The role of geological data in resource modelling is well documented. A sound geological model is the foundation of a reliable Mineral Resource, and ultimately of an optimised mining operation.

Geology controls geometry and metallurgy

The geometry and metallurgical behaviour of ore are often strongly dependent on geology. Within a single deposit, mineralisation style can change with the host rock: sulphide-rich bedded replacement, shear-hosted veins, stockworks of veins. Each style implies a different modelling approach — narrow high-grade shoots following a shear trend, vein arrays with a distinct orientation, or stratabound replacement bodies.

The regolith profile carries the same weight. Metallurgical response and mining method differ substantially between soft oxide material and preserved, reduced, sulphide-bearing fresh rock.

Despite this, geological models are still sometimes built as simple grade shells interpreted above a cut-off, with limited geological context. Without that context, the shape, size and orientation of the interpreted mineralisation may be incorrect. The error propagates into the Mineral Resource estimate and any subsequent Ore Reserve, and can affect ore recovery — for example, when material with poor metallurgical characteristics reaches the mill without the plant being prepared for it.

Why geological context is still missing from some models

Most geologists agree on the importance of geology, and modelling software has advanced considerably. Two practical constraints, however, remain common:

First, logging on resource drilling is frequently assigned to junior geologists who have not been trained on the deposit, or who are not given adequate tools to support consistent logging.

Second, multi-element geochemistry would resolve much of the ambiguity, but assaying every sample for a full suite is costly, and that cost is difficult to justify at the resource definition stage.

Where pXRF data fits

Portable XRF offers a middle path: a consistent geochemical dataset, acquired at low marginal cost, that can support geological logging and subsequent modelling. In practice, pXRF data can be used to:

• Discriminate units that are difficult to separate visually — for example, mafic from ultramafic rocks, or chemically distinct felsic units;
• Provide an indicative estimate of metal content per interval;
• Characterise ore types through pathfinder element signatures;
• Delineate domains likely to present metallurgical or recovery issues before they reach the plant.

In many cases, the full geochemical suite is not required to obtain the information a geological model needs. Where multi-element data already exists from earlier exploration passes, it can serve to normalise the pXRF dataset and validate its classifications — combining the precision of laboratory data with the coverage and turnaround of field measurement.

Known limitations — and how they are addressed

None of these obstacles is inherent to the method. They are workflow problems, and they have workflow solutions: a simple and repeatable field procedure, automated data transfer and validation in place of manual entry, QAQC monitoring with standards and blanks, and exploratory data analysis approaches that account for the semi-quantitative nature of the data rather than relying on fixed cut-offs . Simple hardware can also make a real difference: a purpose-built stand for measuring in the field, or a robotic scanner running trays back at the office, can turn pXRF acquisition from a chore into a routine.

Conclusion

Used with an organised field system and robust data treatment, pXRF provides geological information at a resolution and cost that conventional assaying cannot match at the resource drilling stage — from regolith classification, to lithological discrimination, to ore characterisation. The instrument is rarely the limiting factor; the workflow around it is.