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

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

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Item Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021354 Version Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021354_1
Genre: Conference Paper

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 Creators:
Zbinden, Jonas1, Author
Lasser, Martin1, Author
Meyer, Ulrich1, Author
Panos, Brandon1, Author
Arnold, Daniel1, Author
Jäggi, Adrian1, Author
Affiliations:
1IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations, ou_5011304              

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 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.

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Language(s): eng - English
 Dates: 2023-07-112023-07-11
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.57757/IUGG23-4955
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Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Place of Event: Berlin
Start-/End Date: 2023-07-11 - 2023-07-20

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Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Source Genre: Proceedings
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Publ. Info: Potsdam : GFZ German Research Centre for Geosciences
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