由Dst指数中的重复模式揭示的磁层波动的时间组织
Temporal organization of magnetospheric fluctuations unveiled by recurrence patterns in the Dst index
摘要：
磁暴是磁层非线性动力学中最显著的大尺度现象。通过复杂性测度研究地磁活动指数数据中宏观变率的动力学组织，为识别潜在过程和相关时间尺度提供了一种有前途的方法。在这里，我们应用一套从递归量化分析（RQA）和递归网络分析（RNA）的特点，以揭示一些关键的非线性特征的每小时的扰动风暴时间（DST）指数期间的磁暴等正常的变化。我们的研究结果表明，递归为基础的措施可以作为很好的示踪剂的动态复杂性的变化沿非平稳记录的地磁活动。特别是，捕获时间（表征所观察到的动态中的“层流阶段”的典型长度）和递归网络传递性（与系统的有效动力学自由度的数量相关）允许磁暴和静止阶段之间的非常好的区分。一般来说，一些RQA和RNA特征区分风暴和非风暴时间同样好，甚至比其他先前考虑的非线性特征，如赫斯特指数或符号动力学的熵概念。我们的研究结果指出，未来的潜力的复发特性揭示磁层的动态复杂性的时间变化。
英文摘要：
Magnetic storms constitute the most remarkable large-scale phenomena of nonlinear magnetospheric dynamics. Studying the dynamical organization of macroscopic variability in terms of geomagnetic activity index data by means of complexity measures provides a promising approach for identifying the underlying processes and associated time-scales. Here, we apply a suite of characteristics from recurrence quantification analysis (RQA) and recurrence network analysis (RNA) in order to unveil some key nonlinear features of the hourly Disturbance storm-time (Dst) index during periods with magnetic storms and such of normal variability. Our results demonstrate that recurrence-based measures can serve as excellent tracers for changes in the dynamical complexity along non-stationary records of geomagnetic activity. In particular, trapping time (characterizing the typical length of "laminar phases" in the observed dynamics) and recurrence network transitivity (associated with the number of the system's effective dynamical degrees of freedom) allow for a very good discrimination between magnetic storm and quiescence phases. In general, some RQA and RNA characteristics distinguish between storm and non-storm times equally well or even better than other previously considered nonlinear characteristics like Hurst exponent or symbolic dynamics based entropy concepts. Our results point to future potentials of recurrence characteristics for unveiling temporal changes in the dynamical complexity of the magnetosphere.
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