Dissertação

Modelagem de dados MT 2D em multicamadas com anisotropia arbitrária

We present in this work the bidimensional Magnetotelluric method modelling with arbitrary anisotropy in the electrical conductivity for horizontally stratified multilayers models, separating the total field into primary and secondary fields. The primary field is the 1D MT field in the absence of h...

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Autor principal: COSTA, Marcus Danilo Ferreira Borges da
Grau: Dissertação
Idioma: por
Publicado em: Universidade Federal do Pará 2014
Assuntos:
Acesso em linha: http://repositorio.ufpa.br/jspui/handle/2011/5921
Resumo:
We present in this work the bidimensional Magnetotelluric method modelling with arbitrary anisotropy in the electrical conductivity for horizontally stratified multilayers models, separating the total field into primary and secondary fields. The primary field is the 1D MT field in the absence of heterogeneity. The secondary field is the field generated by the heterogeneity, in other words, it is the difference between the total field and the primary field. The numerical technique of Finite Elements (FE) is used for this 2D Modelling. In addition we include the MT 1D modelling case with arbitrary 3D conductivity anistropy using the propagation matrix method. This result is used as the source in the bidimensional formulation. When we studied the horizontal fields in layered media; there seems to exist sources in each anisotropic layer, because there is an increase of amplitude in these layers, also we verify the appearence of the normal component Ez of the field, without a current density in this direction. The arbitrary anisotropy allows the principals anisotropic axes to have any orientation in relation to cartesian referencial system choosen for the problem. This orientation is defined by three consecutive rotations, the first being strike, the second dip and the third slant. These rotations simulate common geological environments in the earth. This type of anisotropy changes the pseudo-sections of apparent resistivity, making data interpretation more challenging.