Regional Gravity Field Modeling Using Band-Limited SRBFs: A Case Study in Colorado

The use of spherical radial basis functions (SRBFs) in regional gravity field modeling has become popular in recent years. However, to our knowledge, their potential for combining gravity data from multiple sources, particularly for data with different spectrum information in the frequency domain, h...

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Veröffentlicht in:Remote sensing (Basel, Switzerland) Switzerland), 2023-09, Vol.15 (18), p.4515
Hauptverfasser: Ma, Zhiwei, Yang, Meng, Liu, Jie
Format: Artikel
Sprache:eng
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Zusammenfassung:The use of spherical radial basis functions (SRBFs) in regional gravity field modeling has become popular in recent years. However, to our knowledge, their potential for combining gravity data from multiple sources, particularly for data with different spectrum information in the frequency domain, has not been extensively explored. Therefore, band-limited SRBFs, which have good localization characteristics in the frequency domain, were taken as the main tool in this study. To determine the optimal expansion degree of SRBFs for gravity data, a residual and a priori accuracy comparative analysis method was proposed. Using this methodology, the expansion degrees of terrestrial and airborne data were determined to be 5200 and 1840, respectively, and then a high-resolution geoid model called ColSRBF2023 was constructed for use in Colorado. The results indicated that ColSRBF2023 had a standard deviation (STD) of 2.3 cm with respect to the GSVS17 validation data. This value was 2–6 mm lower than models obtained using different expansion degrees for gravity data and models from other institutions considered in this study. Furthermore, when comparing it with the validation geoid model on a 1′ × 1′ grid, ColSRBF2023 exhibited an STD value of 2.4 cm, which was also the best among the examined models. These findings highlight the importance of determining the optimal expansion degree of gravity data, particularly for constructing high-resolution gravity field models in rugged mountainous areas.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15184515