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| Format: | Artículo científico |
| Language: | en |
| Published: |
Universitat de Barcelona
2010
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| Online Access: | https://www.redalyc.org/articulo.oa?id=50513113003 |
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Table of Contents:
- 2-D Niblett-Bostick magnetotelluric inversion J. Rodríguez F.J. Esparza E. Gómez-Treviño Ciencias de la Tierra Niblett magnetotelluric Bostick inversion Hopfield Neural Network A simple and robust imaging technique for two-dimensional magnetotelluric interpretations has been developed following the well known Niblett-Bostick transformation for one-dimensional profiles. The algorithm processes series and parallel magnetotelluric impedances and their analyticalinfluence functions using a regularized Hopfield artificial neural network. The adaptive, weighted average approximation preserves part of the nonlinearity of the original problem, yet no initial model in the usual sense is required for the recovery of the model; rather, the built-in relationship between model and data automatically and concurrently considers many half spaces whose electrical conductivities vary according to the data. The use of series and parallel impedances, a self-contained pair of invariants of the impedance tensor, avoids the need to decide on best angles of rotation for identifying TE and TM modes. Field data from a given profile can thus be fed directly into the algorithm without much processing. The solutions offered by the regularized Hopfield neural network correspond to spatial averages computed through rectangular windows that can be chosen at will. Applications ofthe algorithm to simple synthetic models and to the standard COPROD2 data set illustrate the performance of the approximation. 2010 artículo científico 1695-6133 https://www.redalyc.org/articulo.oa?id=50513113003 en http://www.redalyc.org/revista.oa?id=505 Geologica Acta: an international earth science journal application/pdf Universitat de Barcelona Geologica Acta: an international earth science journal (España) Num.1 Vol.8