Sector: <span>Mining</span>
Probabilistic modeling of lateritic nickel mineral resources
Mining
This study, co-authored by Geovariances, presents a unified probabilistic workflow for lateritic nickel resource modeling that combines unfolding, plurigaussian simulation, multivariate imputation, and PPMT within Isatis.neo. Applied to...
Webinar replay: DHSA: How to drill smarter for better resource classification
Mining
Geovariances is to talk for the first time at DIGMIN 2025
Mining
Discover how advanced multivariate geostatistics techniques combined with machine learning can unlock smarter mineral resource modeling. Sept. 12-13, 2025.
Coherent modeling of mineral grades and zones by coupling cokriging and Support Vector Machine
Mining
This technical paper explores the complementary use of geostatistics and machine learning for mineral resource modeling, particularly when complex categorical variables make traditional geostatistical workflows difficult to manage....
Geovariances gives a technical presentation at MMME’2025 Paris
Mining
Discover how advanced multivariate geostatistics techniques combined with machine learning can unlock smarter mineral resource modeling. August 19, 2025
Using multiple-point geostatistics for geomodeling of a vein-type gold deposit
Mining
Vein-type gold deposits are notoriously hard to model: narrow, long-range structures with sparse drill-hole data defeat conventional two-point geostatistics. This study from Nazarbayev University and the University of...