Signal and Noise Separation From Satellite Magnetic Field Data Through Independent Component Analysis: Prospect of Magnetic Measurements Without Boom and Noise Source Information
Signal and Noise Separation From Satellite Magnetic Field Data Through Independent Component Analysis: Prospect of Magnetic Measurements Without Boom and Noise Source Information
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DOI：
10.1029/2020ja028790
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发表时间：
2021-05-01
影响因子：
2.8
通讯作者：
Matsuoka, A.
中科院分区：
文献类型：
--
作者：
Imajo, S.;Nose, M.;Matsuoka, A.
我们提出了一个应用程序的独立分量分析（伊卡）分离卫星引起的随时间变化的杂散磁场从使用机载多个磁强计获得的磁场数据。伊卡是一种用于估计多个站点处的源信号的方法，使得所估计的源信号可以变得在统计上彼此独立。由于杂散场变化在统计上独立于外部自然场变化，因此伊卡方法有望将自然场变化与杂散场分离。因此，我们将伊卡应用于第一颗准天顶卫星的磁场数据，该卫星具有两个三轴磁通门磁力计，而不使用可扩展的吊杆。首先，我们从原始数据中删除长期趋势，以创建去趋势数据。然后，我们将FastICA算法应用于去趋势数据，并获得六个独立分量（IC）。杂散场被成功地分离到三个IC（噪声IC）中，而自然信号由另外三个IC（信号IC）表示。最后，我们从信号IC恢复了观察到的信号，并确认自然现象的变化没有被处理步骤改变。提出了一种利用混合向量方差系数C来选择噪声IC的方法。C系数最大的第三和第四类IC之间的C差异较大。总体而言，这些结果表明，伊卡方法可以支持在未来的卫星任务的繁荣磁观测的可能性。
We propose an application of the independent component analysis (ICA) to separate satellite-induced time-varying stray fields from magnetic field data obtained using onboard multiple magnetometers. The ICA is a method for estimating source signals at multiple sites so that the estimated source signals can become statistically independent of each other. Since stray field variations are statistically independent of external natural field variations, the ICA method is expected to separate the natural variations from stray fields. Thus, we applied the ICA to magnetic field data from the first Quasi-Zenith Satellite, which has two triaxial fluxgate magnetometers, without using an extendable boom. First, we removed the long-period trend from the original data to create detrended data. Then, we applied the FastICA algorithm to the detrended data and obtained six independent components (ICs). The stray fields were successfully separated into three ICs (noise ICs), and the natural signals were represented by the other three ICs (signal ICs). Finally, we restored the observed signals from the signal ICs, and confirmed that the natural phenomena variations were not altered by the processing step. We also proposed a selection method of the noise ICs using the C coefficient, which is the coefficient of the variance of the mixing vectors. There was a large difference in C between the ICs whose C coefficients are the largest third and fourth ones. Overall, these results demonstrate the possibility that the ICA method can support for boom-less magnetic observations in future satellite missions.