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Conference Paper

Automated outleir detection with machine learning in GRACE-FO postfit residuals

Authors

Zbinden,  Jonas
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Lasser,  Martin
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Meyer,  Ulrich
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Panos,  Brandon
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Arnold,  Daniel
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Jäggi,  Adrian
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Zbinden, J., Lasser, M., Meyer, U., Panos, B., Arnold, D., Jäggi, A. (2023): Automated outleir detection with machine learning in GRACE-FO postfit residuals, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-4955


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021354
Abstract
GRACE/GRACE-FO inter-satellite K-band range-rates are the main observable for the determination of the monthly solutions of the Earth’s gravity field. The range-rates are sensitive not only to the mass distribution on the Earth, and as a consequence, the relative motion of both GRACE and GRACE-FO satellites respectively, but also to the relative orientation of the satellites and consequently on the attitude handling, which relies on the star camera observations, subject to blinding by the sun and moon. In consequence the range-rates observations exhibit a number of difficult to identify error sources and efficient screening is not trivial. In this contribution, we apply novel outlier detection methods such as Isolation Forest and Local Outlier Factor (collectively known under the term machine learning) to flag outliers in an unsupervised fully automated way. We apply both techniques to the post-fit residuals of monthly, common orbit and gravity field determination processings, combined with the geographical position of each observation. The flagged outliers are investigated for local geographical correlations to distinguish between unfitted signal from gravitational sources and artefacts caused by the satellites’ instrumentation. For that we train a Mutual Information Neural Network, learning the mutual information between the post-fit residuals and the geographical location. The outliers flagged as artefacts are removed from the original inter-satellite range-rate data and orbit and gravity field determination processing is repeated to investigate the benefit of the proposed screening procedures.