Poor sample integrity directly undermines resource estimation by introducing errors that distort grade calculations, tonnage figures, and confidence intervals. When samples do not accurately represent the material they were taken from, the resulting resource model reflects those errors, not the actual geology. The sections below unpack the most common failure points and what you can do to prevent them.
What happens to resource estimates when sample integrity fails?
When sample integrity fails, resource estimates become unreliable because the data feeding the geological model no longer reflect reality. Grades can be systematically over- or under-reported, tonnage calculations drift from actual values, and confidence classifications drop. In serious cases, a project can move from an indicated to an inferred resource category, or be invalidated entirely.
The downstream consequences are significant. Investors and regulators rely on resource estimates to make decisions about project viability and permitting. If those estimates are built on compromised samples, feasibility studies, mine plans, and financial projections all carry hidden risk. A resource estimate is only as good as the samples it is built from, and any break in sample quality creates uncertainty that compounds through every stage of project development.
Even small, systematic errors in sample integrity can shift a resource from economically viable to marginal. This is why exploration teams treat sample quality as a foundational requirement rather than an afterthought.
What are the most common causes of sample contamination in exploration?
The most common causes of sample contamination in exploration are cross-contamination between samples during drilling or splitting, inadequate cleaning of equipment between samples, inappropriate sample bags or containers, and poor handling practices in the field. Each of these introduces foreign material that distorts the chemical signature of the sample.
During diamond drilling, cuttings from one interval can migrate into the next if flushing is insufficient. Rotary and reverse circulation drilling carry similar risks when air or water pressure is inconsistent. Splitting equipment such as riffle splitters and rotary splitters must be thoroughly cleaned between samples, because residual material from a high-grade interval will contaminate the next lower-grade one.
Environmental contamination is also a real concern. Dust from nearby operations, rust from equipment, or contact with wooden pallets and dirty tarpaulins can all introduce trace elements that affect assay results. Using clean, purpose-designed sample bags and sealed containers goes a long way toward preventing this kind of contamination.
How does chain of custody protect sample quality?
Chain of custody protects sample quality by creating a documented, unbroken record of who handled each sample, when, and under what conditions from the moment of collection to final assay. This traceability allows teams to identify exactly where a problem occurred if results appear anomalous, and it deters accidental or deliberate tampering.
A robust chain of custody includes sample tagging at the point of collection, secure packaging with tamper-evident seals, signed transfer records at each handover point, and temperature or humidity controls where samples are sensitive to environmental change. Every person who handles a sample should be recorded, and gaps in the record should trigger a formal review.
Chain of custody is not just about preventing fraud. Most integrity failures in exploration are accidental, caused by mislabeling, mixed batches, or samples left in unsuitable conditions during transport. A clear custody process catches these errors early, before they reach the laboratory and contaminate the dataset.
What is the difference between sample bias and sample variance?
Sample bias is a systematic error that consistently pushes results in one direction, while sample variance refers to random scatter around the true value. Bias shifts your entire dataset away from the truth. Variance creates noise around it. Both affect resource estimation, but they require different responses.
Understanding sample bias
Bias typically comes from a flaw in the sampling method itself. For example, if coarse gold particles are consistently lost during splitting because the equipment favors fine material, every split sample will under-report gold. No amount of additional sampling fixes this unless the method changes. Identifying bias requires comparing results from the same material using different methods, such as check assays or duplicate programs.
Understanding sample variance
Variance is the natural scatter you expect when taking multiple samples from the same material. Some variance is unavoidable, especially in nugget-effect deposits where gold or other minerals are unevenly distributed. High variance does not mean your results are wrong, but it does mean you need more samples to achieve a given level of confidence. Reducing variance involves better sample preparation, larger sample masses, and more replicates.
How should core samples be stored to preserve integrity?
Core samples should be stored in clean, labeled core trays inside a secure, covered facility that protects them from moisture, temperature extremes, UV exposure, and physical disturbance. Proper storage preserves the physical and chemical properties of the core so it can be re-logged, re-sampled, or audited long after the initial drilling program.
Core trays must be clearly labeled with the drill hole identifier, depth interval, and orientation markers. Trays should be stacked in a logical order that allows easy retrieval without disturbing adjacent samples. The storage area should be dry and well-ventilated, because moisture accelerates oxidation and can alter the mineralogy of certain sample types.
Access to stored core should be controlled and logged. Unauthorized handling introduces risk of physical contamination and mislabeling. Many operators photograph core before and after sampling as a reference record, which is also useful for resolving disputes about sample condition at a later stage.
When should QA/QC samples be inserted into a sampling program?
QA/QC samples should be inserted at regular, predefined intervals throughout the entire sampling program, not just at the beginning or end. A common practice is to insert one blank, one standard, and one duplicate for every 20 to 25 routine samples, giving continuous visibility into contamination, accuracy, and precision across the full dataset.
Certified reference materials, known as standards, verify that the laboratory is returning accurate results for known grade values. Blanks, which are samples of material known to contain no target mineral, detect contamination in the preparation or analytical process. Field duplicates and pulp duplicates measure variance at different stages of the sample handling chain.
The timing of insertion matters. Blanks should follow high-grade intervals to catch carry-over contamination. Standards should be distributed across a range of grade levels to check accuracy at both low and high concentrations. If QA/QC results fall outside acceptable limits, the affected batch should be re-assayed before those results are used in any resource calculation.
At Palsatech, our sample processing and geological services are built around these principles. We support exploration teams with technical expertise, purpose-designed facilities, and structured workflows that protect sample integrity from the field through to final reporting. Whether you need short-term field support or a full-service approach for a longer project, we are ready to help you get results you can trust.