Geophysical Data from Norrbotten, Sweden - Evidence for the Presence of a Crustal Scale Fault?

Abstract: The method of combining multiple geophysical, geological, or geochemical datasets can reveal patterns of otherwise hidden features in the Earth’s crust. This may aid in geological mapping, locating economic mineral deposits and for general anomaly/feature detection. In this study a multidimensional geophysical approach implementing five geophysical datasets is applied using Self-Organizing Maps (SOM), where the main objective is to locate and understand a previously unknown hypothesized fault in Norrbotten, Sweden. The fault is estimated to extend from the Finnish border in the north, across northern Sweden in the N-S direction at a hypothesized length of > 250 km. Self-Organizing Maps is an unsupervised neural network - originally developed by Finnish physicist Teuvo Kohonen - capable of combining any number of datasets and thereby visualize them on a simple two-dimensional map. The datasets used in the analysis were three magnetic derivatives for the x, y and z components, as well as gamma-ray intensity measurements of the 238U, 40K and 232Th radioisotopes. All these variables have been shown to be effective tools for bedrock mapping and geological feature detection and were hence chosen based on these properties. The results revealed the efficiency of the SOM analysis to represent multivariate data on a 2D plane and proved to be a generally good visualization tool for multiple geophysical datasets. There seems to be a relatively sharp difference in geophysical properties between the eastern and western blocks divided by the hypothesized fault, which may indicate the presence of this crustal scale structure. Despite the evidence found in this study, more investigations are needed to verify the existence and nature of the fault, and the results shown here may motivate further projects by providing indications and suggestive evidence for its presence.