Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 5
Articles | Volume 17, issue 5
https://doi.org/10.5194/gmd-17-2187-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2187-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 5
Model description paper
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15 Mar 2024
Model description paper | Highlight paper |  | 15 Mar 2024

Minimum-variance-based outlier detection method using forward-search model error in geodetic networks

Utkan M. Durdağ

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Executive editor
Robust outlier detection is a challenge for all areas of science that deal with real data. Here, the author describes a new approach to this in the field of geodesy, but does so in a readable and accessible way. It will therefore be valuable reading for those beyond that field.
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Short summary
This study introduces a novel approach to outlier detection in geodetic networks, challenging conventional and robust methods. By treating outliers as unknown parameters within the Gauss–Markov model and exploring numerous outlier combinations, this approach prioritizes minimal variance and eliminates iteration dependencies. The mean success rate (MSR) comparisons highlight its effectiveness, improving the MSR by 40–45 % for multiple outliers.
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