A data-adaptive network design for the regional gravity field modelling using spherical radial basis functions

A high-precision regional gravity field model is significant in various geodesy applications. In the field of modelling regional gravity fields, the spherical radial basis functions (SRBFs) approach has recently gained widespread attention, while the modelling precision is primarily influenced by th...

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Vydané v:	Geodesy and Geodynamics Ročník 15; číslo 6; s. 627 - 634
Hlavní autori:	Zhang, Fang, Liu, Huanling, Wen, Hanjiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.11.2024 Beijing Fangshan Satellite Laser Ranging National Observation and Research Station,Beijing 102406,China Chinese Academy of Surveying and Mapping,Beijing 100036,China KeAi Communications Co., Ltd
Predmet:	HDBSCAN clustering algorithm Poisson kernel function Regional gravity field modelling Spherical radial basis functions HDBSCAN clustering algorithm Spherical radial basis functions Poisson kernel function Regional gravity field modelling
ISSN:	1674-9847
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Shrnutí:	A high-precision regional gravity field model is significant in various geodesy applications. In the field of modelling regional gravity fields, the spherical radial basis functions (SRBFs) approach has recently gained widespread attention, while the modelling precision is primarily influenced by the base function network. In this study, we propose a method for constructing a data-adaptive network of SRBFs using a modified Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, and the performance of the algorithm is verified by the observed gravity data in the Auvergne area. Furthermore, the turning point method is used to optimize the bandwidth of the basis function spectrum, which satisfies the demand for both high-precision gravity field and quasi-geoid modelling simultaneously. Numerical experimental results indicate that our algorithm has an accuracy of about 1.58 mGal in constructing the gravity field model and about 0.03 m in the regional quasi-geoid model. Compared to the existing methods, the number of SRBFs used for modelling has been reduced by 15.8%, and the time cost to determine the centre positions of SRBFs has been saved by 12.5%. Hence, the modified HDBSCAN algorithm presented here is a suitable design method for constructing the SRBF data adaptive network.
ISSN:	1674-9847
DOI:	10.1016/j.geog.2024.04.001