Visualising 2D and 3D Drillhole Assay Data with Geocluster
Drill hole visualization in Geocluster, step by step: join collars to assay data, build a 3D voxel model, slice it, and draw a 2D view with the AI agent.
All
AI 129
Exploration & Target Generation 64
Geospatial Modeling 56
Machine Learning 44
AI Agents 39
Computer Vision 18
Anomaly Detection 17
Process Optimization 15
Hyperspectral Imaging 12
Drilling & Blasting Operations / Blasthole Analysis 11
Geological Modelling & Resource Estimation
AI-Assisted Implicit Geological Modelling (Lithology, Structure, Alteration & Ore Zones)
Implicit geological modelling of lithology, structure and alteration in Leapfrog or Vulcan, with Seequent's Driver adding ML clustering and classification.
Predicting Geotechnical & Recovery Variables in Your Block Model with ML
Block model machine learning: train regressors on lab results (RQD, PLT, BWI/DWI, clays, recovery), then krige predictions into every block model cell.
Predicting Mineralogy and Geometallurgical Domains with ML
Predict geometallurgical domains by training gradient-boosted models on QEMSCAN, MLA or TIMA data, then carrying predictions into a Leapfrog block model.
Statistical Analysis and Domaining
Speed up geological domaining with clustering (K-means or GMM in scikit-learn) on assay and logging data, then validate the new domains in GeostatsPy.