Modelling geoid undulations with an artificial neural network

Author

Seager, James ; Collier, Philip ; Kirby, Jonathon

Author_Institution

Dept. of Geomatics, Melbourne Univ., Parkville, Vic., Australia

Volume

5

fYear

1999

fDate

1999

Firstpage

3332

Abstract

Examines the use of a backpropagation neural network to model geoid undulations. Modelling of the Earth´s gravity field, and in particular the separation between ellipsoid and geoid surface, is one of the fundamental problems in the field of geodesy. Geoid undulations are important for relating heights derived from the satellite based Global Positioning System to orthometric heights, which determine the flow of water. Modelling of geoid undulations has been traditionally done using Stokes integral, least squares collocation, or by fast Fourier transforms. The paper presents the results of preliminary investigations which suggest the backpropagation neural network provides a useful tool for geoid undulation modelling

Keywords

Global Positioning System; backpropagation; geodesy; geophysical techniques; gravity; neural nets; Earth´s gravity field; backpropagation neural network; geodesy; geoid surface; geoid undulations; orthometric heights; Artificial neural networks; Backpropagation; Earth; Ellipsoids; Fast Fourier transforms; Geodesy; Global Positioning System; Gravity; Least squares methods; Satellites;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.836195

Filename

836195