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In this webinar, Robin Simpson, Principal Consultant, SRK Consulting (Russia) demonstrates how Leapfrog Geo and Edge can create twinned pairs of data from each sampling type, and then analysing the statistics of the paired grades. The example dataset is an open pit gold deposit, and the core drilling and blast hole samples will be compared.

Mining geologists usually work with drill hole databases that represent multiple generations and types of sampling. An assessment of the relative biases that may exist between the main sources of grade information should be a key component of any data review.

Comparing summary statistics for grades from each type of sampling is often misleading, because the various sampling types are usually clustered quite differently, and even standard declustering techniques may not adequately resolve these spatial differences.

A more sophisticated approach would be to prepare separate estimates based on each main sampling type, but choosing optimal parameters for multiple estimates is likely be time-intensive, and the test estimates themselves may be sensitive to input assumptions and domaining.

This webinar will demonstrate a third option: using Leapfrog Geo and Edge to create twinned pairs of data from each sampling type, and then analysing the statistics of the paired grades. The example dataset is an open pit gold deposit, and the core drilling and blast hole samples will be compared. The same general approach can be applied to other comparisons, such as drilling vs channel samples, and historical drilling vs new drilling.