Combination of GOCE Gravity Gradients in Regional Gravity Field Modelling Using Radial Basis Functions

2015 ◽  
pp. 101-108 ◽  
Author(s):  
Verena Lieb ◽  
Johannes Bouman ◽  
Denise Dettmering ◽  
Martin Fuchs ◽  
Michael Schmidt
Keyword(s):  
Gravity Field ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Gravity Gradients ◽  
Gravity Field Modelling ◽  
Field Modelling ◽  
Radial Basis ◽  
Regional Gravity

scholarly journals A numerically efficient technique of regional gravity field modeling using Radial Basis Functions

2016 ◽  
Vol 348 (2) ◽  
pp. 99-105 ◽  
Author(s):  
Anahita Shahbazi ◽  
Abdolreza Safari ◽  
Ismael Foroughi ◽  
Robert Tenzer
Keyword(s):  
Gravity Field ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Efficient Technique ◽  
Field Modeling ◽  
Radial Basis ◽  
Gravity Field Modeling ◽  
Regional Gravity ◽  
Regional Gravity Field Modeling

scholarly journals On the regional gravity field modeling via Radial Basis Functions and modefied Levenberg-Marcoardet Algorithm

2016 ◽  
Vol 4 (3) ◽  
pp. 107-119
Author(s):  
Mahboobeh Mohammad Yusefi Bahlouli Ahmadi ◽  
Abdolreza Safari ◽  
Anahita Shahbazi ◽  
◽  
◽  
...  
Keyword(s):  
Gravity Field ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Field Modeling ◽  
Radial Basis ◽  
Gravity Field Modeling ◽  
Regional Gravity ◽  
Regional Gravity Field Modeling

Different radial basis functions and their applicability for regional gravity field representation on the sphere

2013 ◽  
Vol 4 (1) ◽  
pp. 67-96 ◽  
Author(s):  
Katrin Bentel ◽  
Michael Schmidt ◽  
Christian Gerlach
Keyword(s):  
Gravity Field ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Field Representation ◽  
Radial Basis ◽  
Regional Gravity

Global and Regional Gravity Field Determination from GOCE Kinematic Orbit by Means of Spherical Radial Basis Functions

2015 ◽  
Vol 36 (6) ◽  
pp. 773-801 ◽  
Author(s):  
Blažej Bucha ◽  
Aleš Bezděk ◽  
Josef Sebera ◽  
Juraj Janák
Keyword(s):  
Gravity Field ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Kinematic Orbit ◽  
Radial Basis ◽  
Gravity Field Determination ◽  
Spherical Radial Basis Functions ◽  
Regional Gravity

scholarly journals Artifacts in regional gravity representations with spherical radial basis functions

2013 ◽  
Vol 3 (3) ◽  
Author(s):  
K. Bentel ◽  
M. Schmidt ◽  
C. Rolstad Denby
Keyword(s):  
Radial Basis Functions ◽  
Basis Functions ◽  
Radial Basis ◽  
Spherical Radial Basis Functions ◽  
Regional Gravity

Sparsifying spherical radial basis functions based regional gravity models

2020 ◽  
pp. 1-16
Author(s):  
Haipeng Yu ◽  
Guobin Chang ◽  
Shubi Zhang ◽  
Nijia Qian
Keyword(s):  
Radial Basis Functions ◽  
Basis Functions ◽  
Gravity Models ◽  
Radial Basis ◽  
Spherical Radial Basis Functions ◽  
Regional Gravity

Regional gravity field modelling from GOCE observables

2017 ◽  
Vol 59 (1) ◽  
pp. 114-127 ◽  
Author(s):  
Martin Pitoňák ◽  
Michal Šprlák ◽  
Pavel Novák ◽  
Robert Tenzer
Keyword(s):  
Gravity Field ◽  
Gravity Field Modelling ◽  
Field Modelling ◽  
Regional Gravity

scholarly journals Regularization methods for the combination of heterogeneous observations using spherical radial basis functions

2019 ◽  
Author(s):  
Qing Liu ◽  
Michael Schmidt ◽  
Roland Pail ◽  
Martin Willberg
Keyword(s):  
Radial Basis Functions ◽  
Regularization Parameter ◽  
Basis Functions ◽  
Parameter Determination ◽  
Data Sets ◽  
Radial Basis ◽  
Spherical Radial Basis Functions ◽  
Ill Posed ◽  
Curve Method ◽  
Regional Gravity

Abstract. Various types of heterogeneous observations can be combined within a parameter estimation process using spherical radial basis functions (SRBF) for regional gravity field refinement. However, this process is in most cases ill-posed, and thus, regularization is indispensable. We discuss two frequently used methods for choosing the regularization parameter which are the L-curve method and variance component estimation (VCE). Based on these two methods, we propose two new approaches for the regularization parameter determination, which combine the L-curve method and VCE. The first approach, denoted as ‘VCE + L-curve method’, starts with the calculation of the relative weights between the observation techniques by means of VCE. Based on these weights the L-curve method is applied to determine the regularization parameter. In the second approach, called ‘L-curve method + VCE’, the L-curve method determines first the regularization parameter and it is set to be fixed during the calculation of the relative weights between the observation techniques from VCE. These methods are investigated based on two different estimation concepts for combining various observation techniques. All the methods are applied and compared in six study cases using four types of observations in Europe. The results show that the ‘VCE + L-curve method’ delivers the best results in all the six cases, no matter using SRBFs with smoothing or non-smoothing features. The ‘L-curve method + VCE’ also gives rather good results, generally outperforming the cases just using the L-curve method or VCE. Therefore, we conclude that the newly proposed methods are decent and stable for regularization parameter determination when different data sets are combined and can be recommended regardless of the type of SRBFs used.

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