2-D Niblett-Bostick magnetotelluric inversion
Rodríguez, J. (CiCese/Ciencias de la Tierra (Ensenada, Mèxic))
Esparza, F. J. (CiCese/Ciencias de la Tierra (Ensenada, Mèxic))
Gómez-Treviño, E. (CiCese/Ciencias de la Tierra (Ensenada, Mèxic))

Data:	2010
Resum:	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 analytical influence 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 of the algorithm to simple synthetic models and to the standard COPROD2 data set illustrate the performance of the approximation.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan aquestes es distribueixin sota la mateixa llicència que regula l'obra original i es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Niblett-Bostick inversion ; Hopfield Neural Network ; Magnetotelluric
Publicat a:	Geologica acta, Vol. 8, Núm. 1 (Març 2010) , p. 15-31, ISSN 1696-5728

Adreça alternativa: https://raco.cat/index.php/GeologicaActa/article/view/188002
Adreça alternativa: https://revistes.ub.edu/index.php/GEOACTA/article/view/1939
DOI: 10.1344/105.000001513

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