Sample contamination directly affects exploration results by introducing foreign material into geological samples, causing assay values to read either higher or lower than the true grade of the rock or soil being tested. Even minor contamination can shift analytical results enough to mislead resource estimates and drilling decisions. The sections below walk through where contamination comes from, how it distorts data, and what you can do to prevent it.
What are the most common sources of sample contamination in exploration?
The most common sources of sample contamination in exploration are drilling equipment, sample bags and containers, cross-contamination between adjacent samples, and the surrounding environment. Contamination can enter a geological sample at any point from the moment material is extracted to the moment it reaches the laboratory.
During drilling, residue from previous drill runs can mix with new material if equipment is not properly cleaned between holes or intervals. This is particularly problematic in reverse circulation drilling, where particles can carry over in the sample stream. Core barrels, riffle splitters, and sample bags that are not thoroughly cleaned between uses are frequent offenders.
Environmental contamination is another serious source. Wind can carry dust from nearby stockpiles or spoil heaps onto open samples. Groundwater can flush material from one interval into another. Even the hands and tools of field personnel can introduce organic matter, metals, or soil from other locations if basic hygiene protocols are not followed during geology sample collection.
Labelling errors and mixed sample bags, while not contamination in the chemical sense, produce the same effect on data quality: results get attributed to the wrong location, which distorts the geological picture just as badly as physical contamination does.
How does contamination distort assay and analytical results?
Contamination distorts assay results by artificially inflating or deflating the measured concentration of target elements. If high-grade material from a previous sample interval carries over into a lower-grade interval, the result reads falsely high. The reverse is equally damaging: dilution from barren material pulls grades down and masks genuine mineralisation.
In gold exploration, even microscopic carryover of high-grade particles can produce what geologists call a “nugget effect,” where a single grain of coarse gold creates a spike in assay values that looks like a real discovery. This kind of false positive can trigger expensive follow-up drilling on a zone that does not actually exist.
Contamination also introduces systematic bias across a dataset. If the same piece of equipment is used throughout a programme without proper cleaning, the error compounds over time. Statistical outliers become harder to distinguish from genuine anomalies, and the integrity of the entire dataset is compromised. For field sampling quality, this means that even a well-designed sampling programme can produce unreliable data if contamination is not controlled at every step.
What are the consequences of contaminated samples for resource estimation?
Contaminated samples can lead to incorrect resource classifications, inflated or understated mineral inventory estimates, and flawed economic assessments of a project. In a worst-case scenario, a resource estimate built on contaminated data may significantly overstate the value of a deposit, leading to investment decisions that cannot be justified once the contamination is identified.
Regulatory and reporting standards such as JORC and NI 43-101 require that data quality be verified and disclosed. If contamination is discovered after a resource estimate has been published, the estimate may need to be revised or withdrawn entirely. This carries significant reputational and financial consequences for any exploration company.
Beyond the resource model itself, contamination affects grade continuity modelling. If certain sample intervals are unreliable, the spatial interpolation used to estimate grades between drill holes becomes less accurate. This can result in poorly designed mine plans or incorrect pit outlines that only become apparent during production, when the cost of correction is far higher.
How can contamination be detected before it affects exploration decisions?
Contamination can be detected through a combination of quality control samples, duplicate analysis, and visual inspection of the sample stream. Inserting certified reference materials, blanks, and field duplicates into the sample sequence at regular intervals is the standard method for identifying contamination and other data quality issues before results are used for decision-making.
Blank samples, which are prepared from purpose-made geological sampling products known to contain negligible concentrations of the target elements, are particularly useful for detecting carryover contamination. If a blank inserted after a high-grade sample returns elevated values, it signals that material from the previous interval has carried over into the sample stream.
Visual inspection during soil sampling and core logging also catches problems early. Discolouration, unusual texture, or the presence of foreign material in a sample are all warning signs that should trigger investigation before the sample is dispatched. Field personnel trained in recognising these signs are your first line of defence against contamination entering the analytical pipeline.
What best practices prevent sample contamination in the field?
The most effective practices for preventing sample contamination in the field are thorough equipment cleaning between samples, using clean and purpose-specific containers, minimising sample exposure to the open environment, and enforcing strict handling protocols for all field personnel involved in geology sample collection.
- Clean equipment between every sample interval: Flush drill rods, clean riffle splitters, and replace sample bags to prevent carryover from one interval to the next.
- Use dedicated, unused containers: Sample bags and containers should be clean and dry before use. Reused bags are a direct route for cross-contamination.
- Limit environmental exposure: Cover samples as soon as they are collected. In windy or dusty conditions, work quickly and use covers or lids to protect open material.
- Train all personnel: Everyone handling samples, from drillers to field technicians, should understand contamination risks and follow the same protocols.
- Document the chain of custody: Record who handled each sample, when, and under what conditions. This makes it far easier to trace the source of any contamination identified later.
- Insert quality control samples systematically: Blanks, duplicates, and standards should be inserted at regular intervals throughout every sample batch, not just occasionally.
Consistent application of these practices across the entire sampling programme is what separates reliable field sampling quality from data that cannot be trusted. One lapse in protocol can compromise an entire batch of samples.
When should contaminated samples be re-sampled or rejected?
Contaminated samples should be re-sampled when the original material or a representative portion of it can still be accessed, and rejected outright when the contamination cannot be isolated or the original sample is no longer available. The decision depends on how severe the contamination is, whether the source can be identified, and whether re-sampling will produce a result that is genuinely representative.
If quality control data shows that a blank sample has returned elevated values, the samples collected immediately before and after that blank should be flagged for review. If the contamination appears to be isolated to a specific interval or piece of equipment, targeted re-sampling of those intervals is appropriate.
Rejection without re-sampling is warranted when the sample has been physically mixed with foreign material in a way that cannot be undone, when the sample location can no longer be accessed, or when the contamination source is unknown and widespread enough to affect a large portion of the dataset. In those cases, using the contaminated results, even with caveats, introduces more risk than acknowledging the data gap.
When in doubt, err on the side of re-sampling. The cost of collecting additional samples is almost always lower than the cost of making exploration decisions based on unreliable data.
At Palsatech, we support exploration and mining companies at every stage of the sample handling process, from field sampling and sample processing through to technical support for logging facilities. Our geological and field sampling support services are designed to help you maintain data quality from the moment material is collected to the moment results are delivered. If you want to talk through how to strengthen your sampling protocols or need hands-on support in the field, contact our exploration support team and we are ready to help.