Introduction

Resource Modelling & Estimation Analytics Training Course integrates block modelling, spatial analytics, machine learning, grade estimation, uncertainty quantification, and digital mining transformation to deliver accurate, defensible, and globally compliant resource estimates. Participants will gain hands-on exposure to advanced kriging techniques, variography, simulation modelling, and resource classification frameworks aligned with international reporting standards such as JORC, NI 43-101, and SAMREC.

With increasing demand for sustainable mining, ESG compliance, real-time analytics, and predictive resource intelligence, this training equips professionals with cutting-edge tools, automation strategies, and data integration workflows. The course emphasizes practical applications, case-based learning, and industry software tools, enabling participants to enhance resource confidence, reduce geological risk, and optimize mine planning decisions in a rapidly evolving digital mining ecosystem

Programme Curriculum

Resource Modelling & Estimation Analytics Training Course

Introduction

Course Duration

5 days

Course Objectives

Apply advanced geostatistical modelling techniques for resource estimation
Develop 3D block models using industry-standard software
Integrate machine learning algorithms in resource estimation workflows
Perform variography and spatial continuity analysis
Conduct kriging and simulation-based estimation
Implement uncertainty quantification and risk analysis frameworks
Optimize grade control and short-term resource reconciliation
Apply big data analytics and data integration techniques
Interpret and validate resource classification
Enhance resource modelling automation using scripting tools (Python/R)
Evaluate economic impacts of resource estimation decisions
Align modelling practices with JORC, SAMREC, NI 43-101 compliance standards
Leverage AI-driven predictive modelling for resource optimization

Target Audience

Mining Engineers
Exploration Geologists
Resource Geologists
Geostatisticians
Mine Planning Engineers
Data Scientists in Mining
Mineral Economists
GIS & Spatial Data Analysts

Course Modules

Module 1: Fundamentals of Resource Modelling

Geological data types and structures
Sampling methods and QA/QC protocols
Data validation and preprocessing
Introduction to block modelling
Spatial data visualization
Case Study: Gold deposit data validation in West Africa

Module 2: Geostatistics & Variography

Spatial continuity concepts
Variogram modelling
Anisotropy analysis
Model fitting techniques
Variogram validation
Case Study: Iron ore variography in Australia

Module 3: Estimation Techniques

Inverse Distance Weighting (IDW)
Ordinary Kriging (OK)
Universal Kriging (UK)
Indicator Kriging
Nearest Neighbor methods
Case Study: Copper grade estimation in Chile

Module 4: Advanced Simulation Methods

Sequential Gaussian Simulation (SGS)
Conditional simulation techniques
Multiple realizations analysis
Risk modelling and uncertainty
Comparison of deterministic vs stochastic models
Case Study: Diamond deposit uncertainty modelling in Botswana

Module 5: Block Modelling & Software Applications

Block model creation and optimization
Sub-blocking techniques
Model validation and reconciliation
Software tools
Visualization techniques
Case Study: Platinum reef modelling in South Africa

Module 6: Resource Classification & Reporting

Classification criteria
Confidence levels and uncertainty
Reporting standards
Documentation and audit trails
Competent Person requirements
Case Study: JORC-compliant reporting in Australia

Module 7: AI & Data Analytics in Resource Estimation

Machine learning for grade prediction
Data integration
Predictive analytics models
Automation using Python/R
Real-time data analytics
Case Study: AI-based lithium resource modelling in Canada

Module 8: Economic Evaluation & Decision-Making

Cut-off grade optimization
Sensitivity analysis
Resource-to-reserve conversion
Financial modelling inputs
Decision-making under uncertainty
Case Study: Coal reserve optimization in Indonesia

Training Methodology

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Geological modeling Grade estimation Statistical analysis Resource classification

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 26 2026

26 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $1,000