Grade control · Mining dilution · Drilling optimization
Drill smarter, not more. Learn to quantify grade uncertainty by drill spacing and design cost-effective drilling meshes that improve resource classification and reduce geological risk.
Make resource classification more quantitative and defensible. Use kriging, simulations and uncertainty measures to apply auditable JORC criteria with greater confidence.
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.
Model complex geological patterns beyond traditional two-point methods. Learn MPS simulation with DeeSse in Isatis.neo, from training images to realistic uncertainty-aware realizations.
Site characterization · Soil remediation · Excavation volumes
Move from samples to confident remediation decisions. Learn to map contamination, estimate impacted volumes and quantify uncertainty using rigorous, practical geostatistical methods.
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.
Map surface contamination on soils and building structures, classify it against your radiological thresholds, and see exactly where your survey is not enough.
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.
Grade-tonnage curves ·Localized Uniform Conditioning (LUC) · Change of support
Turn sparse sampling into reliable grade-tonnage curves. Master Uniform Conditioning to estimate grade, tonnage and metal above cut-off, and strengthen recoverable resource estimates.
Choose the right nonlinear method for your deposit. Build practical command of Multiple Indicator Kriging and Conditional Expectation to estimate recoverable resources with confidence.
Capture the full range of possible grade outcomes. Apply conditional simulation and post-processing to quantify uncertainty and produce realistic recoverable resource estimates.
Decorrelation · Multivariate imputation · Heterotopic data
Keep complex multivariate estimates coherent. Use PCA, MAF and PPMT to decorrelate variables, impute missing assays and preserve sum or ratio constraints in estimation and simulation.
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