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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.