Describe various estimation methods of ore reserves.

Methods of Ore Reserve Estimation

Ore reserve estimation methods can be broadly categorized into direct and indirect methods. Direct methods involve actual measurement or sampling of the ore body, while indirect methods rely on geological interpretations and extrapolations.

1. Direct Methods

• Detailed Geological Mapping: This involves meticulously mapping the ore body’s geometry, lithology, and structural features. It provides a fundamental understanding of the ore deposit’s characteristics.
• Channel Sampling: Representative samples are collected along channels cut into the ore body. This provides a relatively accurate grade estimate for localized areas.
• Bulk Sampling: Large volumes of ore are extracted and assayed to determine the average grade. This is often used for initial resource assessment or for verifying estimates from other methods.
• Core Drilling: Obtaining core samples through drilling is the most common and reliable method. Core samples are analyzed for grade and other relevant properties. The spacing of drill holes is critical for accuracy.
• Trenching & Pitting: Used for near-surface deposits, trenches and pits expose the ore body for sampling and geological mapping.

2. Indirect Methods

• Triangulation Method: Based on the geometry of the ore body and the known grades at specific points (e.g., drill holes). It’s suitable for regularly shaped deposits.
• Polygonal Method: The ore body is divided into polygons, and the grade within each polygon is estimated based on the grades of surrounding drill holes. This is a common method for irregular ore bodies.
• Isopach Method: Used for layered deposits, isopachs (lines of equal thickness) are drawn to represent the ore body’s thickness, and the volume is calculated.
• Cross-Sectional Method: The ore body is represented by a series of cross-sections, and the volume and grade are estimated by integrating the areas and grades on each section.
• Geostatistical Methods: These are advanced statistical techniques that utilize spatial variability of data to estimate ore reserves.
  • Ordinary Kriging: A widely used geostatistical interpolation technique that minimizes the estimation variance.
  • Inverse Distance Weighting (IDW): Estimates values based on the weighted average of surrounding data points, with weights inversely proportional to distance.
  • Sequential Gaussian Simulation (SGS): Creates multiple equiprobable realizations of the ore body, allowing for uncertainty assessment.

3. Resource Classification

Ore reserves are classified based on the level of geological confidence and economic feasibility. Common classifications include:

Category	Geological Confidence	Economic Viability
Measured	High	Proven
Indicated	Moderate	Probable
Inferred	Low	Possible

The JORC Code (Australasia) and similar codes (e.g., NI 43-101 in Canada) provide standardized definitions for these categories.

4. Factors Influencing Method Selection

• Geological Complexity: Complex deposits require more sophisticated methods (e.g., geostatistics).
• Data Availability: The density and quality of drilling data influence the choice of method.
• Deposit Type: Different deposit types (e.g., porphyry copper, volcanogenic massive sulfide) require tailored approaches.
• Economic Considerations: The cost of estimation must be balanced against the potential benefits.