论文标题
实时机器学习启用了低成本磁力计系统
Real-Time Machine Learning Enabled Low-Cost Magnetometer System
论文作者
论文摘要
地磁诱导的电流（GIC）是太空天气中最危险的影响之一。地面水平磁成分DBH/DT的变化速率被用作GIC的代理度量。为了监视和预测DBH/DT，使用地面的磁力仪。但是，在使用此类磁力计数据之前，基线校正至关重要。在本文中，已经实施了启用的低成本机器学习（ML）启用的磁力计系统，以执行对磁力计数据的实时基线校正。然后，预测的地磁组件用于得出DBH/DT的预测。部署了两个不同的ML模型，并检查了它们的实时和离线预测精度。使用二进制事件分析进一步验证了预测的DBH/DT的局部峰。
Geomagnetically Induced Currents (GICs) are one of the most hazardous effects of space weather. The rate of change in ground horizontal magnetic component dBH/dt is used as a proxy measure for GIC. In order to monitor and predict dBH/dt, ground-based fluxgate magnetometers are used. However, baseline correction is crucial before such magnetometer data can be utilized. In this paper, a low-cost Machine Learning (ML) enabled magnetometer system has been implemented to perform realtime baseline correction of magnetometer data. The predicted geomagnetic components are then used to derive a forecast for dBH/dt. Two different ML models were deployed, and their real-time and offline prediction accuracy were examined. The localized peaks of the predicted dBH/dt are further validated using binary event analysis.
