Build a strong foundation in mineral resource estimation. Follow the univariate workflow, from data analysis and variography to block modeling, kriging, validation and grade-tonnage curves.
Build realistic geological models that reflect complexity and uncertainty. Apply indicator kriging, SIS, TGS, PGS, and implicit modeling through hands-on workflows in Isatis.neo.
Deep Kriging · Geometallurgical domains · Lithology classification
Put machine learning to work on real geoscience and mining challenges. Build, evaluate and apply classification and regression models by connecting scikit-learn with Isatis.neo.
Turn spatial data into sound decisions. Learn to analyze datasets, map surfaces, model subsurface properties and quantify uncertainty with practical geostatistical workflows.
Site characterization · Soil remediation · Excavation volumes
Radiological contamination is spatially heterogeneous. See how geostatistics helps assess sampling, map uncertainty and evaluate affected volumes for decommissioning projects.
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