Using Machine Learning Explainability Techniques to Examine Drivers of Ground Magnetic Field Localization

Abstract Solar wind particles interact with the Earth's magnetic field and can cause rapid changes in the magnetic field on the ground. This can result in Geomagnetically Induced Currents capable of causing significant damage to infrastructure, making it vital to predict when and where the fluctuations will occur so the impact can be limited. The fluctuations can occur on both a large and highly localized scale, further complicating precise predictions. Machine learning (ML) techniques have emerged as an effective method of predicting space weather phenomena, with their largest complication being their lack of explainability. Here we seek to use such ML methods, combined with a model explainability technique called SHapley Additive exPlanation to both predict and times of extreme localization. Using L1 solar wind data and magnetometer data from SuperMAG, we train two different types of models, one predicting extreme and one predicting large Region‐to‐Specific Difference (RSD). We are seeking to forecast the maximum of RSD and within a rolling 60‐min window, beginning 30 min in the future. The models perform well across a variety of latitudes and Magnetic Local times. While traditional drivers of space weather ( and ) are important drivers of the ML models, other not often examined parameters (particularly ) exhibit non‐uniform spatial and latitudinal dependencies which cannot be attributed to correlation with more influential parameters. Additionally, the inertia of the internal geomagnetic field on a regional scale exhibits a more nuanced behavior compared to previous studies on individual magnetometer stations.