Abstract

The paper proposes an automated method for analyzing ionospheric parameters and detecting ionospheric anomalies. The method is based on a generalized multicomponent model of ionospheric parameters (GMCM) developed by the authors. The model identification is based on an integrated approach combining the wavelet-transform methods with the autoregressive-integrated moving average models (ARIMA models). The paper provides estimates of the method efficiency, describes the operations of detecting ionospheric anomalies and evaluating their parameters. On the example of ionospheric parameter processing (the ionospheric critical frequency (foF2)) for the Kamchatka region, we demonstrate the possibility of applying the method in on-line mode (as data become available to system). On the basis of the method, we detected shot-period anomalous changes proceeding magnetic storms and characterizing the occurrences of oscillatory processes in the ionosphere at the background of increased solar activity. The method has been implemented in the “Aurora” system for complex geophysical data analysis (http://lsaoperanalysis.ikir.ru/lsaoperanalysis.html).

中文翻译：

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电离层参数建模和电离层扰动检测方法

摘要

本文提出了一种分析电离层参数和检测电离层异常的自动化方法。该方法基于作者开发的电离层参数 (GMCM) 的广义多分量模型。模型识别基于将小波变换方法与自回归积分移动平均模型（ARIMA 模型）相结合的集成方法。该论文提供了方法效率的估计，描述了检测电离层异常和评估其参数的操作。以堪察加地区的电离层参数处理（电离层临界频率 (foF2)）为例，我们展示了在在线模式下应用该方法的可能性（因为数据可供系统使用）。在方法的基础上，我们检测到磁暴和电离层振荡过程在太阳活动增加的背景下发生的射击周期异常变化。该方法已在用于复杂地球物理数据分析的“Aurora”系统中实现（http://lsaoperanalysis.ikir.ru/lsaoperanalysis.html）。