P. Schmoldt, PhD - MTNet - DIAS

10. Data inversion
WinGLink software [WinGLink, 2005], also based on the algorithm by Rodi and Mackie
[2001]. The ai1D code by Pek and Santos [2006] is used for anisotropic 1D inversion,
and isotropic 3D inversion is carried out using the program WSINV3DMT [Siripunvaraporn
et al., 2005a].
First, optimal smoothing parameters and enhanced a priori models are determined for
inversion of crust and mantle ranges. Inversion models for the two modes of MT in
2D (TE and TM) are derived separately to gain better insight about the nature of the
subsurface; particularly, in order to infer the extent of 3D effects in each of the modes.
In addition, hypotheses from previous studies regarding the Tajo Basin subsurface (see
Sec. 7) are tested and an enhanced subsurface model is presented. Furthermore, results
of anisotropic 1D, isotropic 2D, anisotropic 2D, and isotropic 3D inversion are contrasted
and their applicability for the Tajo Basin subsurface case is evaluated.
10.1. Inversion for crustal structures
10.1.1. Determining smoothing parameters for inversion
As illustrated in Section 6.3, inversion of MT data is non-unique and additional constraints
have to be applied to limit the range of acceptable models. The choice of smoothing
parameters, reducing spatial variation of electric resistivity in the inversion model, can
have significant impacts on the derived inversion model. Therefore, a deliberate parameter
study is carried out prior to the final inversion for crustal structures. The 2D inversion
program implemented in WinGLink [WinGLink, 2005], based on the algorithm by Rodi
and Mackie [2001], includes three smoothing parameters, controlling different aspects of
model constraints (cf. Sec. 6.3), namely
• α: multiplication factor of the horizontal derivatives in the objective function; higher
values of alpha drive horizontal smoothness of the inversion model;
• β: exponential factor in the weighting function for uniform grid Laplacian regularisation;
higher values increase the penalty on horizontal roughness;
• τ: “global” weighting function, determines the trade-off between data-misfit and
model-roughness (cf. Eq. 6.31); higher values are in favour of smoother models.
An increased global model smoothing (i.e. an increased τ) is usually preferable, as it
reduces the number of features within a model for most cases; thus, yielding a minimum
structure model (cf. Sec. 6.3.3). An increased horizontal smoothing (i.e. an increased α)
is reasoned for the Tajo Basin subsurface model by results of crustal seismic studies in
the region (Sec. 7.3.2), indicating relatively homogeneous intermediate and lower crustal
layers with small variations in depth. β is kept at a value of 1, following recommendations
by Mackie [2002] for the case of isotropic 2D inversion using uniform grids with
Laplacian regularisation (∆(m) 2 ).
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