A software package for computing a regional gravimetric geoid model by the KTH method

Abbak, Ramazan Alpay; Ustun, Aydin

doi:10.1007/s12145-014-0149-3

Cited by 19 publications

(8 citation statements)

References 16 publications

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“…Of all the studies (in TableA.1, with no surface fitting) the smallest standard deviation of 29 mm is obtained using the Radial Basis Function (RBF) method(Lin et al 2019), while the Finite Element Method (FEM) method(Janák et al 2014) provided the largest standard deviation of 97 mm. The KTH method has provided the smaller standard deviations of 24 mm(Yildiz et al 2012), 25 mm(Abbak & Ustun 2015) and 26 mm (this study) after four-parameter, seven-parameter and four-parameter surface fitting, respectively. Utilising the high degree-order EGM2008 (d/o 2190), the CUT method (this study) also provided a standard deviation of 26 mm after four-parameter fitting.…”

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confidence: 73%

Empirical comparison between stochastic and deterministic modifiers over the French Auvergne geoid computation test-bed

et al. 2021

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Since 2006, several different groups have computed geoid and/or quasigeoid (quasi/geoid) models for the Auvergne test area in central France using various approaches. In this contribution, we compute and compare quasigeoid models for the Auvergne test area using Curtin University of Technology's and the Swedish Royal Institute of Technology's approaches. These approaches differ in many ways, such as their treatment of the input data, choice of type of spherical harmonic model (combined or satellite-only), form and sequence of correction terms applied, and different modified Stokes's kernels (deterministic or stochastic). We have also compared our new results with most of the previously reported studies over Auvergne in order to seek any improvements with respect to time [exceptions are when different subsets of data have been used]. All studies considered here have compared the computed quasi/geoid models with the same 75 GPS-levelling heights over Auvergne. The standard deviation for almost all of the computations (without any fitting) is of the order of 30-40 mm, so there is not yet any clear indication whether any approach is necessarily better than any other nor improving over time. We also recommend more universal standardisation on the presentation of quasi/geoid comparisons with GPS-levelling data so that results from different studies over the same areas can be compared more objectively.

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confidence: 73%

Empirical comparison between stochastic and deterministic modifiers over the French Auvergne geoid computation test-bed

et al. 2021

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“…For the detailed explanation of the LSMSA method, interested readers may refer to references specific to the topic, e.g. Sjöberg and Hunegnaw (2000), Ågren (2004), Abbak and Ustun (2014), Ellmann (2004), Ellmann (2005a, Ellmann (2005b) and Sjöberg and Bagherbandi (2017).…”

Section: Annex A: Geoid Modelling Method: Leastsquares Modification Of Stokes' Formula With Additive Correctionsmentioning

confidence: 99%

Contribution of GRAV-D airborne gravity to improvement of regional gravimetric geoid modelling in Colorado, USA

Varga¹,

Pitoňák

Novák

et al. 2021

J Geod

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This paper studies the contribution of airborne gravity data to improvement of gravimetric geoid modelling across the mountainous area in Colorado, USA. First, airborne gravity data was processed, filtered, and downward-continued. Then, three gravity anomaly grids were prepared; the first grid only from the terrestrial gravity data, the second grid only from the downward-continued airborne gravity data, and the third grid from combined downward-continued airborne and terrestrial gravity data. Gravimetric geoid models with the three gravity anomaly grids were determined using the least-squares modification of Stokes’ formula with additive corrections (LSMSA) method. The absolute and relative accuracy of the computed gravimetric geoid models was estimated on GNSS/levelling points. Results exhibit the accuracy improved by 1.1 cm or 20% in terms of standard deviation when airborne and terrestrial gravity data was used for geoid computation, compared to the geoid model computed only from terrestrial gravity data. Finally, the spectral analysis of surface gravity anomaly grids and geoid models was performed, which provided insights into specific wavelength bands in which airborne gravity data contributed and improved the power spectrum.

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“…The need for reductions in gravity anomalies to the geoid stems from the requirement of regular grid usage of geoid computation algorithms such as the LSMSA method. Therefore, pointwise gravity anomalies on the Earth's surface are transformed into simple or complete (refined) Bouguer anomalies that have smooth characteristics for interpolation processes [11,13]. Although the interpolation process directly affects the accuracy of the geoid computation, the choice of the optimum interpolation techniques plays an important role [7,46].…”

Section: Gravity Anomalies and Terrain Correctionmentioning

confidence: 99%

“…In geodesy, Bouguer anomalies are important to use for gridding process and data interpolation purpose [11,13]. These anomalies are also necessary because many geoid modeling algorithms evaluate the gravity data in grid form.…”

Section: Gravity Anomalies and Terrain Correctionmentioning

confidence: 99%

Assessments of Gravity Data Gridding Using Various Interpolation Approaches for High-Resolution Geoid Computations

Karaca,

Erol,

Erol

2024

Geosciences

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This article investigates the role of different approaches and interpolation methods in gridding terrestrial gravity anomalies. In this regard, first of all, simple and complete Bouguer anomalies are considered in gravity data gridding. In the comparison results of gridding these two Bouguer anomaly datasets, the effect of the high-frequency contribution of topographic gravitation (by means of the terrain correction) is clarified. After that, the role of the used interpolation algorithm on the resulting grid of mean gravity anomalies and hence on the geoid modeling accuracy is inspected. For this purpose, four different interpolation methods including geostatistical Kriging, nearest neighbor, inverse distance to a power (IDP), and artificial neural networks (ANNs) are applied. Here, the IDP and nearest neighbor methods represent simple-structured algorithms among the interpolation methods tested in this study. The ANN method, on the other hand, is preferred as a complex, optimization-based soft computing method that has been applied in recent years. In addition, the geostatistical Kriging method is one of the conventional methods that is mostly applied for gridding gravity data in geodesy and geophysics. The calculated gravity anomalies in grids are employed in high-resolution geoid model computations using the least squares modifications of Stokes formula with additive corrections (LSMSA) technique. The investigations are carried out using the test datasets of Auvergne, France that are provided by the International Service for the Geoid for scientific research. It is concluded that the interpolation algorithms affect the gravity gridding results and hence the geoid model determination. The ANN method does not provide superior results compared to the conventional algorithms in gravity gridding. The geoid model with 4.1 cm accuracy is computed in the test area.

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A software package for computing a regional gravimetric geoid model by the KTH method

Cited by 19 publications

References 16 publications

Empirical comparison between stochastic and deterministic modifiers over the French Auvergne geoid computation test-bed

Empirical comparison between stochastic and deterministic modifiers over the French Auvergne geoid computation test-bed

Contribution of GRAV-D airborne gravity to improvement of regional gravimetric geoid modelling in Colorado, USA

Assessments of Gravity Data Gridding Using Various Interpolation Approaches for High-Resolution Geoid Computations

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