by Lucy Potter, Founder & CEO at Etana Mining Mentors
The job of a Resource Geologist requires practitioners to call upon a multitude of skills in areas such as data analysis, descriptive and spatial statistics, geochemistry, structural and deposit model geology and mineralogy. This entire spectrum of knowledge must be held together by computing skills, problem-solving savvy and, most of all, a good dose of common sense. Yet another dimension of responsibility is layered on, as a Competent Person is expected to assess Reasonable Prospects for Eventual Economic Extraction (RPEEE), and other modifying factors like land tenure and metallurgical recoveries.
A resource geologist could be forgiven for experiencing a sort of identity crisis; in that they are often not close enough to the rock to be fully in tune with local ore-forming processes and their characteristic features and are not rigorous enough to ensure no statistical rules are bent along the way. Rather, a resource geologist must successfully balance the tension between interpreting natural systems represented by poorly sampled data sets, and using sophisticated methods designed to respect stationarity and change of support for different model objectives. They must also balance the technical aspects of the job with external pressure from management, clients, and downstream users such as mining engineers.
So, although resource geologists must draw upon a breadth of experience that is technical and practical, they must learn to apply that experience not as a set procedure or routine, but as a winding path, partially mapped out, and adapted to the specificities of every deposit and model they seek to represent. It’s this high degree of unpredictability that highlights some key characteristics of a resource geologist like a talent for problem solving, an appetite for continuous learning and an aptitude for collaborative work.
While uncertainty is part of the process, resource geologists can look to several sources to facilitate their work and to produce the most accurate estimates possible given the available data. These can be broken down into three areas: organizational, technical and human.
Since time constraints are ubiquitous, good time management is essential. Figure 1 illustrates the typical workflow for the preparation of a Mineral Resource Estimate, and the approximate proportion of time to be spent on each step. More important than targeting the exact numbers shown, is the recognition that all the steps are crucial, and that skipping any may end up extending the process unnecessarily. However tempting it is to dive right into the data, a project kick-off is extremely valuable. This permits discussion between data providers (e.g. field geologists, core loggers), downstream users (e.g. mine planners, metallurgists) and project managers to highlight objectives and concerns. In the case where different practitioners are responsible for individual sections of the resource estimate (e.g. database management, geological modelling, and geostatistics), additional touchpoints should be added to transfer knowledge. Special mention must also be made of the geological modelling stage, which is often the longest to complete, and has the most significant impact on every subsequent piece of work. No amount of geostatistics can save a model that is not geologically sound.
Under pressure to deliver, validation is often neglected. It should occur several times throughout the process, both informally and formally, and should be documented to avoid replicating errors and to provide an audit trail. A single type of validation, such as a global swath plot, is insufficient. Several visual and statistical validations must be performed both by the practitioner and a peer reviewer.
Finally, a standardized checklist at the conclusion of the model update is recommended to catch potential gaps and to avoid imposing on the time of model users if, for example, the model is missing key information (see example Figure 2).
The veracity of a mineral resource model hinges on careful use of available and verified data, proficient use of modelling and geostats techniques and, above all else, robust geological understanding that translates into the 3D model. Ideally, the resource geologist participates in data collection, and so understands the geological setting, and has an innate sense of the controls on mineralization. In practice, these tasks are often segmented, and the resource geologist must seek out understanding from colleagues who can share knowledge on the interpreted emplacement, geometry and continuity of the system. Photos of the outcrop, drill core and field sketches are essential resources, and their influence should be apparent in a model that is geologically sound.
The data that underpins the interpretation and geostatistics usually comes from several generations of work, from a number of different sources, and has variable levels of confidence. Resource geologists must somehow qualify or quantify the level of confidence and decide which data can be used ‘as is’, what needs some form of adjustment, and what must be removed entirely. One way to manage this is to implement a scoring system that manages data usability objectively and serves as a possible guide for downstream resource classification (see example, Figure 3).
Most resource geologists have access to sophisticated software offering tools like variography modelling, co-kriging, and multiple indicator kriging. The tools are often easy to implement, but complex to understand. And if used inappropriately, they can lead to inaccurate grade estimates, project valuation studies and mine plans. For example, the use of multiple indicator kriging is notoriously risky, since it relies on the separation of data into subsets that can be either too small or too erratic to model appropriately. Kriging has the natural effect of smoothing grades during change of support (e.g. from composited drillhole data to a block model grade). If not handled appropriately, grades above a cut-off can therefore be over or understated. In Figure 4, 16% of the data is above the selected threshold of 100 g/t Ag, but only 7% of the blocks are. Further checks are necessary (such as data declustering) to validate whether this is reasonable.
Software packages also permit the generation of visually appealing, but potentially dangerous geological models. Some common features observed are generally created where data is limited and models blow out to create volume (‘pincushions’), isolated mineralized intercepts are allowed to create individual ‘smarties’, or models weave in and out of discontinuous mineralization to include only higher-grade assays (‘lightning bolts’) (Figure 5).
Human factors in mineral resource estimation are omnipresent: from the client/manager waiting for results, to the mill manager needing to know next week’s feed material, to the exploration geologist wanting to plan their next hole with the best chance of success. There is frequent pressure for bigger numbers (tonnes and grade), faster completion, and/or replication of past results. Resource geologists can reduce the temptation to bend to such influence through frequent communication and updates, thorough documentation of their work and decisions, and by turning to a technical mentor and peers to question assumptions and results.
Resource geology is often seen as an exclusive domain reserved to a select few industry specialists, and that methodologies are shrouded in code and secrecy to keep the general population as bay. In fact, the opposite should be true; resource geology is a genuinely multidisciplinary field that must rely on a vast amount of information, data and knowledge from multiple sources. Above all else, the model must be a 3D representation of that knowledge and data and must also convey the competent person’s confidence of all combined factors.
For more information visit: www.etanaminingmentors.com
The article presents some of the content shared in a February 2022 Geohug seminar.
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