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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 1
Nonlin. Processes Geophys., 17, 65–76, 2010
https://doi.org/10.5194/npg-17-65-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Geocomplexity: novel approaches to understanding geosystems

Nonlin. Processes Geophys., 17, 65–76, 2010
https://doi.org/10.5194/npg-17-65-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  03 Feb 2010

03 Feb 2010

Inversion of 2-D DC resistivity data using rapid optimization and minimal complexity neural network

U. K. Singh1, R. K. Tiwari2, and S. B. Singh2 U. K. Singh et al.
  • 1Department of Applied Geophysics, Indian School of Mines, Dhanbad-826 004, India
  • 2National Geophysical Research Institute, Hyderabad-500 007, India

Abstract. The backpropagation (BP) artificial neural network (ANN) technique of optimization based on steepest descent algorithm is known to be inept for its poor performance and does not ensure global convergence. Nonlinear and complex DC resistivity data require efficient ANN model and more intensive optimization procedures for better results and interpretations. Improvements in the computational ANN modeling process are described with the goals of enhancing the optimization process and reducing ANN model complexity. Well-established optimization methods, such as Radial basis algorithm (RBA) and Levenberg-Marquardt algorithms (LMA) have frequently been used to deal with complexity and nonlinearity in such complex geophysical records. We examined here the efficiency of trained LMA and RB networks by using 2-D synthetic resistivity data and then finally applied to the actual field vertical electrical resistivity sounding (VES) data collected from the Puga Valley, Jammu and Kashmir, India. The resulting ANN reconstruction resistivity results are compared with the result of existing inversion approaches, which are in good agreement. The depths and resistivity structures obtained by the ANN methods also correlate well with the known drilling results and geologic boundaries. The application of the above ANN algorithms proves to be robust and could be used for fast estimation of resistive structures for other complex earth model also.

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