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  Physics-informed neural networks for the improvement of platform magnetometer measurements

Styp-Rekowski, K., Michaelis, I., Korte, M., Stolle, C. (2025): Physics-informed neural networks for the improvement of platform magnetometer measurements. - Physics of the Earth and Planetary Interiors, 358, 107283.
https://doi.org/10.1016/j.pepi.2024.107283

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 ???ViewItemFull_lblCreators???:
Styp-Rekowski, Kevin1, ???ENUM_CREATORROLE_AUTHOR???           
Michaelis, Ingo1, ???ENUM_CREATORROLE_AUTHOR???           
Korte, M.1, ???ENUM_CREATORROLE_AUTHOR???           
Stolle, Claudia1, ???ENUM_CREATORROLE_AUTHOR???           
???ViewItemFull_lblAffiliations???:
12.3 Geomagnetism, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146030              

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 ???ViewItemFull_lblAbstract???: Space-based measurements of the Earth's magnetic field with a good spatiotemporal coverage are needed to understand the complex system of our surrounding geomagnetic field. High-precision magnetic field satellite missions form the backbone for sophisticated research, but they are limited in their 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 non-dedicated satellite missions complement the high-precision data by additional coverage in space, time, and magnetic local times. In this work, we present an extension to our previous Machine Learning approach for the 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. We achieved a mean residual of about 7 nT and 4 nT for low- and mid-latitudes, respectively.

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 ???ViewItemFull_lblDates???: 20242025
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1016/j.pepi.2024.107283
???ENUM_IDENTIFIERTYPE_GFZPOF???: p4 T1 Atmosphere
???ENUM_IDENTIFIERTYPE_GFZPOFWEITERE???: p4 T3 Restless Earth
???ENUM_IDENTIFIERTYPE_GFZPOFWEITERE???: p4 MESI
???ENUM_IDENTIFIERTYPE_OATYPE???: Hybrid - DEAL Elsevier
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???ViewItemFull_lblSourceTitle???: Physics of the Earth and Planetary Interiors
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus
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???ViewItemFull_lblPages???: ???lbl_noEntry??? ???ViewItemFull_lblSourceVolumeIssue???: 358 ???ViewItemFull_lblSourceSequenceNo???: 107283 ???ViewItemFull_lblSourceStartEndPage???: ???lbl_noEntry??? ???ViewItemFull_lblSourceIdentifier???: ???ENUM_IDENTIFIERTYPE_CONE???: https://gfzpublic.gfz.de/cone/journals/resource/journals400
???ENUM_IDENTIFIERTYPE_PUBLISHER???: Elsevier