使用XGBoosts来纠正GOES任务磁场测量中的弧射污染
Fadil Inceoglu1,2, Paul T M Loto'aniu1,2
1Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, United States.
Frontiers in artificial intelligence
|October 2, 2025
概括
使用XGBoost的机器学习通过纠正GOES磁力计中的卫星推进器干扰来改善空间天气数据. 这种方法优于特定磁场组件的传统技术,提高了数据的准确性.
科学领域:
- 空间物理 空间物理
- 地质物理学 地质物理学
- 机器学习应用 机器学习应用
背景情况:
- 地静运营环境卫星 (GOES) 上的磁力计对于太空天气监测至关重要.
- 对卫星发射的射箭推进器会污染磁力计数据,影响准确性.
- 当前的校正方法与短暂的变化和残余错误作斗争.
研究的目的:
- 开发和评估一种替代方法来纠正GOES-17磁力计数据中的弧射诱导污染.
- 为了比较机器学习方法 (XGBoost) 与现有的校正矩阵方法的有效性.
主要方法:
- 使用XGBoost,一个机器学习算法,用于数据校正.
- 使用GOES-18作为GOES-17数据校准的基础真相.
- 应用跨卫星比较和监督学习技术.
主要成果:
- XGBoost 方法有效地减少了人工干扰,特别是非线性变化.
- XGBoost的性能优于E和P磁场组件的校正矩阵.
- 传统的校正矩阵对N组件的表现更好.
结论:
- 机器学习,特别是XGBoost,通过纠正卫星驱动的文物来改善磁力计数据质量的显著前景.
- 在GOES-17/GOES-18的拼接提供了一个宝贵的机会,用于跨卫星校准和验证.
- 需要进一步研究更长的调配时间和解决培训数据限制,才能充分实现ML在操作数据纠正方面的潜力.
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