Mining data analysis for resource estimation in Australia

Mining data analysis turns scattered geological observations into an evidence base for estimating the size, quality and confidence of a mineral resource. Drill logs, assays, geological interpretations, geophysical surveys and historical workings all contribute, but their value depends on consistent collection, validation and spatial control.

In Australia, the process commonly supports reporting under the JORC Code, investment decisions on the ASX and planning for operations in regions such as Western Australia’s Pilbara, the Goldfields around Kalgoorlie and Queensland’s Bowen Basin. A sound workflow must therefore combine technical accuracy with clear documentation, regulatory awareness and practical knowledge of local conditions.

Building a reliable geological dataset

Resource estimation begins with data management rather than a statistical model. Exploration teams assemble collar coordinates, downhole surveys, lithology, alteration, mineralisation intervals, density measurements and laboratory results. Each record should retain its source, collection date, analytical method and quality-control status so that later users can identify gaps or questionable values.

Database validation can reveal duplicate samples, overlapping intervals, incorrect coordinates and implausible assay results. A review should also test whether survey data reflect the actual orientation of holes, particularly in structurally complex deposits. Specialist market research resources may help place geological findings within broader commodity, industry and investment contexts, although market commentary cannot replace verified technical information.

Australian projects often work across large distances, with field crews travelling from Perth, Brisbane or regional centres to remote tenements. Reliable offline data capture, satellite communication and disciplined file synchronisation are important when internet access is limited. These everyday operating conditions affect the speed and consistency of exploration data collection.

Sampling, assays and quality control

Sampling practices determine whether an estimate represents the deposit or merely reflects irregular field procedures. Geologists define intervals using consistent geological and mineralisation criteria, while laboratories apply certified standards, blanks and duplicate samples. Failed control samples should trigger investigation rather than being quietly excluded from the dataset.

Sample preparation is especially important where coarse gold, nugget effects or variable mineral grain sizes create high short-range variability. For iron ore, base metals and battery minerals, density and moisture measurements may also influence tonnage calculations. Assay methods need appropriate detection limits and must be suitable for the expected grade range.

Photographs, drone imagery and mobile devices can support sample-tracking and field verification when used under a controlled protocol. Guidance on cell phone cameras is not a substitute for geological QA/QC, but it illustrates how image quality, lighting and device limitations matter when visual records are used to supplement written observations.

Interpreting spatial continuity

Geostatistical analysis estimates how grades and geological properties change through space. Exploratory data analysis commonly includes histograms, probability plots, contact analysis and variogram modelling. These steps help identify skewed populations, outliers, distinct geological domains and directions of continuity.

Domaining is a critical geological decision. A high-grade lens should not automatically be blended with surrounding low-grade material simply because the samples are nearby. Boundaries may follow lithology, structure, weathering, alteration or mineralisation style. The selected search strategy, composite length and top-cut treatment should reflect the deposit’s geology rather than relying on default software settings.

In Western Australia, structural controls and weathering profiles can vary substantially between deposits, while Queensland coal projects may require different treatment of seams, partings and quality variables. The analysis should therefore be reviewed by professionals who understand both the deposit type and the limitations of the available drilling. A transparent technical team overview can help stakeholders understand who is responsible for interpretation, modelling and review.

Estimating tonnes, grade and uncertainty

A block model divides the mineralised volume into three-dimensional cells and assigns estimated values, densities and classification attributes. Common interpolation methods include ordinary kriging, inverse distance and nearest-neighbour estimation. The method should suit the sampling pattern, continuity model and intended use of the estimate.

Validation compares model results with input data through visual checks, global statistics, swath plots and comparisons at different scales. Local bias can remain hidden if only the overall average is tested. Sensitivity analysis can show how changes to search parameters, domaining, top cuts or density assumptions affect tonnes and grade.

Resource classification expresses geological confidence, not profitability. Inferred, Indicated and Measured categories require progressively stronger evidence and continuity support. A deposit may contain a large estimated volume while still having limited economic significance until mining, metallurgy, infrastructure, environmental approvals and operating costs are assessed.

Reporting for decisions and compliance

Australian public reporting normally requires clear alignment with the JORC Code, competent-person responsibilities and explanations of the methods, assumptions and limitations used. Projects may also need to address State-based environmental approvals, water access, heritage obligations and the Native Title Act 1993. These requirements can influence drilling access, mine design and the timing of a resource update.

The local market adds practical considerations. A project near Port Hedland may assess rail and port capacity, while a site near Kalgoorlie may rely on established mining services and FIFO labour arrangements. Energy costs, water availability, exchange-rate movements and demand for minerals such as lithium, copper and iron ore can change the economic interpretation of a geological model.

Approach	Best suited to	Strength	Main limitation
Nearest neighbour	Early geological review and benchmarking	Simple and preserves local values	Does not smooth grades or model continuity well
Inverse distance	Regularly sampled deposits with moderate continuity	Straightforward and easy to audit	Results depend strongly on search settings
Ordinary kriging	Mature deposits with adequate variogram support	Accounts for spatial continuity and provides estimation variance	Requires sound domains, variograms and geological assumptions
Simulation methods	Risk analysis and uncertainty assessment	Represents multiple plausible outcomes	More complex to build, validate and communicate

A defensible report should explain how raw observations became a classified estimate and identify the boundaries of confidence. Clear records also support later reconciliation between predicted and mined tonnes, helping teams refine sampling density, geological interpretation and grade-control practices. Where external information is required, a direct contact channel provides a formal route for requesting clarification without obscuring the technical basis of the estimate.