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| Titel |
Inversion of 2-D DC resistivity data using rapid optimization and minimal complexity neural network |
| VerfasserIn |
U. K. Singh, R. K. Tiwari, S. B. Singh |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 17, no. 1 ; Nr. 17, no. 1 (2010-02-03), S.65-76 |
| Datensatznummer |
250013637
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| Publikation (Nr.) |
 copernicus.org/npg-17-65-2010.pdf |
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| Zusammenfassung |
| 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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