Oceanic eddy detection and lifetime forecast using machine learning methods

Ashkezari Mohammad D.; Hill Christopher N.; Follett Christopher N.; Forget Gael; Follows Michael J.

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Oceanic eddy detection and lifetime forecast using machine learning methods

机译：使用机器学习方法进行海洋涡流检测和寿命预测

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We report a novel altimetry-based machine learning approach for eddy identification and characterization. The machine learning models use daily maps of geostrophic velocity anomalies and are trained according to the phase angle between the zonal and meridional components at each grid point. The trained models are then used to identify the corresponding eddy phase patterns and to predict the lifetime of a detected eddy structure. The performance of the proposed method is examined at two dynamically different regions to demonstrate its robust behavior and region independency.

机译：我们报告了一种新颖的基于高程的机器学习方法，用于涡流识别和表征。机器学习模型使用地转速度异常的每日地图，并根据每个网格点处纬向和经向分量之间的相角进行训练。然后，将训练后的模型用于识别相应的涡流相位模式并预测检测到的涡流结构的寿命。在两个动态不同的区域检查该方法的性能，以证明其鲁棒的行为和区域独立性。

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来源
《Geophysical Research Letters》 |2016年第23期|共8页
作者
Ashkezari Mohammad D.; Hill Christopher N.; Follett Christopher N.; Forget Gael; Follows Michael J.;
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正文语种 eng
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关键词
ocean; eddy; machine learning; eddy lifetime; remote sensing;

机译：海洋;涡流;机器学习;涡流寿命;遥感;

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1. Oceanic eddy detection and lifetime forecast using machine learning methods [J] . Ashkezari Mohammad D., Hill Christopher N., Follett Christopher N., Geophysical Research Letters . 2016,第23期

机译：使用机器学习方法进行海洋涡流检测和寿命预测
2. Medium-Term Forecasting of Loop Current Eddy Cameron and Eddy Darwin Formation in the Gulf of Mexico With a Divide-and-Conquer Machine Learning Approach [J] . Wang Justin L., Zhuang Hanqi, Cherubin Laurent M., Journal of Geophysical Research, C. Oceans: JGR . 2019,第8期

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7. A novel feature extraction method of eddy current testing for defect detection based on machine learning [O] . Liyuan Yin, Bo Ye, Zhaolin Zhang, 2019

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Oceanic eddy detection and lifetime forecast using machine learning methods

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