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地磁场的核心组成部分并对其进行建模，以填补常规卫星磁力飞行任务之间存在的时间覆盖空白，并增进我们对地球磁特性及其起源的了解。卫星磁测数据将提供关于地磁场模型的重要见解，该模型主要基于覆盖面差且有偏差的地面观测数据。GFM还依赖于常规卫星磁测数据，但各任务之间存在重大的时间差距。幸运的是，低地球轨道卫星携带有姿态控制磁强计。最近确定，KOMPSAT-1号卫星仅进行了3天的TAM测量，就提供了关于自1958年以来大约30年内磁场行为的重要知识，而当时没有常规的磁场飞行任务。因此，建议进行一项研究，调查如何使用KOMPSAT-1和-2号卫星全部测量数据中的综合TAM数据和其他磁力测量卫星的常规磁力数据。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，总场图和球谐核场模型可用于全球模型的更新。地下模拟的结果也将提高我们对碳氢化合物和矿产资源以及地质灾害的地壳分布的理解。

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.