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Predicting Free-Air Gravity Anomaly Using Artificial Neural Network

  • Conference paper
Book cover Vertical Reference Systems

Part of the book series: International Association of Geodesy Symposia ((IAG SYMPOSIA,volume 124))

Abstract

Generally, the values of the gravity are obtained on roads, and near rivers and valleys. This kind of distribution may produce irregular space data and lacking of data in large areas. Geoid heights are usually estimated in regular grids using spectral techniques. Numerous methods have been used for free air gravity anomalies interpolation. This paper reports an Artificial Neural Network (ANN) implementation for predicting free air gravity anomaly. The results were compared with both Kriging and Minimum Curvature. The ANN shows better results in the prediction.

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Author information

Author notes

  1. A. R. Tierra

    Present address: Presently in the Federal University of Paraná, Brazil

Authors and Affiliations

  1. Geodetic Laboratory Department of Geography and Environmental Engineering, Army Polytechnic School, Av. El Progreso s/n, Sangolquí, Equator

    A. R. Tierra

  2. Graduate Program in Geodetic Sciences Earth Sciences, Department of Geomatics, Federal University of Paraná, P.Box 19001, Zip Code 81531-990, Curitiba-Pr, Brazil

    S. R. C. de Freitas

Authors
  1. A. R. Tierra
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  2. S. R. C. de Freitas
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Editor information

Editors and Affiliations

  1. Deutsches Geodätisches Forschungsinstitut, Marstallplatz 8, 80539, München, Germany

    Hermann Drewes

  2. Inst. Eng. Surv. Space Geodesy, University of Nottingham, NG 7 2 RD, University Park, Nottingham, UK

    Alan H. Dodson

  3. Instituto Brasileiro de Geografia e Estatística, Av. Brasil 15671, 21241-051, Rio de Janeiro, RJ, Brazil

    Luiz Paulo Souto Fortes

  4. Instituto Geográfico Agustín Codazzi, Carrera 30 No. 48-51, Bogotá, Colombia

    Laura Sánchez  & Pedro Sandoval  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Tierra, A.R., de Freitas, S.R.C. (2002). Predicting Free-Air Gravity Anomaly Using Artificial Neural Network. In: Drewes, H., Dodson, A.H., Fortes, L.P.S., Sánchez, L., Sandoval, P. (eds) Vertical Reference Systems. International Association of Geodesy Symposia, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04683-8_41

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  • DOI: https://doi.org/10.1007/978-3-662-04683-8_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07701-2

  • Online ISBN: 978-3-662-04683-8

  • eBook Packages: Springer Book Archive

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