Fuzzy clustering for the compilation of pseudo-lithology maps and the exploration of zones of mineralization in Karasjok, northern Norway

• Main Author: Omar, Mamdouh (author)
• Other Authors: van der Wal, W. (mentor), Gradmann, Sofie (mentor), Brönner, Marco (mentor), Delft University of Technology (degree granting institution)
• Format: Master Thesis
• Language: English
• Published: 2019
• Subjects: Clustering; Geophysics; pseudo-lithology; mineralization; Karasjok; Norway; Fennoscandia; Northern Finland; Northern Norway
• Online Access: http://resolver.tudelft.nl/uuid:f5d0f191-ff10-449b-87c2-98ff210d60f0

Northern Norway has an extensive geological variation compared to other parts of Norway. It is home to major ore deposits containing base and precious metals. It is located within Fennoscandia, which has been identified as a significant metal-producing region in Europe. Recently, Northern Norway has extensively been surveyed using several geophysical techniques, which led to coverage improvement of basic geological information relevant to the assessment of its mineral potential. The study area, in the vicinity of Karasjok, is located on the Karasjok Greenstone Belt and comprises mainly Palaeoproterozoic rocks. This terrane is located on the same Palaeoproterozoic greenstone terrane of northern Finland, in which the detected gold deposits showed, with a few exceptions, similar characteristics to gold occurrences in Palaeoproterozoic greenstone belts in other parts of the world. Also, economic mineral deposits, e.g. Ni-Cu-PGE-Au mineralization, are largely restricted to events that occurred during the Palaeoproterozoic era. However, the study area for this study still remains largely under-explored and previous research and field work of the Karasjok Greenstone Belt have proven unsuccessful to locate significant mineral deposits. This area contains very few outcrops and traditional methods to map such areas are challenging. A large number of different geophysical data sets exist for Norway. While individual data sets cannot provide direct information about the lithology of the mapped area, a combination of multiple data sets makes it possible to delineate and interpret characteristic regions of potentially similar lithology. This is of particular interest where the geological map may be unreliable or that geological mapping itself is difficult, for example, due to difficult accessibility or large vegetation coverage. In this study, three fuzzy clustering algorithms, i.e. the fuzzy c-means algorithm, the Gustafson-Kessel algorithm, and the possibilistic fuzzy c-means algorithm, have been applied to compile ...