Geomagnetic indices quantify the disturbance caused by the solar activity on a planetary scale or in particular regions of the Earth. Among them, the SYM-H and ASY-H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the magnetic field at midlatitude with a 1-min resolution. Their resolution, along with their relation to the solar wind parameters, makes the forecasting of the geomagnetic indices a problem that can be addressed through the use of Deep Learning, particularly using Deep Neural Networks (DNNs). In this work, we present two DNNs developed to forecast respectively the SYM-H and ASY-H indices. Both networks have been trained using the Interplanetary Magnetic Field (IMF) and the related index for the solar storms occurred in the last two solar cycles. As a result, the networks are able to accurately forecast the indices 2 h in advance, considering the IMF and indices values for the previous 200 min. The evaluation of both networks reveals a great forecasting precision, including good predictions for large storms that occurred during the solar cycle 23 and comparing with the persistence model for the period 2013–2020.

中文翻译：

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用于 SYM-H 和 ASY-H 预测的具有卷积和 LSTM 层的深度神经网络

地磁指数量化了由行星尺度或地球特定区域的太阳活动引起的扰动。其中，SYM-H和ASY-H指数分别代表中纬度磁场水平分量的（纵向）对称和非对称地磁扰动，分辨率为1分钟。它们的分辨率以及它们与太阳风参数的关系，使得地磁指数的预测成为一个可以通过使用深度学习，特别是使用深度神经网络 (DNN) 来解决的问题。在这项工作中，我们提出了两个 DNN，分别用于预测 SYM-H 和 ASY-H 指数。这两个网络都使用行星际磁场 (IMF) 和最近两个太阳周期中发生的太阳风暴的相关指数进行了训练。因此，考虑到 IMF 和前 200 分钟的指数值，网络能够提前 2 小时准确预测指数。对这两个网络的评估显示出很高的预测精度，包括对太阳活动周期 23 期间发生的大风暴的良好预测，并与 2013-2020 年期间的持续模型进行了比较。