Revisiting the Ground Magnetic Field Perturbations Challenge: A Machine Learning Perspective
Revisiting the Ground Magnetic Field Perturbations Challenge: A Machine Learning Perspective
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DOI：
10.3389/fspas.2022.869740
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
V. Pinto;A. Keesee;M. Coughlan;Raman Mukundan;Jeremiah W. Johnson;C. Ngwira;H. Connor
中科院分区：
文献类型：
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作者：
V. Pinto;A. Keesee;M. Coughlan;Raman Mukundan;Jeremiah W. Johnson;C. Ngwira;H. Connor
预测地面磁场扰动一直是空间气象界的一个长期目标。地面磁场数据的可用性及其在地磁诱发电流研究中的潜力，如风险评估，在过去几十年中导致了几项预测工作。一项特别的社区工作是地球空间环境模型对地面磁场扰动的挑战，评估了几个经验模型和第一原理模型在中纬度和高纬度的预测能力，以便选择可操作的模型。在这项工作中，我们使用了三种不同的深度学习模型--前馈神经网络、长短期记忆递归网络和卷积神经网络--预测6个不同地磁仪台站的地磁场变化率(dBH/dt)的水平分量，并尽可能直接地与原始GEM挑战进行比较。我们发现，总的来说，这些模型能够在与原始挑战中获得的水平相似的水平上执行，尽管性能在很大程度上取决于正在评估的特定风暴。然后，我们讨论了这种比较的局限性，因为最初的挑战在设计时没有考虑到机器学习算法。
Forecasting ground magnetic field perturbations has been a long-standing goal of the space weather community. The availability of ground magnetic field data and its potential to be used in geomagnetically induced current studies, such as risk assessment, have resulted in several forecasting efforts over the past few decades. One particular community effort was the Geospace Environment Modeling (GEM) challenge of ground magnetic field perturbations that evaluated the predictive capacity of several empirical and first principles models at both mid- and high-latitudes in order to choose an operative model. In this work, we use three different deep learning models-a feed-forward neural network, a long short-term memory recurrent network and a convolutional neural network-to forecast the horizontal component of the ground magnetic field rate of change (dB H /dt) over 6 different ground magnetometer stations and to compare as directly as possible with the original GEM challenge. We find that, in general, the models are able to perform at similar levels to those obtained in the original challenge, although the performance depends heavily on the particular storm being evaluated. We then discuss the limitations of such a comparison on the basis that the original challenge was not designed with machine learning algorithms in mind.