In this dissertation an approach is presented for the three-dimensional electrical resistivity tomography (ERT) using unstructured discretizations.
The geoelectrical forward problem is solved by the finite element method using tetrahedral meshes with linear and quadratic shape functions.
Unstructured meshes are suitable for modelling domains of arbitrary geometry (e.g., complicated topography).
Furthermore, the best trade-off between accuracy and numerical effort can be achieved due to the capability of problem-adapted mesh refinement.
Unstructured discretizations also allow the consideration of spatial extended finite electrodes.
Due to a corresponding extension of the forward operator using the complete electrode model, known from medical impedance tomography, a study about the influence of such electrodes to geoelectrical measurements is given.
Based on the forward operator, the so-called triple-grid-technique is developed to solve the geoelectrical inverse problem.
Due to unstructured discretization, the ERT can be applied by using a resolution dependent parametrization on arbitrarily shaped two-dimensional and three-dimensional domains. A~Gauss-Newton method is used with inexact line search to fit the data within error bounds.
A global regularization scheme is applied using special smoothness constraints.
Furthermore, an advanced regularization scheme for the ERT is presented based on unstructured meshes, which is able to include
a-priori information into the inversion and significantly improves the resulting ERT images.
Structural information such as material interfaces known from other geophysical techniques
are incorporated as allowed sharp resistivity contrasts.
Model weighting functions can define individually the allowed deviation of the final resistivity model from given start or reference values.
As a consequent further development the region concept is presented where the parameter domain is subdivided into lithological or geological regions with individual inversion and regularization parameters.
All used techniques and concepts are part of the open source C++ library GIMLi, which has been developed during this thesis as an advanced tool for the method-independent solution of the inverse problem
