Resource Modelling & Estimation Analytics Training Course
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.
Course Overview
Resource Modelling & Estimation Analytics Training Course
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.
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@datastatresearch.org or call +254724527104
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 DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.