Abstract This paper presents an application of the global-local orthogonal mapping (GLO-MAP) algorithm to derive data-driven models for magnetic field variations of the Earth. The GLO-MAP algorithm rigorously merges different independent local approximations that are based upon measured data to obtain a desired order, globally continuous approximation. We show the local magnetic field data acquired by ground-based survey and discuss details of the survey process. A potassium vapor magnetometer is used in the field experiments to obtain accurate observations that form the basis of the local modeling. Numerical results based on the experimental data show that the GLO-MAP algorithm can accurately and efficiently map the magnetic field variations, while a single global polynomial based modeling approach produces over 30% of the approximation error in the worst case.

中文翻译：

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使用 GLO-MAP 算法对磁场变化进行数据驱动建模

摘要 本文介绍了应用全局-局部正交映射 (GLO-MAP) 算法来推导地球磁场变化的数据驱动模型。GLO-MAP 算法严格合并基于测量数据的不同独立局部近似，以获得所需的阶数、全局连续近似。我们展示了通过地面勘测获得的局部磁场数据，并讨论了勘测过程的细节。在现场实验中使用钾蒸气磁力计来获得准确的观测结果，这些观测结果构成了局部建模的基础。基于实验数据的数值结果表明，GLO-MAP算法能够准确有效地绘制磁场变化，