The rock quality designation (RQD) is an important factor for geotechnical work in mining operations. RQD is defined by rock core measurement, which is subjective and time consuming procedure. It would be more effective if we could indirectly define RQD from well logs. We show using engineering and geophysical borehole data from the Kevitsa Mine how we may develop relationships between Vp and RQD. For the prediction to be robust the data needs to be clustered, which for the Kevitsa data four clusters were necessary and sufficient. Our predicted RQD using clustered characteristics shows a very good match with the RQD values from core measurements. This encouraging result that demonstrates how low cost, routine, borehole data can greatly improve the understanding of subsurface engineering properties of a prospect. Furthermore, adding surface or borehole tomography data would create volumetric data that can guide engineering decisions about design and better risk management strategies.


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