Can machine learning reveal precursors of reversals of the geomagnetic axial dipole field?
Can machine learning reveal precursors of reversals of the geomagnetic axial dipole field?
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DOI：
10.1093/gji/ggac195
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发表时间：
2022-05
影响因子：
2.8
通讯作者：
K. Gwirtz;T. Davis;M. Morzfeld;C. Constable;A. Fournier;G. Hulot
中科院分区：
文献类型：
--
作者：
K. Gwirtz;T. Davis;M. Morzfeld;C. Constable;A. Fournier;G. Hulot
众所周知，地球磁场的轴向偶极部分颠倒了极性，因此磁北极变成了南极，反之亦然。在过去的160年里，逆转的时机已经有了很好的记录，但导致逆转的条件仍然没有很好地理解。目前尚不清楚是否有可靠的逆转“前兆”(表明逆转即将到来的事件)或它们可能是什么。我们研究了机器学习(ML)技术是否能够可靠地识别基于轴向磁偶极子磁场时间序列的反转前兆。其基本思想是利用轴向磁偶极子的时间序列段来训练分类器。这一训练步骤需要修改标准的ML技术，以考虑到我们对罕见事件感兴趣的事实--反转是不常见的，而非反转的场是常态。如果没有我们的调整，ML分类器将导致无用的预测。也许更重要的是，可用的观测记录被限制在0-2 Ma，并且只包含五个反转，这就需要我们确定数据是否足以可靠地训练和验证ML算法。为了回答这些问题，我们使用了几个最大似然分类器(线性/非线性支持向量机和长期短期记忆网络)，调用了一系列数值模型(从简化模型到三维地球发电机模拟)，以及两个古地磁重建(PADM2M和SINT-2000)。ML分类器的性能在不同的模型和观测记录中有所不同，我们提供的证据表明，这不是数值上的人工产物，而是反映了模型或观测记录的“可预测性”。因此，通过ML分类器研究地球磁场模型可以帮助识别各种模型的缺点或优点。对于地球磁场，我们得出结论，ML识别倒转前兆的能力是有限的，这主要是因为数据量小，频率分辨率低，这使得训练和后续验证几乎是不可能的。简单地说：我们尝试的ML技术目前不能可靠地识别地磁逆转的轴向偶极矩(ADM)前兆。这并不一定意味着这样的前兆不存在，在时间分辨率和ADM记录长度方面的改进很可能在未来提供更好的前景。
It is well known that the axial dipole part of Earth’s magnetic field reverses polarity, so that the magnetic north pole becomes the south pole and vice versa. The timing of reversals is well documented for the past 160 Myr, but the conditions that lead to a reversal are still not well understood. It is not known if there are reliable “precursors” of reversals (events that indicate that a reversal is upcoming) or what they might be. We investigate if machine learning (ML) techniques can reliably identify precursors of reversals based on time series of the axial magnetic dipole field. The basic idea is to train a classifier using segments of time series of the axial magnetic dipole. This training step requires modification of standard ML techniques to account for the fact that we are interested in rare events – a reversal is unusual, while a non-reversing field is the norm. Without our tweak, the ML classifiers lead to useless predictions. Perhaps even more importantly, the usable observational record is limited to 0-2 Ma and contains only five reversals, necessitating that we determine if the data are even sufficient to reliably train and validate an ML algorithm. To answer these questions we use several ML classifiers (linear/nonlinear support vector machines and long short-term memory networks), invoke a hierarchy of numerical models (from simplified models to 3-D geodynamo simulations), and two paleomagnetic reconstructions (PADM2M and Sint-2000). The performance of the ML classifiers varies across the models and the observational record and we provide evidence that this is not an artifact of the numerics, but rather reflects how “predictable” a model or observational record is. Studying models of Earth’s magnetic field via ML classifiers thus can help with identifying shortcomings or advantages of the various models. For Earth’s magnetic field, we conclude that the ability of ML to identify precursors of reversals is limited, largely due to the small amount and low frequency resolution of data, which makes training and subsequent validation nearly impossible. Put simply: the ML techniques we tried are not currently capable of reliably identifying an axial dipole moment (ADM) precursor for geomagnetic reversals. This does not necessarily imply that such a precursor does not exist, and improvements in temporal resolution and length of ADM records may well offer better prospects in the future.