Irida: A machine learning based code for the automated derivation of site-specific rock type logs and their properties using Kimeleon colourlith image logs

Direct mineral content prediction from drill core images via transfer learning

“…Also, spectral data, in terms of multi-sensor spectral imaging, together with SVM, were used to distinguish between six mineralogically meaningful classes, and the corresponding probability estimates of each class were derived in Lorenz et al (2019). In the study discussed in Mishra et al (2022), core plug samples were combined with continuous Kimeleon colorlith logs, which use information from the apparent matrix density, neutron porosity, and gamma-ray logs. K-means clustering was then used to classify the different rock types.…”