ML4DrillCore

ML4DrillCore: Fusing Machine Learning and Computer Vision Techniques for Automatic Drill Core Analysis on Visual and Compositional Data

ML4DrillCore: (Feb. 2021 - Nov. 2021)

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 the environmental impact of mining. The main input data in such models are derived from the analysis of drill cores. Owing to the complexity of geological materials coupled with time constraints, only a fraction of the information stored in the rocks is collected. Subjectiveness in logging of textural features furthermore leads to that results may differ between observers, inducing additional complexity. These uncertainties and confusion impact negatively on the models’ reliability, which may, in turn, have adverse effects further down the value chain. ML4DrillCore aims at improving the analysis and evaluation of drill core data with the combined use of deep learning techniques, likeML4DrillCore - Seed Money Application 2020-2021 Page 2 for example computer vision techniques on visual data and standard machine learning methods on compositional data.

Funding Partner: SUN seed projects