Constraining potential field interpretation by geological data: examples from geophysical mapping, inverse and forward modelling

In this thesis three different strategies in potential field data interpretation were implemented and studied. The strategies are related to map transformation, inversion and forward problem. The thesis aims at obtaining geophysical outputs with geological-like features. These kinds of outputs are a significant key to make it easier the geological interpretation of the geophysical data modelling. In particular, the outputs obtained by the different strategies will tend to highlight different units with distinct boundaries and represented by fairly constant field or physical property values. A map transformation technique (terracing) is first proposed. It is based on the use of a cluster analysis technique applied to a gravity or pole-reduced magnetic map. The centre values of the clusters and the cluster number are selected by a statistical analysis of the data map. The use of cluster technique breaks the continuous function (potential field map) onto different areas characterized by piecewise constant values (terraces). The homogeneity within each area is preserved and this kind of feature allow an easy computation of an apparent physical property horizontal distribution map, directly comparable with a geological map. Tests on synthetic and real data are shown. The inversion is treated by applying a strategy made up by three steps. The first and the last steps are inversions with different constraints and associated weights, the second one is conducted by clustering the output of the first smooth inversion. The strategy allows obtaining, in the final step, a volume where the retrieved physical property is classified (by clustering technique) in different volumes of relatively constant values, easily relatable to different geological units. The number of the units, as well as the physical property values associated to each unit, it has to be fixed a priori according to the geological knowledge of the area. Tests on synthetic and real data show that the final obtained models are valid in both geophysical (honoring the data) and geological (understandable relationships among clearly-defined geological units) points of view. A forward problem solver procedure, based on iterative stochastic process is finally proposed. The solution is represented by surfaces that bound different layers having different physical properties. The anomaly field produced by the surfaces is computed by an algorithm working in a Fourier domain. According to the Markov chain simulation, at each iteration several surfaces are created and the best one is selected to be a starting model in the next iteration. The best model selection is performed according to the value of a goodness coefficient. A synthetic case is shown, and the final model obtained shows a possible shape of different bodies, with homogeneous physical property distribution, able to produce a field that adequately match an observed anomaly field.