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An exploration program lives or dies on the reliability of its assay data. A brilliant geological model, a well-targeted drilling program, a carefully logged core — all of it becomes worthless if the numbers coming back from the laboratory can’t be trusted. Quality assurance and quality control (QA/QC) protocols exist to answer one deceptively simple question with rigor: how do we actually know these assay results are accurate? For an industry where a single misreported grade can move a company’s valuation or, worse, mislead an investment decision, that question deserves more than a passing assumption of laboratory competence.
Why QA/QC Isn’t Optional
Every step between collecting a sample in the field and receiving a number on an assay certificate introduces opportunities for error: sample contamination, mislabeling, preparation errors, instrument drift, contamination during crushing or pulverizing, and outright laboratory mistakes. None of these are hypothetical risks — they occur regularly across the industry, which is exactly why regulatory frameworks like Canada’s NI 43-101 and Australia’s JORC Code require documented QA/QC procedures as a condition of any publicly reported mineral resource estimate. Beyond regulatory compliance, rigorous QA/QC is simply good practice: it protects a company’s credibility, protects investors from decisions based on flawed data, and protects the geological interpretation itself from being built on a foundation of unreliable numbers.
The Core QA/QC Toolkit
Certified Reference Materials (Standards)
Certified reference materials — samples with a precisely known, independently verified grade — are inserted into the sample stream at regular intervals, typically disguised among regular samples so laboratory staff can’t identify and treat them differently. When results come back, the reported value for each standard is compared against its certified value; results falling outside an acceptable tolerance (commonly two or three standard deviations from the certified mean) trigger investigation and, if needed, re-assay of the affected sample batch. Standards are the single most important tool for detecting systematic laboratory bias — a consistent tendency to over- or under-report grade.
Blanks
Blank samples — material known to contain effectively no target mineralization — are inserted into the sample stream to detect contamination occurring during sample preparation or analysis. If a blank returns an unexpectedly elevated result, it signals that contamination is occurring somewhere in the preparation or analytical process, potentially compromising results for samples processed in the same batch.
Duplicates
Duplicate samples test precision — the consistency of results when the same material is sampled or analyzed more than once. Different duplicate types test different points in the process: field duplicates (a second sample collected from the same location) test sampling representativeness; preparation duplicates (splits taken during sample crushing/pulverizing) test the consistency of laboratory sample preparation; and analytical duplicates (repeat analysis of the same prepared pulp) test instrument and analytical precision. Comparing duplicate pairs statistically — commonly through relative difference calculations or scatter plots — reveals where in the workflow precision is degrading, if at all.
Umpire (Check) Assaying
Sending a subset of samples, or their retained pulps, to a second, independent laboratory provides an external check on the primary laboratory’s performance, helping detect systematic bias that internal standards and duplicates from the same lab might not fully reveal, particularly if an issue lies in that laboratory’s own calibration or reference materials.
Setting Insertion Rates and Tolerances
Industry practice typically calls for QC samples — standards, blanks, and duplicates combined — to make up somewhere in the range of 10-20% of the total sample stream, though the specific rate depends on program stage, deposit variability, and regulatory expectations. Tolerance limits for standards are usually set statistically, based on the certified reference material’s established standard deviation, with results outside two standard deviations flagged for review and results outside three standard deviations typically triggering batch rejection or re-assay. These thresholds shouldn’t be treated as arbitrary bureaucracy — they represent the practical boundary between normal analytical variability and a genuine problem worth investigating.
What Happens When QC Fails
A failed QC result doesn’t automatically mean the associated sample batch is worthless — but it does mean the batch requires investigation before its data is accepted into a resource database or public report. Common responses include re-assaying the failed standard or the surrounding batch, reviewing laboratory records for procedural issues around the time of the failure, and, if a systematic pattern of bias or contamination emerges, engaging directly with the laboratory to resolve the underlying cause before continuing to submit further samples. Ignoring or explaining away QC failures without investigation is one of the more serious red flags in exploration data management, and it’s exactly the kind of gap that due diligence reviews and regulatory audits are designed to catch.
Practical Guidance for Exploration Programs
- Establish QA/QC protocols before drilling begins, not retroactively — insertion rates, standard selection, and tolerance limits should be part of the program design from day one.
- Use certified reference materials appropriate to the expected grade range and commodity of the target deposit; a standard calibrated for high-grade gold is of limited use in monitoring a low-grade base metal program.
- Review QC performance regularly during the program, not just at its conclusion — catching a laboratory issue early prevents an entire drilling season’s data from being compromised.
- Document every QC failure and its resolution; this record is exactly what technical reviewers, auditors, and future geologists working with the dataset will need to trust the results.
The Bottom Line
QA/QC protocols are, in a real sense, the trust infrastructure underlying every resource estimate, investment decision, and technical report built on exploration assay data. They add cost and complexity to a sampling program, but that cost is trivial compared to the cost of a resource estimate — or a mining decision — built on data nobody actually verified. In an industry where the entire value proposition rests on trusting numbers from rock nobody directly saw processed, rigorous QA/QC isn’t administrative overhead; it’s the foundation the rest of the geology stands on.
