@article{Kosarev2026, 
author = {Nikolay Kosarev and Oleg Odalovic and Roman Sermiagin and Denis Goldobin and Vadim Kanushin and Nurgan Kemerbayev and Andrei Sholomitskii},
title = {Modeling of the vertical gravity gradient in the territory of the Republic of Kazakhstan: Methods, Comparison and Evaluation of Accuracy},
year = {2026},
journal = {Geodesy and Geodynamics},
volume = {17},
number = {4},
pages = {480-492},
keywords = {Vertical gravity gradient, Relative gravity, Global geopotential model, Gravity gradient modelling},
url = {https://www.sciopen.com/article/10.1016/j.geog.2025.10.001},
doi = {10.1016/j.geog.2025.10.001},
abstract = {This study investigates the modeling of the vertical gravity gradient (VGG) in the Republic of Kazakhstan using three non-instrumental methods: global geopotential models (GGMs), a precomputed VGG grid from the Institute of Geophysics and Planetary Physics (IGPP), and a method combining the normal gravity field with digital elevation models (DEMs). The research compares these modeled VGG values with high-precision measurements from 48 outdoor gravity stations, employing Scintrex CG-6 gravimeters for indirect VGG determination. Key findings reveal that topographic modeling is unreliable for stations located in depressions due to unaccounted mass above benchmarks. At the same time, GGMs provide results comparable to the normal gradient but lack resolution for stations on elevated terrain. After excluding problematic stations, modeling accuracy improved to approximately 10 μGal/m. The study concludes that while direct VGG measurements remain essential near the Earth's surface, modeled values can offer better accuracy than theoretical corrections in specific scenarios, depending on the detail of input data. The research highlights the importance of VGG in geodesy and gravimetry, particularly for high-precision applications. It underscores the limitations of current modeling techniques in accounting for local topographic and subsurface density variations. The work contributes to refining methods of gravity data reduction and supports the development of Kazakhstan's geodetic infrastructure.}
}