Geometallurgy
Published: June 13,2024The growth of geometallurgy over the past decades has been largely organic, resulting in different levels of capability and maturity, limited development of industry wide systems and guidelines, and limited opportunities for collaboration. However, the industries resources and operating context are changing and becoming more available. These include:
- An increased focus on data and systems to allow the ability to maintain productivity in the face of deeper, more complex orebodies and realize the opportunities provided by AI.
- Greater focus on resource characterization to support future processing options.
- More focus on tailings characterization to reduce closure costs and environmental impact, in line with increased community expectations.
What is geometallurgy
Geometallurgy is an interdisciplinary practice that plays a pivotal role in any project evaluation or mining operation. It focuses on spatially characterizing different ore and non-ore types within a deposit, considering their impact on processing and mining performance, as well as environmental aspects. By integrating geological, mining, metallurgical, environmental, and economic parameters, geometallurgy aims to reduce technical and operational risk during project evaluation and production stages via informed decisions. Ultimately, the goal of geometallurgy is to maximize the economic value of an orebody through sufficiently characterizing different material types to inform predicted project and operational outcomes through a comprehensive understanding of the data generated.
Read the now published “Introduction to Gemetallurgy White Paper” here!
Guideline for Common Data Reporting in Geometallurgy
Standardized reporting enhances data management, interoperability, and predictive modeling. The guideline covers:
- Structured Data schemas: Defining common formats for comminution, flotation, and leaching tests.
- Automated Mineralogy Standards: Consistency in X-Ray Diffraction, hyperspectral imaging, and particle size analysis.
BACKGROUND
The data value chain consists of five main links: collection, organization, analysis, storage, and use. The focus of this project is on the first step of the chain, improving the collection of data through reporting guidelines as an enabler of better organization, analysis, storage, and use.
The existing methods for reporting data between testing laboratories and customers (mining companies) is largely determined by the metallurgical laboratories that generate and report data and mining companies that receive the data.
The project will only be successful if it is supported and adopted by both metallurgical laboratories, mining companies and in some cases major equipment manufacturers. Stakeholder engagement and industry support will be critical to project success.
PURPOSE
A fundamental principle of geometallurgy is to test a large number (high volume) of samples and to relate metallurgical response to ore properties. The large number of samples and tests typically results in large datasets that contain valuable data. Related disciplines in the mining value chain, such as geology and mining engineering, also manage large data sets; however, these disciplines have a wide range of commercial software applications tailored for their specific data types. There are no commercial applications specifically tailored to managing geometallurgical and mineralogical data. One reason for this is the lack of standardization in the reporting of geometallurgical and mineralogical test results which can create a barrier to the development and commercialization of data management applications.
The development of common data reporting guidelines for geometallurgical and mineralogical data is the first step to unlock greater value from geometallurgical data through better data management, interoperability, efficient data communication, and more efficient and effective data retention and use.
Once data is organized and stored in a database it removes barriers for a wide range of analysis and visualization tools and methodologies, including collaborative innovation, embedded reconciliation and continuous improvement, and machine learning and AI. Data reporting guidelines can improve the quality of the available data, make the industry more efficient, and is a key enabler for downstream data analysis and decision making.
The objective of this project is to develop data reporting guidelines, including templates and user guides for common geometallurgical and mineralogical tests, as a key enabler for efficient consumption of data. The guidelines are intended to include terminology, representation, suggested format, definition, structuring, and tagging. The expectation is to develop reporting guidelines to cover the most common comminution, flotation, leaching, mineralogy (X-Ray Diffraction, automated mineralogy), and hyperspectral analysis forms of data. Note that there are some standards (i.e., ISO) for iron ore that could be considered as well (e.g., RI, RDI, TI).
To ensure a manageable scope, particularly with the large number of potential tests to consider, it is expected that a set of core attributes be defined along with guiding/design Principles. These would provide a standardised reference that can be applied to any test.
IN SCOPE
- Development of guiding/design principles with a standardized format/terminology for the most common terms that can be applied universally.
- Prioritization list
- Data layout
- Glossary/codes
- Results file formats
- Data transfer format
- File names for transfer files
- Common metallurgical and geometallurgical tests
- Mineralogy
- Development of suggest templates
- Development of user guide(s)
- API format
- Stakeholder engagement with major metallurgical labs
OUT OF SCOPE
- Geotechnical and structural data
- The guideline will include data from automated mineralogy but will exclude data formats that cannot easily interact with the manufacturer (i.e., MLA data)
- tQEMSCAN data
- Chemical analysis and reporting
TABLE OF CONTENTS
Under development.
WHAT'S HAPPENING
- 2025: Template generation – Create a structure that explains the basics of each data table and hierarchy between tables. We need a couple more member geometallurgists. If you can help, email Neil at nednie@gmggroup.org .
- 2025: Consolidation – Remove duplicated data between tables and ensure consistency. This is a collaboration opportunity for grad students and researchers led by the steering committee. If you can help, email Neil at nednie@gmggroup.org .
- Dec. 2025: Industry validation – Peer review and voting on minimum data required for each field (geometallurgist, programming, and lab perspectives needed).
- Q1 2026: Generating deliverables – Produce both machine-readable and human-readable versions of the results.
- Q2 2026: Finalize guideline development
Project Steering Committee
Andrew Goulsbra – MMG
Christian Munga – Vale
Cornelia Holtzhausen – IAMGOLD
Keith Merriam – Teck
Leonard Hill – Freeport-McMoRan
Lucas Pereira – HZDR University
Megan Becker – University of Cape Town
Petya Gutzmer – Erzlabor
Pieter Botha – Rio Tinto
Steve Mundell – acQuire
Tomas Hrstka – SGS
Get Involved
To get involved contact us at: gmgprojects@gmggroup.org
Guideline for Representative Sampling in Geometallurgy
This guideline establishes best practices for geometallurgical sampling, including:
- Sampling Design: Statistical techniques for selecting representative samples.
- Measurement Uncertainty Reduction: Methods for minimizing bias and ensuring repeatability.
- Data-Driven Process Control: Linking sample data with real-time mineral processing adjustments.
BACKGROUND
The GMG community met to discuss the importance of geometallurgy and their common pain points and challenges. Representative sample selection was flagged as a critical challenge that should be a starting point because it is one of the first processes in the lifecycle. Despite similar techniques being used by different organizations for geometallurgical sampling across the mining industry, there is a lack of published or shared methodologies. Without industry-wide standards, companies—regardless of their size—encounter difficulties in achieving stakeholder approval for their sample selections. This leaves companies to navigate the complexities of representativity assessment based on disparate internal practices and expert judgment. Moreover, the reporting on sample selection processes is often inadequately detailed, making it challenging for reviewers and stakeholders to assess the representativity of the samples accurately. This lack of transparency and standardization not only impacts the efficiency and effectiveness of geometallurgical programs but also undermines confidence in the results derived from such samples, leading to potential errors in estimated resources and in development strategies.
PURPOSE
The industry’s need for a comprehensive, common guideline for geometallurgical sampling has become a more critical challenge because of the lack of information publicly available. With common guidance available, it can bridge the gap of vagueness in the term “representative” with respect to geometallurgical sampling. There are no accepted industry standards on how to select samples, or how to demonstrate representativity. Such a guideline will foster stakeholder trust in sampling methodologies and outcomes.
The primary objective of this project is to develop a guideline that provides clear, actionable considerations on geometallurgical sampling, encompassing the evaluation of sample representativity, sample selection, and sample management. Deliverables include a comprehensive guideline document, case studies demonstrating the application of these principles, and examples of templates that could be used when implementing concepts from the guideline. Examples including base metals, precious metals, and industrial minerals will be included.
IN SCOPE
Technical Scope
- Declaration of confidence level target
- Representative samples
- Guidelines on typical sample numbers required per domain based on various types and characteristics
- Declaration of the objective of the tests to be performed
- Declaration of the principal features
- Identification of the focus case
- Sampling methodologies
- Sample processing
- Composite samples
- Quality assurance/quality control (QAQC)
- Reporting
Logistical Scope
- Sample collection and chain of custody
- Data management
Methodological Scope
- Sample planning and budgeting
- Environmental and safety considerations
- Stakeholder engagement
- Communication strategies to ensure transparency and address concerns
OUT OF SCOPE
- Geological, geochemical, mineralogical, and metallurgical techniques: While the guideline focuses on sampling, detailed techniques of characterizing the deposit or the samples are beyond the scope of selecting and obtaining and handling samples are not covered.
- Advanced Statistical Modeling: While basic data analysis may be covered, advanced statistical and geostatistical modeling techniques are considered beyond the scope of this guideline.
TABLE OF CONTENTS
1 Introduction to Geometallurgical Sampling
1.1 Importance of Sample Representativity in Geometallury
1.2 Definitions
1.3 The Theory of Sampling Applied to Geometallurgy
1.4 Types of Geometallurgical Sampling
2 Sample Program Planning
2.1 Sample Planning
2.2 Budget and Schedule
2.3 Constraints
3 Selection Rationale
3.1 Sampling Methods Over Deposit Lifespan
3.2 Performance Drivers – Principle Features
3.3 Sampling for Performance Drivers
3.4 Using Drilling Data and Block Models for Sample Selection
3.5 Random Sampling
3.6 Stratified Random Sampling
3.7 Systematic Sampling
3.8 Number of Samples
3.9 Representative Samples
3.10 Quality Criteria for Samples
4 Execution of Sampling
4.1 Sample Extraction
4.2 Drill Core Sampling Best Practices
4.3 Sample Labelling Protocols
4.4 Packaging Guidelines
4.5 Hazards Associated with Geometallurgical Sampling
4.6 Transporation
4.7 Environmental and Safety Considerations
5 Stakeholder Engagement
5.1 Benefits of Stakeholder Engagement
5.2 Steps to Ensure Good Stakeholder Engagement
5.3 Ensuring Transparency in Sampling Processes
6 Case Studies
6.1 Early Stage Project Case Study
6.2 Late Stage Project Case Study
6.3 Operational Mine Case Study
7 Compliance to Codes
7.1 International Reporting Codes
8 Tools and Templates
8.1 Sample Selection Checklist
8.2 Data Reporting Template
8.3 Quality Control Log
8.4 Chain of Custody Form
Table of contents subject to change.
WHAT'S HAPPENING
- September 2025: Steering committee is nearing completion of the technical edit of the initial draft
- September 25: Preview the guideline in action! Webinar – Interactive demo using data
- Q1 2026: Interactive work sessions and demonstrations for industry validation
Project Steering Committee
Adam Johnston – Transmin
Andrew Goulsbra – MMG
Christian Munga – Vale
Craig McClung – Rio Tinto
Cornelia Holtzhausen – IAMGOLD
Iris McElroy – Boliden
Kelly Murphey Cooper – Newmont
Leonard Hill – Freeport-McMoRan
Nick Redman – Anglo Gold Ashanti
Regina Baumgartner – Teck
Simon Dominy – CSM/U, Exeter & Novo Resources Corp.
Tricia Scott – Anglo American
Get Involved
To get involved contact us at: gmgprojects@gmggroup.org
The Project Plan
In response to an increased availability of information, GMG is focusing on the creation of several guidelines addressing each aspect of the discipline (which will be broken down in a white paper). These aspects include:
|
Representative Sampling (underway) |
develop guidance that provides clear, actionable considerations on geometallurgical sampling, encompassing the evaluation of sample representativity, sample selection, and sample management. Deliverables include guidance and case studies demonstrating the application of these principles, and examples of templates that could be used. |
|
Common Data Reporting (underway) |
develop data reporting guidelines, including user guides for common geometallurgical and mineralogical tests, as a key enabler for efficient consumption of data. The guidelines are expected to cover representation, format, definition, structuring, and tagging, as well as reporting guidelines to cover the most common comminution, flotation, leaching, mineralogy (X-Ray Diffraction, TESCAN TIMA), and Hyperspectral analysis. |
|
Geological Characterization |
ensure the samples collected are thoroughly described, both from a qualitative and quantitative point-of-view. |
|
Metallurgical and Environmental Characterization |
develop a dataset that is used for life of mine modelling, process design, assessing tailings disposal methods, and identifying health and environmental risks such as fibrous minerals, acid rock drainage (ARD) and net acid generation (NAG) that can lead to groundwater contamination. |
|
Data Processing |
data consolidation, data quality assurance is intended so all valuable data turned into information for each iteration’s analysis and testing. |
|
Model Development |
cover all aspects that should take place during model development starting with identification of domains in deposits that can permit understanding of metallurgical behaviour specific to geological parameters obtained during characterization stages (geochemistry, minerology, geotechnical, and others). |
|
Model Evaluation |
obtain feedback on whether the model honours the data. |
|
Project Optimization |
discusses how projects and processes may have potential to be optimized through a combination of representative sampling and appropriate testing and underlying understanding geological drivers of performance. |
|
Reconciliation |
validate, calibrate, and ultimately improve the geometallurgical models and forecasts. |