Dispersion processes in environmental flows have been traditionally studied under the strong assumption of homogeneous, isotropic and stationary turbulence. To overcome this limitation, we propose a new approach that combines autocorrelation analysis of simulated Lagrangian trajectories together with unsupervised clustering. To test the approach, we consider several dynamic scenarios around a coastal gulf, subject to different forcing, in order to compare our method with other approaches. Lagrangian trajectories forced by the varying coastal circulation exhibited different behaviors, looping and non-looping paths, and produced a variety of Lagrangian autocorrelation functions. Our approach proves to be able to reveal spatio-temporal variations in ocean dispersion processes without any a priori knowledge of the character of the trajectories. Clusters based on the autocorrelation functions are associated to different inhomogeneous dispersion processes. Finally, we propose a new stochastic model capable of predicting the different forms of autocorrelations.

中文翻译：

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使用聚类方法揭示海洋和沿海环流中的非均匀扩散过程

传统上，环境流中的扩散过程是在均匀、各向同性和稳态湍流的强烈假设下进行研究的。为了克服这一限制，我们提出了一种新方法，它将模拟拉格朗日轨迹的自相关分析与无监督聚类相结合。为了测试该方法，我们考虑了沿海海湾周围受不同强迫影响的几种动态场景，以便将我们的方法与其他方法进行比较。不同的沿海环流迫使拉格朗日轨迹表现出不同的行为、循环和非循环路径，并产生各种拉格朗日自相关函数。事实证明，我们的方法能够揭示海洋扩散过程的时空变化，而无需任何关于轨迹特征的先验知识。基于自相关函数的聚类与不同的非均匀色散过程相关联。最后，我们提出了一种新的随机模型，能够预测不同形式的自相关。