Abstract A Kohonen artificial neural network (ANN) was used to classify patterns of causal relationships between the level of geomagnetic activity in the auroral zone and plasma and magnetic field parameters in the body of an interplanetary magnetic cloud (IMO). Terrestrial and satellite observations during 33rd interplanetary magnetic clouds recorded from 1998 to 2012 are examined in detail. Experiments with the ANN during its fast training show that substorm discrimination by their intensity by three classes plus a “collector” for collecting atypical events is optimal for the study. An analysis of the classification result studies showed that each selected class of substorms corresponds to a specific set of perturbations of the plasma parameters and the magnetic field of the IMO. Using the integral characteristics of the plasma and the IMF components as input parameters of the ANN allowed us to detect the levels of the expected intensity of the AL index with an accuracy of up to 70%. The created ANNs can be used to restore the AL index both during periods of isolated magnetospheric substorms and during periods of a series of continuous successive substorms, one after another.

中文翻译：

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太阳风磁云引起的亚暴地磁活动的神经网络分类

摘要 使用 Kohonen 人工神经网络 (ANN) 对极光区地磁活动水平与行星际磁云 (IMO) 体内的等离子体和磁场参数之间的因果关系模式进行分类。详细检查了 1998 年至 2012 年记录的第 33 次行星际磁云期间的地面和卫星观测。在 ANN 的快速训练期间进行的实验表明，通过三个类别的强度和一个用于收集非典型事件的“收集器”来区分亚暴是本研究的最佳选择。对分类结果研究的分析表明，每个选定的亚暴类别对应于一组特定的等离子体参数和 IMO 磁场的扰动。使用等离子体的积分特性和 IMF 分量作为 ANN 的输入参数，使我们能够以高达 70% 的准确度检测 AL 指数的预期强度水平。创建的人工神经网络可用于在孤立的磁层亚暴期间和在一系列连续连续亚暴期间一个接一个地恢复 AL 指数。