Key problems with grab sampling

gold mine screen

gold mine screen

In terms of the theory, grab sampling from muck piles is problematic because:

  • Samplers tend to over sample the fines, and/or pick out high grade looking’ fragments
  • Surface sampling of piles does not test material within the pile
  • Muck piles in development drives/faces are likely to be zoned due to the blasting sequence
  • High or low grade material may preferentially segregate in the pile during mucking
  • The 5% mass reject size (screen size that that rejects 5% of the total stockpile mass) of the material in muck piles is very large; generally in the order of 10 cm to 20 cm from underground blasting, but may be +50 cm in some stoping operations
  • Some correlation usually exists whereby the larger fragments are enriched or depleted in the critical component of value
  • The average squared error made in estimating the true stockpile grade (sampling error variance) is likely to be high, even with large samples.


The general recommendation when considering the application of grab sampling is to be very careful. Where possible, an alternative method such as predicting the grade from in situ samples is likely to be a better option.

Grab sampling has been known to work in more ‘homogeneous’ low nugget effect mineralization styles (e.g. some massive/disseminated base metal deposits), but in heterogeneous high nugget effect systems such as gold (especially with coarse gold present) strong bias is likely. If grab sampling is the only option, the following approaches are recommended to understand error:

  • Carry out tests to investigate whether there is a relationship between fragment size and the grade (s) of interest. For a complete stockpile, or a least several bucket loads that are deemed representative of the ore size distribution, establish the size distribution of the material and identify the screening sizes that split the stockpile mass into say five or more size ranges. Analyse multiple samples from each fraction (at least 10) to determine the variability of the grade distribution
  • Carry out heterogeneity tests and mineralogical characterization on the ore to establish the key sampling parameters and gold deportment. From this work, establish what sample size is required to achieve the minimum level of sampling precision that can be accepted for the sampling
  • During mucking, collect increments from the stockpile (e.g. from each bucket, drawpoint, etc) targeting the size fractions established in the first step and using the sampling masses identified in the second step. For the larger fragment sizes, collect chips from the lager rocks to achieve the required mass, while for the smaller fractions use a screen to exclude oversize and/or undersize material. Collect duplicate samples as a quality control step for say 1 in 10 stockpiles
  • Assay the samples from step 3 and mass weight the results from the screen testing in step 1. Monitor the precision of the results through duplicate sampling.

One of the greatest issues with grab sampling is the size of the primary sample that is required. The few kg of sample that are usually collected over a pile is generally inadequate and leads to a large FSE (potentially to ±500% or more). In the most challenging of cases (e.g. nuggetty gold), it is likely that tonnes of material are required for each sample. This raises the issue of how to collect and assay the samples and the required assay charge size. In this case, the options are usually either sample size and fragment reduction via a sampling tower or total processing through a plant.

Grab sampling may be effective where gold grades vary little between size fractions (e.g. fine disseminated gold that is locked in sulphides) and where both the ore and waste break into pieces of approximately equal size. In many gold mines, despite best efforts, the mine geologist should expect precision to be poor and ore/waste misclassification high unless the operation can afford to crush its ore before sampling. Grab sampling must, therefore, be used with extreme caution as in many cases it can be best described as a random process.

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