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在过渡路径理论中提高时间统计的稳定性,使用稀疏的数据
Gage Bonner1, F J Beron-Vera1, M J Olascoaga2
1Department of Atmospheric Sciences, Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, Florida 33149, USA.
Chaos (Woodbury, N.Y.)
|June 15, 2023
概括
一种新的数据聚类方法改进了Ulam分析海洋流浪者数据的方法,提高了跟踪海洋生物运动的过渡路径理论 (TPT) 计算的稳定性.
科学领域:
- 海洋学 海洋学 海洋学
- 动态系统 动态系统
- 计算物理 计算物理
背景情况:
- 乌拉姆的方法使用马尔科夫链来进行域分析,对随机运算符进行分类.
- 过渡路径理论 (TPT) 分析了轨迹,例如海洋漂流者的轨迹.
- 了解流浪者的运动对于跟踪像Sargassum这样的海洋现象至关重要.
研究的目的:
- 为了调查乌拉姆分析卫星追踪流浪者数据的方法的稳定性.
- 将TPT应用于从西非到墨西哥湾的流浪者轨迹,灵感来自萨尔加索运动.
- 为TPT分析开发一个更强大的分密化方案.
主要方法:
- 将Ulam的方法和TPT应用于全球流浪者计划数据.
- 与标准经度-度单元覆盖面进行比较,采用一种新的数据聚类方法.
- 研究了细胞数量对过渡时间稳定性的影响.
- 提出了一个通用的TPT过渡时间统计.
主要成果:
- 乌拉姆的方法中的标准正规网格离散显示了变化细胞数量的过渡时间的显著不稳定性.
- 基于数据集群的离散提供了稳定的过渡时间计算.
- 一般化的TPT统计数据可以将域区分为动态连接的区域.
结论:
- 选择离散对海洋学数据TPT分析的可靠性产生重大影响.
- 数据驱动的集群为Ulam的方法提供了更稳定,更准确的方法.
- 提出的方法增强了复杂的海洋运输现象的分析.
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