the data and identification of coherent features informative of ocean dynamics. Several methods exist to fill gaps of missing oceanographic data, and are often based on projecting the measurements onto basis functions or a statistical model. Herein, we use an information transport approach inspired from an image processing algorithm. This approach aims to restore gaps in data by advecting and diffusing information of features as opposed to the field itself. Since this method does not involve fitting or projection, the portions of the domain containing measurements can remain unaltered, and the method offers control over the extent of local information transfer. This method is applied to measurements of ocean surface currents by high frequency radars. This is a relevant application because data coverage can be sporadic and filling data gaps can be essential to data usability. Application to two regions with differing spatial scale is considered. The accuracy and robustness of the method is tested by systematically blinding measurements and comparing the restored data at these locations to the actual measurements. These results demonstrate that even for locally large percentages of missing data points, the restored velocities have errors within the native error of the original data (e.g., $<10$% for velocity magnitude and $<3$% for velocity direction). Results were relatively insensitive to model parameters, facilitating a priori selection of default parameters for de novo applications.