Mon Oct 13, F. Flechtner
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Styp-Rekowski, Kevin, Michaelis, Ingo, Korte, Monika, and Stolle, Claudia, 2025. Physics-informed neural networks for the improvement of platform magnetometer measurements. Physics of the Earth and Planetary Interiors, 358:107283, doi:10.1016/j.pepi.2024.10728310.22541/au.170602061.18680921/v2.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025PEPI..35807283S,
author = {{Styp-Rekowski}, Kevin and {Michaelis}, Ingo and {Korte}, Monika and {Stolle}, Claudia},
title = "{Physics-informed neural networks for the improvement of platform magnetometer measurements}",
journal = {Physics of the Earth and Planetary Interiors},
keywords = {Space-based magnetic field measurements, Platform magnetometers, Magnetometer calibration, Physics-informed neural networks, AMPS, Average Magnetic field and Polar current System, CHAMP, CHAllenging Minisatellite Payload, ETL, Extract, transform, and load process, FAC, Field-aligned currents, FFNN, Feed-forward neural network, GOCE, Gravity and steady-state Ocean Circulation Explorer, GRACE, Gravity Recovery And Climate Experiment, GRACE-FO, Gravity Recovery And Climate Experiment Follow-On, IMF, Interplanetary Magnetic Field, MAE, Mean absolute error, MSE, Mean squared error, ML, Machine learning, MLT, Magnetic local time, MTQ, Magnetorquer, NEC, North-East-Center frame, NN, Neural network, PIC, Physics-informed component, PINN, Physics-informed neural network, QDLat, Quasi-dipole latitude, SD, Standard deviation.},
year = 2025,
month = jan,
volume = {358},
eid = {107283},
pages = {107283},
abstract = "{High-precision space-based measurements of the Earth's magnetic field
with a good spatiotemporal coverage are needed to analyze the
complex system of our surrounding geomagnetic field. Dedicated
magnetic field satellite missions like the Swarm mission form
the backbone of research, providing high-precision data with
limited coverage. Many satellites carry so-called platform
magnetometers that are part of their attitude and orbit control
systems. These can be re-calibrated by considering different
behaviors of the satellite system, hence reducing their
relatively high initial noise originating from their rough
calibration. These platform magnetometer data obtained from
satellite missions not dedicated to geomagnetic fields
complement high-precision data from the Swarm mission by
additional coverage in space, time, and magnetic local times. In
this work, we present an extension to a previous machine
learning approach for automatic in-situ calibration of platform
magnetometers. We introduce a new physics-informed layer
incorporating the Biot-Savart formula for dipoles that can
efficiently correct artificial disturbances due to electric
current-induced magnetic fields evoked by the satellite itself.
We demonstrate how magnetic dipoles can be co-estimated in a
neural network for the calibration of platform magnetometers and
thus enhance the machine learning-based approach to follow known
physical principles. Here, we describe the derivation and
assessment of re-calibrated datasets for two satellite missions,
GOCE and GRACE-FO, which are made publicly available. Compared
to the reference model, we achieved an average residual of about
7 nT for the GOCE mission and 4 nT for the GRACE-FO mission
across all three components combined in the low- and mid-
latitudes.}",
doi = {10.1016/j.pepi.2024.10728310.22541/au.170602061.18680921/v2},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025PEPI..35807283S},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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