Machine learning-based calibration of the GOCE satellite platform magnetometers
Abstract Additional datasets from space-based observations of the Earth’s magnetic field are of high value to space physics and geomagnetism. The use of platform magnetometers from non-dedicated satellites has recently successfully provided additional spatial and temporal coverage of the magnetic fi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-09-01
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| Series: | Earth, Planets and Space |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40623-022-01695-2 |
| _version_ | 1798036282242236416 |
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| author | Kevin Styp-Rekowski Ingo Michaelis Claudia Stolle Julien Baerenzung Monika Korte Odej Kao |
| author_facet | Kevin Styp-Rekowski Ingo Michaelis Claudia Stolle Julien Baerenzung Monika Korte Odej Kao |
| author_sort | Kevin Styp-Rekowski |
| collection | DOAJ |
| description | Abstract Additional datasets from space-based observations of the Earth’s magnetic field are of high value to space physics and geomagnetism. The use of platform magnetometers from non-dedicated satellites has recently successfully provided additional spatial and temporal coverage of the magnetic field. The Gravity and steady-state Ocean Circulation Explorer (GOCE) mission was launched in March 2009 and ended in November 2013 with the purpose of measuring the Earth’s gravity field. It also carried three platform magnetometers onboard. Careful calibration of the platform magnetometers can remove artificial disturbances caused by other satellite payload systems, improving the quality of the measurements. In this work, a machine learning-based approach is presented that uses neural networks to achieve a calibration that can incorporate a variety of collected information about the satellite system. The evaluation has shown that the approach is able to significantly reduce the calibration residual with a mean absolute residual of about 6.47nT for low- and mid-latitudes. In addition, the calibrated platform magnetometer data can be used for reconstructing the lithospheric field, due to the low altitude of the mission, and also observing other magnetic phenomena such as geomagnetic storms. Furthermore, the inclusion of the calibrated platform magnetometer data also allows improvement of geomagnetic field models. The calibrated dataset is published alongside this work. Graphical Abstract |
| first_indexed | 2024-04-11T21:10:35Z |
| format | Article |
| id | doaj.art-132532042efe43448cbc796f4070324e |
| institution | Directory Open Access Journal |
| issn | 1880-5981 |
| language | English |
| last_indexed | 2024-04-11T21:10:35Z |
| publishDate | 2022-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Earth, Planets and Space |
| spelling | doaj.art-132532042efe43448cbc796f4070324e2022-12-22T04:03:01ZengSpringerOpenEarth, Planets and Space1880-59812022-09-0174112310.1186/s40623-022-01695-2Machine learning-based calibration of the GOCE satellite platform magnetometersKevin Styp-Rekowski0Ingo Michaelis1Claudia Stolle2Julien Baerenzung3Monika Korte4Odej Kao5Distributed and Operating Systems, Technical University of BerlinGFZ German Research Centre for Geosciences, Helmholtz Centre PotsdamLeibniz Institute of Atmospheric Physics at the University of RostockGFZ German Research Centre for Geosciences, Helmholtz Centre PotsdamGFZ German Research Centre for Geosciences, Helmholtz Centre PotsdamDistributed and Operating Systems, Technical University of BerlinAbstract Additional datasets from space-based observations of the Earth’s magnetic field are of high value to space physics and geomagnetism. The use of platform magnetometers from non-dedicated satellites has recently successfully provided additional spatial and temporal coverage of the magnetic field. The Gravity and steady-state Ocean Circulation Explorer (GOCE) mission was launched in March 2009 and ended in November 2013 with the purpose of measuring the Earth’s gravity field. It also carried three platform magnetometers onboard. Careful calibration of the platform magnetometers can remove artificial disturbances caused by other satellite payload systems, improving the quality of the measurements. In this work, a machine learning-based approach is presented that uses neural networks to achieve a calibration that can incorporate a variety of collected information about the satellite system. The evaluation has shown that the approach is able to significantly reduce the calibration residual with a mean absolute residual of about 6.47nT for low- and mid-latitudes. In addition, the calibrated platform magnetometer data can be used for reconstructing the lithospheric field, due to the low altitude of the mission, and also observing other magnetic phenomena such as geomagnetic storms. Furthermore, the inclusion of the calibrated platform magnetometer data also allows improvement of geomagnetic field models. The calibrated dataset is published alongside this work. Graphical Abstracthttps://doi.org/10.1186/s40623-022-01695-2Machine learningCalibrationPlatform magnetometerGOCE satelliteMagnetic field model |
| spellingShingle | Kevin Styp-Rekowski Ingo Michaelis Claudia Stolle Julien Baerenzung Monika Korte Odej Kao Machine learning-based calibration of the GOCE satellite platform magnetometers Earth, Planets and Space Machine learning Calibration Platform magnetometer GOCE satellite Magnetic field model |
| title | Machine learning-based calibration of the GOCE satellite platform magnetometers |
| title_full | Machine learning-based calibration of the GOCE satellite platform magnetometers |
| title_fullStr | Machine learning-based calibration of the GOCE satellite platform magnetometers |
| title_full_unstemmed | Machine learning-based calibration of the GOCE satellite platform magnetometers |
| title_short | Machine learning-based calibration of the GOCE satellite platform magnetometers |
| title_sort | machine learning based calibration of the goce satellite platform magnetometers |
| topic | Machine learning Calibration Platform magnetometer GOCE satellite Magnetic field model |
| url | https://doi.org/10.1186/s40623-022-01695-2 |
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