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HOW DOES THE DATUM POINT LOCATION AFFECT THE ACCURACY OF THE ADJUSTED BENCHMARK HEIGHTS IN A PRECISE LEVELING NETWORK?

Vasil Cvetkov, Christina Mickrenska

First published: 2025-08-15https://doi.org/10.5593/sgem2025/2.1/s08.09View metrics

Abstract

It is well established that the farther a benchmark is from the datum point in a leveling network, the greater its standard error of adjusted height tends to be. To explore the optimal datum point location for minimizing height uncertainty, we performed 11 separate adjustments of the Second Bulgarian Leveling Network. Each adjustment used a different nodal benchmark as the datum point. While all adjustments yielded identical benchmark elevations and the same mean adjustment error of 2.14 mm/km^0.5, the sum and average of the standard errors of the adjusted heights varied. The lowest total standard error was obtained when the datum was set at Kazanlak, located near the network-s mass center. This adjustment produced the lowest average standard error: 21.83 mm. In contrast, placing the datum at peripheral points significantly increased uncertainty. For example, the average standard errors were: - Sofia: 26.2 mm - Tutrakan: 26.4 mm - Dospat: 26.7 mm A Kruskal-Wallis H test comparing the medians of the standard error samples across adjustments rejected the null hypothesis of equal medians at the 90% confidence level. This confirms that the datum location has a meaningful effect on the distribution of benchmark standard errors. Therefore, datum point placement must be considered when evaluating the accuracy of height adjustments, especially when estimating vertical crustal movements in geodynamically stable regions such as Bulgaria.

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Publication details

Title
HOW DOES THE DATUM POINT LOCATION AFFECT THE ACCURACY OF THE ADJUSTED BENCHMARK HEIGHTS IN A PRECISE LEVELING NETWORK?
Authors
Vasil Cvetkov, Christina Mickrenska
Proceedings
25th International Multidisciplinary Scientific GeoConference Proceedings SGEM 2025, Geoinformatics, Remote Sensing, and Artificial Intelligence (AI), Vol 25, Issue 2.1
Publisher
STEF92 Technology
Year
2025
Pages
65-72
SWS Citekey
Cvetkov202586572
ISSN
1314-2704; 13142704
ISBN
9786197603897
Language
en
Publication type
Conference Paper
Proceedings contents
Open official contents
Keywords
References6
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