The Future Logging Assistant
Quantitative and qualitative geological models of mineral deposits form the basis of the mining value chain, including all subsequent decisions on valuation, mining method, processing method and measures to alleviate environmental impact of mining. The main input data in such models are derived from analysis of drill core. Owing to the complexity of geological materials coupled with time-constraints, only a fraction of the information stored in the rocks is collected. Subjectivity in logging leads to that results may differ between observers, inducing additional complexity. These uncertainities impact negatively on the models, which may in turn have adverse effects further down the value chain. The Future Logging Assistant (FLA) is a recently started collaborative research project between LTU and Boliden Mineral, aiming at augmenting the analysis and evaluation of drill core with the combined use of machine learning methods on various types of sensor data.
Luleå University of Technology
Nils Jansson is an associate professor in Ore Geology at Luleå University of Technology, who also has a background in exploration for volcanogenic massive sulphide deposits in Sweden. His main research interest lies in integrating structural, stratigraphic, geochemical, and mineralogical data for unraveling the genesis of complex, metamorphosed iron oxide and sulfide deposits. He received the Geological Society of Sweden’s Young Scientist Award in 2017 and the Bergforsk award for best PhD thesis in 2011. He is examiner for courses in Mining Geology and Mineralogy at LTU, and Project leader for the EXplORE international MSc exchange program in exploration.
Presentation Nils Jansson