Article #1: How to Improve Resource Estimation
Resource estimation determines both the quantity and quality of a mineral deposit. It is the foundation of all mine planning. But what if it’s not representative?
To determine a resource estimate, geologists must create resource models based on their interpretation of geology and mineralisation controls, which affect ore quality, while at the same time also appropriately quantifying risk and taking the cost of optimal drilling densities versus deposit complexity into account. At each step, inaccuracies in the physical characterisation of the mineralisation and associated assumptions may accumulate, lowering the quality of the estimate.
Any estimate is an educated guess: how to make it the best guess possible is the challenge
To estimate the properties of a mineral deposit, a geologist must thoroughly understand the deposit as well as the method of emplacement/mineralisation, especially as economic orebodies around the world become increasingly complex. And that requires sound, reliable data.
Yet traditionally, mining companies have depended on two exploratory drilling methods to obtain the physical samples — typically the only working data — geologists have used to model and estimate mineralisation:
- diamond drilling, which involves withdrawing small diameters of core rock for analysis, and
- reverse-circulation drilling, which involves collecting crushed rock cuttings for analysis.
That means billion-dollar decisions are based on the physical analysis of a very small amount of material, while the bulk of the material to be mined, both overburden/waste and the mineralised orebody itself, remains unexamined.
To help make resource estimates more robust, geologists need more data from other sources.
The good (and bad) news about advances in data acquisition
The good news is that advances in data acquisition technologies mean that a whole new world of data — from downhole geophysics, multi- and hyper-spectral core scanning, and more routine collection of geometallurgical parameters — is now available to inform and enhance resource modelling and estimation.
At the same time, however, this additional information can result in an overload of big data (potentially many terabytes in size) that might also have varying degrees of accuracy and which must be separately validated before it can be used. Validation can add substantial time and effort, since the new “non-traditional” data must be made to work with — and be stored and visualised alongside — the traditional physical drilling information, such as lithologies and assays, typically found in a geologist’s resource database. It also makes automatic modelling and simulation a complex, processing-intensive task.
Improving the likelihood of producing good quality estimates
A poor dataset will always produce a poor estimation; a good dataset, taking into account all available data, will produce an estimation that is more statistically sound, with clearly defined reasoning behind each of the decisions made along the way.
The three articles that follow in this technical series discuss ways to improve the likelihood of producing a dependable, statistically sound resource estimate by understanding:
- The challenges of managing big data for resource estimation
- How to use big data in resource estimation, and
- How to choose the right technology platform for managing big data.
A related technical series, called How to Use Machine Learning in Resource Estimation, will follow after this one. Topics in this second series include what machine learning is and how it works, along with how it can be used in automatic data domaining to provide geologists with the most suitable sub-datasets to use in estimating distinct volumes of the orebody.
Author
Michael Mattera is a Mining Industry Process Consultant at Dassault Systèmes GEOVIA with 30 years of experience in Industry. Michael holds an MSc (Engineering) in Mineral Economics from the University of the Witwatersrand. He has experience across a wide range of commodities and geographies leading to a broad understanding of multiple mining disciplines and associated technical systems. This experience includes resource modelling and estimation, multi-disciplinary project reviews focusing on Mineral Resources (PFS to Post Investment stages), public reporting of Mineral Resources and Ore Reserves (R&R) in multiple jurisdictions, associated governance and assurance processes and development of multiple R&R reporting systems.
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