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? AI-Powered Orebody Delineation & Grade Control - Complete Guide

Application of machine learning (ML), geostatistics, and sensor fusion to improve orebody modeling fidelity, reduce dilution, and enable real-time grade control decisions.

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Case Studies
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AI-Powered Orebody Delineation & Grade Control - Complete Guide

Using AI and sensors to draw precise 3D maps of where valuable ore is located undergroundβ€”and adjust mining decisions on...

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Quick Start

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Knowledge Base

15 pages
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Key Concepts

AI-Powered Orebody
Delineation & Grade ControlGeostatistical ML
for Orebody Modeling
Sensor Fusion:
LiDAR + Hyperspectral
Neural Kriging vs
Variogram Modeling
Real-Time Grade Control
with Edge-AI Drilling
Dilution Prediction
with ConvLSTM
Uncertainty Quantification
in ML Block Models
Bias MitigationRegulatory Compliance

Visual overview of key concepts and their relationships

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Real Projects

4 cases
Copper Mine Block Model Refinement Using Neural Kriging Traditional kriging over-smoothed high-grade chalcocite zones βˆ’8.2% reserve Neural Kriging Engine 3D variogram features + geochemical pathfinder ratios Surpac Integration Python API β€’ Real-time update RMSE Reduction 1.7 β†’ 0.9 g/t Reserve Upside +12.4 Mt @ +0.18% Cu

Copper Mine Block Model Refinement Using Neural Kriging

Escondida-style porphyry copper deposit, Chile

Challenge: Traditional kriging over-smoothed high-grade chalcocite zones, causing 8.2% rese...
Edge-AI Rig-MountedHyperspectral + XRFFusionLSTM GradeRegressionMineSuiteWorkflowDecision latency: 92 secChallengeβˆ’14% dilutionDilution avoidance: βˆ’3.6 kt/stopeGold Mine Real-Time Grade Controlat Development Drift Face

Gold Mine Real-Time Grade Control at Development Drift Face

Underground high-grade gold vein system, Western Australia

Challenge: Manual chip sampling caused 24–48 hr delay in stope boundary decisions, leading...
Iron Ore Mine Sensor Fusion for BIF Delineation Conventional geophysics failed β†’ 22% grade variance in ROM feed AM 200m res EM 10m res HS 0.5m res Multi-Scale Fusion Engine U-Net Ensemble (12k images) Band Detection Precision: 94.1% TP/(TP+FP) Feed Grade Std Dev Reduction 1.42% β†’ 0.67% Fe

Iron Ore Mine Sensor Fusion for Banded Iron Formation (BIF) Delineation

Pilbara open pit, Australia

Challenge: Conventional geophysics failed to resolve thin hematite bands (<2m) within jaspi...
GPRMicro-GravityPhysics-Informed NN(Hourly Digital Twin Update)GPR data streamMicro-gravity time-seriesGrade DropCaCO₃ <85%Alert outputβ€’ Min void: 0.8 mΒ³ @ 12m depthβ€’ Forecast horizon: 4.2 daysβ€’ Rejection rate: 11%

Limestone Mine Digital Twin for Karst-Related Grade Uncertainty

Karst-hosted limestone quarry, USA

Challenge: Solution cavities caused unpredictable grade drops (CaCO₃ purity <85%) in otherw...
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Downloads

6 resources
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Learning Path

22 lessons

Master AI-Powered Orebody Delineation & Grade Control through a structured learning path β€” from fundamentals to advanced applications.

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