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  GOCE ML-calibrated magnetic field data

Styp-Rekowski, K., Michaelis, I., Stolle, C., Baerenzung, J., Korte, M., Kao, O. (2022): GOCE ML-calibrated magnetic field data.
https://doi.org/10.5880/GFZ.2.3.2022.002

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 Creators:
Styp-Rekowski, Kevin1, Author
Michaelis, Ingo2, Author              
Stolle, Claudia1, Author
Baerenzung, Julien3, Author              
Korte, M.2, Author              
Kao, Odej1, Author
Affiliations:
1External Organizations, ou_persistent22              
22.3 Geomagnetism, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146030              
31.3 Earth System Modelling, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146027              

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Free keywords: GOCE satellite, machine learning, platform magnetometers, calibration
 Abstract: The Gravity field and steady-state ocean circulation explorer (GOCE) satellite mission carries three platform magnetometers. After careful calibration, the data acquired through these can be used for scientific purposes by removing artificial disturbances from other satellite payload systems. This dataset is based on the dataset provided by Michaelis and Korte (2022) and uses a similar format. The platform magnetometer data has been calibrated against CHAOS7 magnetic field model predic-tions for core, crustal and large-scale magnetospheric field (Finlay et al., 2020) and is provided in the ‘chaos’ folder. The calibration results using a Machine Learning approach are provided in the ‘calcorr’ folder. Michaelis’ dataset can be used as an extension to this dataset for additional infor-mation, as they are connected using the same timestamps to match and relate the same data points. The exact approach based on Machine Learning is described in the referenced publication. The data is provided in NASA CDF format (https://cdf.gsfc.nasa.gov/) and accessible at: ftp://isdcftp.gfz-potsdam.de/platmag/MAGNETIC_FIELD/GOCE/ML/v0204/ and further de-scribed in a README.

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Language(s): eng - English
 Dates: 20222022
 Publication Status: Finally published
 Pages: -
 Publishing info: Potsdam : GFZ Data Services
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.5880/GFZ.2.3.2022.002
 Degree: -

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