Calculating mineral resources involves a systematic, multi-step process that integrates geological data and statistical methods to estimate the volume and grade of mineralization within a deposit.
The precise calculation of mineral resources follows a structured workflow, often involving sophisticated software and expert geological interpretation. Based on common industry practice and the provided reference points, the core steps involved are:
The Mineral Resource Calculation Process
Estimating mineral resources is a critical step in the life cycle of a mining project, providing the foundation for economic evaluations. The process ensures the data is robust, the geological understanding is sound, and the statistical estimation is reliable.
Here are the key steps involved:
Step 1: Data Inspection and QAQC
This initial step focuses on ensuring the quality and integrity of the raw data collected from exploration activities, primarily drilling.
- Purpose: To identify and correct errors, inconsistencies, and outliers in geological logging, assay results, and survey data.
- Activities: Includes verifying sample locations, validating assay certificates against database entries, checking downhole surveys, and performing quality control checks on standards, blanks, and duplicates.
Step 2: Exploratory Data Analysis (EDA)
Once the data is cleaned and validated, EDA is performed to understand the statistical characteristics and spatial distribution of the mineralization.
- Purpose: To reveal data patterns, identify populations, assess variability, and understand relationships between variables (e.g., metal grades and lithology).
- Activities: Involves generating statistical summaries (histograms, box plots, summary statistics), scatter plots, and grade-distribution maps. This step helps inform subsequent modeling choices.
Step 3: 3D Geological Modeling
Building a three-dimensional geological model is essential to define the boundaries and controls on mineralization.
- Purpose: To create a realistic representation of the subsurface geology, including lithological units, structural features (faults, folds), and mineralized domains.
- Activities: Geologists interpret drill hole data, surface mapping, and geophysical information to construct wireframe solids or implicit models that constrain the mineralization used for resource estimation.
Step 4: Variography Analysis
Variography is a geostatistical tool used to quantify the spatial variability and continuity of mineralization within the defined geological domains.
- Purpose: To understand how the similarity between grade values changes with distance and direction. This information is crucial for selecting appropriate estimation parameters.
- Activities: Calculating experimental variograms from composite assay data and fitting mathematical models (e.g., spherical, exponential) to describe the spatial correlation.
Step 5: Geostatistical Modeling
This step involves using the geological model and variography results to estimate the average grade within blocks of a 3D block model.
- Purpose: To predict grade values in unsampled locations based on nearby sample data and their spatial relationships.
- Methods: Common geostatistical estimation techniques include Ordinary Kriging, Simple Kriging, Indicator Kriging, or Inverse Distance Weighting. The variogram models from Step 4 are used to weight the influence of surrounding samples in Kriging methods.
Step 6: Resource Classification
The final step involves classifying the estimated resources into categories that reflect the level of geological confidence and data support.
- Purpose: To assign categories such as Measured, Indicated, and Inferred Resources based on criteria like data density, geological continuity, and estimation quality.
- Criteria: Classification considers factors like drill hole spacing, variogram range, number of samples used in estimation, and the results of validation checks. This classification is crucial for regulatory reporting (e.g., NI 43-101, JORC Code).
This sequential process ensures a robust and defensible estimate of mineral resources, providing the necessary information for future mining studies and economic assessments.
