Fereshteh Khammar, Doctoral Researcher

Meritförteckning

A key geological and mining exploration challenge is the responsible and sustainable exploration of deposits with little or no outcrops. This is particularly the case in Finland with a vast and thick vegetation and/or snow cover limiting traditional exploration techniques to expensive drilling campaigns. Mineral prospectivity mapping (MPM) from regional to local scales integrates various geochemical and structural geoscience data to identify exploration target areas. I will develop a model which will help users to identify favorable areas for mineral exploration from a large number of data layers, i.e. the model obtains patterns from multidimensional data. In my work, I will apply machine learning algorithms in MPM to find new favorable regions for IOCG-style Fe-Cu-Au deposits in Northern Finland. My work centres around a geological and a mathematical framework. The geological framework focusses on the critical geological processes that control the formation and preservation of mineral deposits based on mineral system analysis. The mathematical framework will apply ML techniques to MPM using indicator maps showing mappable proxies including lithology, tectonic structures, geophysical and geochemical anomalies.