Soil Geochemical Sampling for Exploration

Soil geochemistry is one of the oldest and most cost-effective tools in mineral exploration. By measuring the concentration of elements in soil, geologists can detect the chemical footprint of mineralisation buried beneath cover. A well-designed soil survey can cover large areas for a modest cost, outline targets for trenching and drilling, and eliminate unpromising ground before expensive work begins.

This article explains how soil sampling works, how to design and execute a survey, and how to interpret results, with attention to the tropical conditions found in much of East Africa.

How Soil Geochemistry Works

When rocks weather, the elements they contain are released, redistributed and incorporated into soil. Around a mineralised body, a halo of elevated concentrations of target and associated elements, often called pathfinders, forms in the weathered rock and soil above it. The soil sample is therefore an indirect measure of what lies beneath.

The signal is stronger or weaker depending on the type of soil. In residual soils, which form in place from the underlying rock, the geochemical anomaly lies close to the source and is easy to interpret. In transported soils, such as colluvium, alluvium or wind-blown sediment, material has moved from elsewhere and may mask or displace the anomaly. A key step in any survey is understanding which type of soil you are sampling.

Choosing Target and Pathfinder Elements

The elements analysed depend on the commodity sought.

Gold: gold itself, together with arsenic, antimony, bismuth, tungsten, silver and sometimes copper, which often accompany gold in orogenic systems.
Base metals: copper, lead, zinc, nickel and cobalt, with sulphur or iron in some settings.
Rare earths, niobium and carbonatite-related deposits: lanthanum, cerium, neodymium, niobium, phosphorus, barium and strontium.
Lithium and pegmatite-hosted minerals: lithium, caesium, rubidium, tin and tantalum.

Multi-element analysis by inductively coupled plasma methods is widely used because it provides a broad suite of elements from a single sample at reasonable cost and helps interpret geology as well as mineralisation.

Step 1: Define Objectives and Conduct an Orientation Survey

Before launching a full programme, clarify what you want to learn, for example to identify gold anomalies over a shear zone or to map lithology under cover. Then run an orientation survey over a known occurrence or old working. This small test checks which soil horizon, grain size fraction and analytical method give the clearest anomaly, and how wide the anomaly is. Orientation work is cheap and can prevent a large survey from being collected in the wrong way.

Step 2: Design the Sampling Grid

Grid design balances coverage, resolution and cost. General principles include:

Orient lines perpendicular to the expected strike of the geology or structures, so that the survey crosses the target rather than running along it.
Use wide spacing for reconnaissance and tighten for follow-up. For example, regional lines may be spaced 200 to 400 metres apart with samples every 50 to 100 metres, while detailed infill grids might use 50 by 25 metre or 100 by 25 metre spacing. The right spacing depends on the expected size of the target.
Extend lines beyond the target to establish background values on both sides of the anomaly.
Allow for access and terrain. Rivers, swamps, steep slopes, settlements and farmland shape what is practical, and sampling points can be adjusted on the ground while keeping the grid logic.
Record every sample location with a handheld or differential GPS and store coordinates in a database.

Step 3: Collect Samples Consistently

Consistency matters more than any individual technique. A typical procedure includes:

Clear surface litter and organic material at each station.
Dig a small pit or use an auger to reach the target horizon, commonly the B horizon where metals accumulate, or the C horizon near weathered bedrock. The same depth and horizon should be sampled throughout.
Collect a sample of several hundred grams to a couple of kilograms, depending on the laboratory’s requirements.
Record the soil colour, texture, moisture, horizon, presence of rock fragments, evidence of transport and any nearby disturbance.
Place the sample in a labelled, durable bag with a unique sample number, and label it twice, inside and outside.
Clean tools between samples to avoid cross-contamination.

Field teams should be trained, supervised and given a written procedure. A photograph of each pit or auger hole supports later interpretation.

Step 4: Prepare and Assay Samples

At the laboratory, samples are dried, disaggregated and usually sieved to a fine fraction. A common choice is minus 80 mesh, around 180 micrometres, because fine particles carry the geochemical signal in many soils. The fraction chosen should match the orientation survey results.

Analytical choice matters.

Aqua regia digestion with ICP analysis is widely used and cost-effective for gold and many pathfinders, though it is a partial digestion that may not dissolve all minerals.
Fire assay is a classic gold method with high reliability for larger samples, but it is more expensive and typically reserved for follow-up or rock samples.
Four-acid digestion is closer to a total digestion for base metals and many other elements.
Portable X-ray fluorescence can provide rapid field data, though accuracy is lower and it is best used for screening alongside laboratory assays.

Detection limits must be low enough to see background variation. For gold in soils, a limit of one part per billion or a few parts per billion is typical, while higher limits can hide subtle anomalies.

Step 5: Apply Quality Assurance and Quality Control

Without quality control, geochemical data cannot be trusted. A standard programme inserts control samples into every batch.

Certified reference materials of known composition check the accuracy of the laboratory.
Blanks of barren material check for contamination during preparation.
Field duplicates test the reproducibility of sampling.
Laboratory duplicates and repeats test analytical precision.

A common practice is to insert controls at a rate of around five percent of samples for each type. Results are plotted and reviewed as they arrive, and batches that fail are re-assayed. Clear sample chain-of-custody records and secure storage are also part of good practice. Using an accredited laboratory strengthens credibility with investors and regulators.

Step 6: Process and Interpret the Data

Interpretation begins with the statistics.

Examine distributions. Geochemical data are usually skewed and often approximate a lognormal distribution, so logarithmic transformations and probability plots help to separate background from anomalous populations.
Choose thresholds. Typical approaches define thresholds from percentiles, from the background mean plus two standard deviations, or from breaks on probability plots, ideally refined with orientation results.
Map the data. Plot values as coloured dots or gridded contours over geology, topography and geophysics.
Look at element associations. Multi-element patterns can reveal lithology, alteration and the type of mineralisation. For example, gold with arsenic and bismuth suggests an orogenic gold system, while copper with zinc suggests a base-metal source.
Integrate with other data. An anomaly lying along a mapped shear zone and matching a magnetic or induced polarisation feature is far more compelling than an isolated high.

A good anomaly is reproducible, shows a coherent shape, is supported by more than one sample and element, and is consistent with the geological model.

Challenges in Tropical and Deeply Weathered Terrain

Much of western Kenya and East Africa is covered by deeply weathered regolith. These conditions create special problems.

Transported cover and laterite. Ferricrete, colluvium and alluvium can bury or displace anomalies. Mapping regolith types helps decide where soil sampling is valid and where deeper sampling, such as auger drilling to saprolite, is needed.
Leaching. Some elements are leached from the upper soil while others are enriched, causing patterns that differ from the underlying bedrock.
Contamination. In areas of artisanal mining, spilled ore, tailings and processing sites can produce false anomalies. Observing local activity during sampling and recording it is vital.
Dilution and nugget effects. Coarse gold is erratically distributed, so duplicate samples can differ widely. Larger sample masses and adequate sampling density reduce this problem.
Seasonal access. Rains can interrupt programmes and influence soil moisture and sampling conditions.

Follow-up Work

Anomalies identified by soil sampling are followed up systematically. Infill soil lines refine the anomaly shape. Mapping and rock sampling identify the source. Trenching or pitting exposes bedrock, while ground geophysics maps structures and sulphides. Auger or shallow drilling tests the base of the weathered profile. Only after these steps are drill holes planned.

Cost Considerations

Soil sampling is inexpensive compared with drilling. The all-in cost per sample, including field work, laboratory fees and data handling, commonly falls in the range of a few tens of United States dollars, depending on the analytical suite, remoteness and local labour costs. A programme of several thousand samples is therefore typically a fraction of the cost of even a modest drilling campaign, yet can narrow the search area dramatically.

Conclusion

Soil geochemical sampling remains one of the best-value tools in exploration when it is carefully designed, consistently executed and properly interpreted. The key principles are to run an orientation survey, understand the soil type, collect samples to a uniform standard, use appropriate analytical methods, apply rigorous quality control, and integrate the results with geology and geophysics. In the weathered landscapes of Kenya and East Africa, these principles help turn a grid of small bags of soil into a convincing case for the next stage of exploration.