Geomagnetic field modeling from on-board LEO attitude-control magnetometer

拟议的研究旨在从用于卫星姿态控制的磁力计测量中分离和模拟地磁场的核心成分，以填补传统卫星磁任务之间存在的时间覆盖差距，并提高我们对地球磁性及其起源的理解。卫星磁数据将为地磁场模型（GFM）提供重要见解，该模型主要基于覆盖范围较差且有偏差的地面观测数据。 GFM 也依赖于传统的卫星磁数据，但任务之间存在显着的时间间隔。幸运的是，近地轨道卫星携带有姿态控制磁力计（TAM）。最近确定，仅 KOMPSAT-1 卫星进行的 3 天 TAM 测量就可以提供有关 1958 年以来约 30 年来磁场行为的重要知识，当时传统的磁任务无法实现。因此，建议开展一项研究，调查利用来自 KOMPSAT-1 和 -2 卫星全部测量数据的综合 TAM 数据以及来自其他磁测卫星的常规磁数据。程序和最先进的数据处理技术将包括坐标变换、光谱相关滤波、象限交换、球谐系数计算和地下建模。作为研究成果，总场图和球谐核心场模型可用于更新全局模型。地下建模的结果还将提高我们对地壳碳氢化合物和矿产资源分布以及地质灾害的了解。

The proposed research aims to separate and model the core components in the geomagnetic field from the magnetometer measurements that have been used in attitude control of satellites, in order to fill temporal coverage gaps that exist between conventional satellite magnetic missions and to improve our understanding of the Earth's magnetic properties and its origin. The satellite magnetic data will provide key insights on the geomagnetic field model (GFM), which is based largely on ground observatory data that has poor and biased coverage. The GFM also relies on the conventional satellite magnetic data, but significant temporal gaps exist between missions. Fortunately, low-Earth orbit satellites carry on-board attitude control magnetometers (TAMs). It was recently determined that only 3 days of TAM measurements from the KOMPSAT-1 satellite were found to provide important knowledge on the behaviour of the magnetic field for the roughly 30 years since 1958, when conventional magnetic missions were unavailable. Accordingly, a study is proposed to investigate the use of comprehensive TAM data from the entire measurement data of KOMPSAT-1 and -2 satellites and conventional magnetic data from other magnetic survey satellites. Procedures and state-of-the-art data processing techniques will include coordinate transformation, spectral correlation filtering, quadrant-swapping, spherical harmonic coefficient calculation, and subsurface modeling. As research outcomes, the total field map and the spherical harmonic core field model can be used to update global models. The results of the subsurface modeling will also improve our understanding of crustal distribution of hydrocarbons and mineral resources, as well as geological hazards.