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Abstract:
Monitoring the Earth’s magnetic field is important to deepen our knowledge of Earth’s interior processes.
The Gravity Recovery and Climate Experiment-Follow-On
(GRACE-FO) is a dual-satellite mission. Each satellite car-
ries platform magnetometers which are used for navigation
and attitude control, they are subject to noise and artificial
disturbance signals. Calibrating these magnetometers, thus
removing artificial magnetic disturbances, will yield datasets
that have a valuable impact for modeling the variability of the
Earth’s magnetic field with higher spatiotemporal coverage.
In this work we propose a new method, modeling the cali-
bration of the magnetic data with machine learning methods.
Therefore, neural networks are employed and adjusted to
specific challenges of satellite missions. One of the challenges
includes an intrinsic time shift in the data of this mission.
We propose an interpolation neuron that generates data while
finding an arbitrary time shift in the data, thus co-estimating
the model and the time shift. Additionally, sample weights
have been added to counteract the partial lack of ground
truth, recovering the extrapolation possibilities of the neural
network at high latitudes. Evaluation experiments have shown
promising results, achieving a meaningful calibration that still
maintains external natural magnetic phenomena signals while
lowering the overall residual by 23.8% on average compared
to current state-of-the-art methods. For the two satellites the
mean absolute error is 8.43nT, respectively 8.62nT on average
over the mission duration. The resulting calibrated dataset will
be published and made available together with this publication.
