An efficient workflow for predicting various logging variables using simple machine-learning programs

International audienceThe paper presents simple tools for the prediction of logging variables in uranium exploration using various instrumental data. These tools include i) the prediction of potassic alteration using routine IR-spectroscopy in the 350-2500nm range using Partial Least Squares Regression (PLS-R), ii) the prediction of alteration facies using Visible-NIR (350-1000nm) spectroscopy along with Partial Least Squares – Discriminant Analysis (PLS-DA) and iii) lithostratigraphic units classification using geochemical assays along with a Random Forest Classifier. These tools are associated with an open online repository describing a standard Machine-Learning pipeline for drilling data