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Journal of Applied Geodesy

Editor-in-Chief: Kahmen, Heribert / Rizos, Chris

CiteScore 2018: 1.61

SCImago Journal Rank (SJR) 2018: 0.532
Source Normalized Impact per Paper (SNIP) 2018: 1.064

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Volume 5, Issue 3-4


Monte Carlo-based data snooping with application to a geodetic network

Rdiger Lehmann
  • Faculty of Spatial Information, Dresden University of Applied Sciences, Friedrich-List-Platz 1, D-01069 Dresden, Germany
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/ Tobias Scheffler


Data snooping is one of the best established methods of gross error detection in geodetic data analysis. Since it is based on hypothesis testing, it requires the choice of levels of error probability. This choice is often, to some degree, arbitrary. If the levels chosen are too high, we run the risk of losing many good measurements that are not actually contaminated by gross errors. If the levels chosen are too low, we run the risk of leaving gross errors undetected. We propose to choose levels of error probability such that the desired parameters are best estimated in some sense. This can be done using the Monte Carlo method. We applied this procedure to a geodetic precision network from construction of a diversion tunnel. Depending on the stochastic model of the measurement process, we observed a gain of such an optimal choice of a few percent of the mean point standard deviation. This comes at a price of considerable computer time consumption. Even on a fast computer, a typical computation of a medium-sized geodetic network may take several minutes.

Keywords.: Data snooping; geodetic adjustment; geodetic network; Monte Carlo method

About the article

Received: 2011-03-21

Accepted: 2011-09-14

Published in Print: 2011-12-01

Citation Information: Journal of Applied Geodesy, Volume 5, Issue 3-4, Pages 123–134, ISSN (Online) 1862-9024, ISSN (Print) 1862-9016, DOI: https://doi.org/10.1515/JAG.2011.014.

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