### Application of Artificial Neural Network for the Inversion of Electrical Resistivity Data

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A.P. Aizebeokhai, 2D and 3D geoelectrical resistivity imaging: theory and field design, Scientific Research and Essays 5 (23), 3592 – 2605.

H. Demuth, M. Beale and M. Hagan, Neural Network Toolbox, 1st edition, Natick, Mass, MathWorks 9 (4), 259 – 265.

O. Koefoed, Resistivity sounding on an earth model containing transition layers with linear change of resistivity with depth, Geophysical Prospecting 27 (4), 862 – 868.

L. Lines and S. Treitel, A review of least-squares inversion and its application to geophysical problems, Geophysical Prospecting 32 (2), 159 – 186.

G.F. Luger and W.A. Stubblefield. Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 2nd edition, Benjamin/Cumming Publishing, Redwood City, California, p. 850 (1993).

J.L. McClelland, D.E. Rumelhart and G.E. Hinton, The Appeal of Parallel Distributed Processing, in Parallel Distributed Processing: Explorations in the Microstructure of Cognition Foundations, MIT Press, Cambridge, p. 34 (1986).

J. Stephen, C. Manoj and S. Singh, A direct inversion scheme for deep resistivity sounding data using artificial neural networks, Journal of Earth System Science 113 (1) (2004), 49 – 66.

A. Tarantola, Inverse Problem Theory and Methods for Model Parameter Estimation, 1st edition, Society for Industrial and Applied Mathematics, Philadelphia, PA, p. 358 (2005).

DOI: http://dx.doi.org/10.26713%2Fjims.v9i2.733

eISSN 0975-5748; pISSN 0974-875X