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Potential field data in databases of the Geological Survey of Sweden (SGU) combined with newly acquired broadband magnetotelluric data are used to map and interpret geological units and structures of a 200 km by 250 km area in the Paleoproterozoic Norrbotten ore province (northern Sweden, latitudes 66°–68.5° and longitudes 19°–24°). In order to achieve this, a new approach is proposed with respect to extracting and analysing the possible correlation between modelled physical properties as well as their patterns with respect to depth variation within the crust. In this study, we propose the use of a neural net self-organising map procedure (SOM) for simplification, data reduction, and domain classification of the models derived from independent 3-D geophysical inversion of magnetotelluric, gravity, and magnetic data. The crustal model of the electrical conductivity structure was obtained from previous 3-D inversion of the magnetotelluric data. Processing and 3-D inversion of the regional magnetic and gravity field data were performed using an open-source object-oriented code called SimPEG. The input data to the SOM analysis contain resistivity, magnetic susceptibility, and density model values within the Norrbotten area for some selected depth levels of the entire crust. The domain classification is discussed with respect to the geological boundaries and composition of the crust. Consistency between model domain classification and geological boundaries is observed in general but an apparent discrepancy is noted for some areas. The reason for the apparent discrepancy is likely related to that most geological boundaries represent surface features whereas the geophysical data includes information at depth.

Joint Interpretation of Magnetotelluric and Potential Field Data From North-Eastern Norrbotten, Sweden