SkillsLib.ai

Skill: Resource Estimation Support

Validate resource estimates against JORC, NI 43-101, and CRIRSCO standards

3.7(33 reviews)
500+ downloads
Updated Oct 2026
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What You Can Do

You can systematically review kriging assumptions, variogram interpretations, and search neighborhood designs to strengthen estimate credibility. This skill helps you diagnose bias and variance issues in block models, compare estimation methods with structured analysis, and map your methodology to JORC Table 1, NI 43-101, or CRIRSCO compliance requirements—turning complex geostatistical work into defensible technical documentation.

Features

Kriging assumption validation

review kriging methodology, variogram interpretation, and search neighborhood design against best practices

Estimation method comparison

systematically compare inverse distance, kriging, and nearest-neighbor approaches with bias and variance trade-off analysis

Bias and variance diagnosis

identify root causes of model bias or variance issues and recommend corrective actions

Compliance mapping

align resource estimation methodology with JORC Table 1, NI 43-101, or CRIRSCO standard requirements

QA/QC protocol development

create defensible quality assurance and quality control workflows for estimation processes

Non-technical communication

translate geostatistical findings into clear language for board presentations and investor disclosure

Classification guidance

evaluate Mineral Resource vs. Ore Reserve classification against regulatory guidelines

Documentation templates

structure technical assumptions and methodology for long-term project file defensibility

Example Output

Example 1: Kriging Validation Summary

  • Variogram model interpretation: Spherical model with 45 m range appropriate for domain geology ✓
  • Search neighborhood: 2×2×1.5 blocks captures 8–12 composites, balancing bias and variance
  • Assumption risks: Stationary mean valid within domain; monitor for grade trends near mineralization boundaries
  • Recommendation: Add soft boundaries or indicator kriging near domain contacts

Example 2: Estimation Method Comparison Table

MethodBias (%)VarianceComp. TimeRegulatory Risk
IDW (p=2)+3.20.18<1 minModerate
Ordinary Kriging+0.80.155 minLow
OK + Soft Boundaries+0.40.148 minLow

Example 3: JORC Mapping Summary JORC 4.1 (Estimation & Modelling): Kriging methodology documented, variogram fit R²=0.92, no apparent bias detected. Classification: Indicated Resource (assumes +2 years mining confidence).

What's Included

  • SKILL.md instruction file with full methodology guidance:
  • Kriging Assumption Checklist: 15-point validation framework for block model review
  • JORC/NI 43-101 Compliance Map: template matching estimation steps to regulatory sections
  • Estimation Method Comparison Template: structured framework for multi-method evaluation
  • QA/QC Protocol Checklist: workflow documentation and bias-variance monitoring template
  • Non-Technical Summary Template: structure for communicating technical findings to boards and investors

Who It's For

  • Mining Geologists — validating block models and resource estimates before regulatory submission
  • Resource Estimation Engineers — systematizing kriging workflows and compliance documentation
  • Technical Directors — reviewing peer estimates and ensuring JORC/NI 43-101 alignment
  • Exploration Managers — preparing resource estimate sections of technical reports for board and investor disclosure
  • Mineral Project Consultants — assessing estimation methodology rigor and regulatory defensibility for third-party reviews

Best For

  • Block model validation and kriging methodology review
  • Diagnosis of bias, variance, or statistical issues in resource estimates
  • JORC Code, NI 43-101, or CRIRSCO compliance mapping and reporting
  • QA/QC protocol development and documentation
  • Technical communication of geostatistical findings to non-technical stakeholders
  • Peer review preparation and anticipating regulator or auditor questions

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