Abstract

Local gravity field modelling demands high-quality gravity data as well as an appropriate mathematical model. Particularly in coastal areas, there may be different types of gravity observations available, for instance, terrestrial, aerial, marine gravity, and satellite altimetry data. Thus, it is important to develop a proper tool to merge the different data types for local gravity field modelling and determination of the geoid. In this study, radial basis functions, as a commonly useful tool for gravity data integration, are employed to model the gravity potential field of the southern part of Iran using terrestrial gravity anomalies, gravity anomalies derived from re-tracked satellite altimetry, marine gravity anomalies, and gravity anomalies synthesized from an Earth gravity model. Reference GNSS/levelling (geometric) geoidal heights are used to evaluate the accuracy of the estimated local gravity field model. The gravimetric geoidal heights are in acceptable agreement with the geometric ones in terms of the standard deviation and the mean value which are 4.1 and 12 cm, respectively. Besides, the reference benchmark of the national first-order levelling network of Iran is located in the study area. The derived gravity model was used to compute the gravity potential difference at this point and then transformed into a height difference which results in the value of the shift of this benchmark with respect to the geoid. The estimated shift shows a good agreement with previously published studies.

Highlights

  • The availability of multi-resolution gravity data corroborates the idea of gravity data fusion to improve their spectral and spatial resolutions for gravity field modelling

  • Basis functions with compact or quasi-compact supports are more suitable choices for local gravity field modelling considering various types of gravity data, their spatial distributions, spatially-limited areas of interest and required accuracy. Among available methods, such as the least-squares collocation (LSC) [3], least-squares modified Stokes (LSMS) method [4] and the fast Fourier transform approach [5], the radial basis functions (RBF) have been proven to be more suitable for the combined processing of different types of gravity data, e.g., [6,7,8,9,10,11,12,13,14,15,16]. In all these studies the RBF technique is used for gravity field modelling where in this study we investigate the application of this method for height datum unification

  • Beside using the same terrestrial gravity data as in [27], gravity anomalies derived from re-tracked satellite altimetry are used instead of only satellite altimetry derived anomalies, a new set of terrestrial gravity data is added, and the RBF processing scheme is advanced by using scattered gravity observations

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Summary

Introduction

The availability of multi-resolution gravity data corroborates the idea of gravity data fusion to improve their spectral and spatial resolutions for gravity field modelling. Basis functions with compact or quasi-compact supports are more suitable choices for local gravity field modelling considering various types of gravity data, their spatial distributions, spatially-limited areas of interest and required accuracy Among available methods, such as the least-squares collocation (LSC) [3], least-squares modified Stokes (LSMS) method [4] and the fast Fourier transform approach [5], the radial basis functions (RBF) have been proven to be more suitable for the combined processing of different types of gravity data, e.g., [6,7,8,9,10,11,12,13,14,15,16].

Theory
The gravity anomalies computedby by EGM2008
Findings
Summary and Conclusions
Full Text
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