Drillholes to Block Model: 5-Day Professional Resource Modeling Course

Course Overview

The Drillholes to Block Model training program is an intensive 5-day course designed for geologists, resource geologists, mining engineers, and technical professionals responsible for mineral resource estimation and geological modeling. This comprehensive hands-on training delivers practical expertise in transforming raw drillhole data into accurate, reliable block models using industry-standard software including Surpac, Vulcan, Datamine Studio RM, Leapfrog Geo, and geostatistical tools.

Participants master the complete resource modeling workflow from drillhole database management through geostatistical analysis, grade estimation, and model validation. With emphasis on best practices, JORC/NI 43-101 compliance, and quality assurance procedures, graduates gain immediately applicable skills that ensure robust, auditable resource estimations meeting international reporting standards.

Target Audience: Resource geologists, exploration geologists, mine geologists, geostatisticians, mining engineers, technical services managers, and professionals involved in mineral resource estimation and reporting.

Prerequisites: Geology or mining engineering degree; basic understanding of ore deposits, geological concepts, and statistics; familiarity with database management and 3D visualization concepts.

Day 1: Drillhole Data Management and Quality Assurance

Morning: Exploration Data Fundamentals and Database Design

Establishing robust data management practices that form the foundation of reliable resource estimation, from field data collection through database validation.

Learning Outcomes:

• Exploration drilling methods: diamond core (DD), reverse circulation (RC), rotary air blast (RAB)

• Drillhole data types: collar, survey, lithology, assay, density, geotechnical

• Database structures: relational database design, primary keys, foreign keys

• Data standards and conventions: collar ID naming, depth intervals, coordinate systems

• Quality control protocols: sampling procedures, QAQC standards, chain of custody

• Understanding assay certificates and laboratory reporting

• Common data quality issues and detection methods

Database Management:

• Importing drillhole data from multiple formats: Excel, CSV, Access, laboratory LIMS

• Database validation checks: overlapping intervals, missing data, coordinate verification

• Handling downhole survey data: azimuth, dip corrections, desurveying methods

• Coordinate system transformations and datum conversions

• Creating derived fields and calculated attributes

• Backing up and versioning exploration databases

Afternoon: Data Visualization and Geological Interpretation

Transforming drillhole data into meaningful 3D visualizations that support geological understanding and domain definition.

Learning Outcomes:

• 3D drillhole visualization techniques in Surpac/Vulcan/Datamine

• Creating custom legends for lithology, alteration, and mineralization

• Section generation: cross-sections, long-sections, level plans

• Drillhole labeling and annotation best practices

• Understanding geological continuity and structural controls

• Identifying mineralization patterns and ore controls

• Preliminary statistical analysis of drillhole data

Practical Exercises:

• Building drillhole databases from raw exploration data

• Performing comprehensive QAQC validation checks

• Creating 3D drillhole visualizations with color-coded attributes

• Generating geological sections for interpretation

• Identifying data quality issues and proposing solutions

• Documenting database assumptions and metadata

Day 2: Geological Modeling and Domain Definition

Morning: 3D Geological Wireframe Modeling

Creating three-dimensional geological models that accurately represent orebody geometry, structural controls, and geological domains essential for resource estimation.

Learning Outcomes:

• Wireframe modeling theory: explicit versus implicit modeling approaches

• Section-based wireframe construction techniques

• Defining mineralized domains: geological versus grade criteria

• Incorporating structural geology: faults, folds, contacts

• Managing complex geological geometries and branching structures

• Understanding topographic surfaces and surface modeling

• Model validation: closure checks, intersection detection, volume calculations

Domain Definition Strategies:

• Hard boundaries: distinct geological contacts, lithological units

• Soft boundaries: gradational transitions, alteration halos

• Grade-based domains: economic versus sub-economic zones

• Weathering profiles: oxide, transitional, fresh rock domains

• Combining geological and statistical criteria for domain boundaries

• Understanding the impact of domain definition on resource estimation

Afternoon: Advanced Wireframe Techniques and Model Refinement

Hands-on application of geological modeling software to create production-quality wireframes from drillhole intersections.

Learning Outcomes:

• Digitizing mineralized envelopes on sections

• String creation and editing techniques

• Surface triangulation and smoothing algorithms

• Boolean operations: unions, intersections, differences

• Wireframe quality assurance and validation procedures

• Creating volume reports and tonnage estimates

• Exporting wireframes for downstream applications

Software Applications:

• Creating explicit wireframes in Surpac/Vulcan/Datamine

• Building implicit models using Leapfrog Geo

• Combining multiple geological interpretations

• Managing wireframe versions and iterations

• Documenting geological assumptions and interpretation rationale

• Validating wireframes against drillhole data

Practical Project:

• Complete geological wireframe for a mineralized deposit

• Creating multiple domain models representing different geological scenarios

• Calculating volumes and comparing interpretations

• Quality assurance checks and peer review

Day 3: Compositing, Statistical Analysis, and Data Preparation

Morning: Sample Compositing and Data Transformation

Preparing drillhole assay data for geostatistical analysis through compositing, ensuring samples are appropriate for spatial analysis and grade estimation.

Learning Outcomes:

• Compositing theory: why composite samples are necessary

• Composite length selection: bench height considerations, statistical guidelines

• Compositing methods: fixed length, weighted average, best fit, variable length

• Handling partial composites at domain boundaries

• Domain coding and composite attribution

• Understanding support effect and volume-variance relationships

• Density assignment methods: measured, default, regression-based

Statistical Analysis Fundamentals:

• Descriptive statistics: mean, median, mode, variance, standard deviation

• Understanding data distributions: normal, lognormal, skewed distributions

• Identifying outliers and high-grade populations

• Histogram analysis and cumulative frequency curves

• Probability plots: Q-Q plots, P-P plots

• Coefficient of variation (CV) as a measure of grade variability

Afternoon: High-Grade Treatment and Top-Cutting

Managing extreme values that can disproportionately influence resource estimates through systematic statistical approaches.

Learning Outcomes:

• Impact of high grades on resource estimation: variance inflation, smoothing effects

• Top-cutting (capping) methodologies: statistical versus economic approaches

• Identifying appropriate cut-off thresholds: percentile methods, log-probability techniques

• Domain-specific versus global top-cutting strategies

• Alternative approaches: restricted search ellipse, indicator kriging, uniform conditioning

• Documenting top-cut decisions for regulatory compliance

• Understanding risk implications of grade capping

Advanced Statistical Techniques:

• Bivariate analysis: scatter plots, correlation analysis between variables

• Multi-element relationships and pathfinder elements

• Contact analysis: grade distribution at domain boundaries

• Declustering techniques: cell declustering, polygonal declustering

• Calculating representative mean grades for domains

Hands-On Exercises:

• Creating fixed-length composites from drillhole assays

• Performing comprehensive statistical analysis by geological domain

• Identifying and applying appropriate top-cuts

• Comparing raw versus composited versus top-cut statistics

• Documenting statistical assumptions for technical reports

Day 4: Geostatistics and Variography

Morning: Geostatistical Theory and Spatial Continuity

Understanding spatial statistics that quantify grade continuity patterns essential for selecting appropriate estimation methods and defining search parameters.

Learning Outcomes:

• Fundamental geostatistical concepts: regionalized variables, stationarity, spatial correlation

• Semi-variogram theory: sill, range, nugget effect interpretation

• Variogram modeling: spherical, exponential, gaussian models

• Anisotropy: geometric versus zonal anisotropy in mineralization

• Nested structures and multi-scale spatial continuity

• Understanding the relationship between variography and geological controls

• Variogram validation and quality checks

Variogram Analysis Workflow:

• Calculating experimental variograms from composite data

• Directional variograms: identifying principal axes of continuity

• Lag spacing and distance selection considerations

• Interpreting nugget effect: sampling error versus micro-scale variability

• Fitting theoretical models to experimental variograms

• Validating variogram models through cross-validation

Afternoon: Kriging Fundamentals and Estimation Methods

Advanced estimation techniques that honor spatial continuity, provide unbiased estimates, and quantify estimation uncertainty.

Learning Outcomes:

• Inverse distance weighting (IDW): advantages and limitations

• Ordinary kriging (OK): theory, assumptions, and applications

• Understanding kriging variance and estimation precision

• Block support versus point support estimation

• Search ellipse definition: ranges, orientations, sample selection criteria

• Minimum and maximum sample requirements

• Comparison of estimation methods: nearest neighbor, IDW, kriging

Alternative Estimation Approaches:

• Indicator kriging (IK) for highly variable deposits

• Multiple indicator kriging (MIK) for conditional simulation

• Uniform conditioning for recoverable resource estimation

• Understanding when to apply different estimation methods

• Advantages and limitations of each approach

Practical Applications:

• Calculating and modeling variograms using GSLib/Isatis/SGeMS

• Interpreting spatial continuity in different geological domains

• Defining search ellipse parameters from variogram models

• Comparing estimation results from different methods

• Understanding the relationship between sample spacing and estimation quality

Day 5: Block Model Creation, Grade Estimation, and Validation

Morning: Block Model Construction and Grade Estimation

Creating three-dimensional block models and populating them with estimated grades using geostatistical methods that honor spatial continuity.

Learning Outcomes:

• Block model design: parent blocks, sub-blocks, block size selection

• Choosing appropriate block dimensions: balancing resolution versus computation

• Block model extent definition and rotation considerations

• Assigning geological attributes to blocks: domain codes, rock types

• Running grade estimation routines in Surpac/Vulcan/Datamine

• Search strategy configuration: multi-pass searches, octant requirements

• Understanding discretization points and grade averaging within blocks

Estimation Parameters:

• Configuring kriging or IDW estimation runs

• Defining search neighborhoods from variogram ranges

• Sample selection criteria and composite weighting

• Managing edge effects and boundary conditions

• Estimating multiple variables: grade, density, metallurgical attributes

• Calculation of kriging variance and classification support

Afternoon: Model Validation, Classification, and Reporting

Ensuring block model accuracy, reliability, and compliance with international reporting standards through systematic validation procedures.

Learning Outcomes:

• Validation techniques: visual validation, statistical comparison, swath plots

• Comparing block grades with composite grades: global and domain-specific

• Nearest neighbor models for validation reference

• Identifying estimation artifacts: smoothing, conditional bias

• Resource classification frameworks: Measured, Indicated, Inferred (JORC/NI 43-101)

• Classification criteria: drillhole spacing, geological confidence, data quality

• Quantitative classification approaches using kriging variance

Quality Assurance Procedures:

• Checking for estimation errors: negative weights, extrapolation issues

• Volume reconciliation: comparing block model volumes with wireframe volumes

• Grade-tonnage curve generation and analysis

• Sensitivity analysis: varying estimation parameters

• Peer review checklists and industry best practices

• Documentation requirements for Competent Person reports

Final Assessment and Deliverables:

• Complete resource estimation project from drillholes to classified block model

• Generating resource statements by classification and domain

• Creating visual validation materials: sections, swath plots, statistics tables

• Presenting estimation methodology and quality assurance results

• Certificate of completion and professional development recognition

Course Deliverables

• Educational licenses for geological modeling and geostatistical software (subject to vendor agreements)

• Comprehensive training manual with geostatistical theory, workflows, and case studies

• Sample drillhole datasets from real deposits for practice

• Block model templates and estimation parameter spreadsheets

• QAQC checklists and validation procedure guidelines

• Video tutorials covering software workflows

• Professional development certificate

• Access to alumni network for ongoing technical support

Why Choose This Course?

Complete Workflow Coverage: End-to-end training from raw drillhole data through classified resource block models following industry best practices.

Software Proficiency: Hands-on experience with industry-standard platforms (Surpac, Vulcan, Datamine, Leapfrog) used globally for resource estimation.

Compliance Focused: Strong emphasis on JORC Code and NI 43-101 requirements ensuring estimates meet regulatory and reporting standards.

Geostatistical Excellence: Comprehensive variography and kriging training providing theoretical understanding and practical application skills.

Quality Assurance: Systematic validation procedures ensuring robust, auditable resource estimates defensible to peer review.

Career Advancement: Resource modeling expertise is highly valued, opening opportunities in exploration, resource geology, consulting, and technical services.

Conclusion

The Drillholes to Block Model course delivers essential skills for creating accurate, reliable mineral resource estimates that support investment decisions and mine planning. Master the complete resource modeling workflow and position yourself as a competent professional in geological modeling and geostatistics.

Enroll today to advance your resource estimation capabilities and contribute to robust, compliant mineral resource reporting.

Keywords: drillhole database, block model creation, resource estimation course, geostatistics training, kriging variogram, geological modeling, Surpac training, Vulcan block modeling, Datamine Studio RM, mineral resource estimation, JORC compliance, NI 43-101, compositing techniques, grade estimation, resource classification, geological wireframe modeling, mining geology training, exploration geology course