ROCKRay implementation success at underground mine South Australia

The Problem:

In 2019, lithological domaining techniques at an underground mine in South Australia were inaccurate with standard deviations of 20-50%. The cost of testing limited the ability to capture the geological variability.

Mining and geotechnical engineers involved in studies required reliable estimates of rock properties for; mine design, ground support design, numerical modelling and fragmentation modelling.

Each of these studies required reliable estimates of the mechanical rock properties including:

  • Rock strength e.g. Uniaxial compressive strength (UCS)
  • Modulus of elasticity e.g. Young’s modulus (E)
  • Density


The Solution:

ROCKRay machine learning software used geological core logs to project three strength properties variables:

  • UCS
  • Young’s Modulus
  • Poisson’s Ratio along 5690 metres of core.

The algorithms leveraged core logging such as geochemistry and mineralogy to estimate rock strength. Full core length was used to estimate rock strength rather than a very small number of core samples tested.

The solution produced 190 times more core rock strength estimates for 3D block modelling.

How ROCKRay works


The Results:

The ROCKRay implementation project at the site was completed within six weeks and enabled project study team to build high granularity 3D block models by providing team with 190 fold increase in the amount of core length with rock strength estimates available for engineering design and 3D ore body modelling.

Before implementing ROCKRay, the site had approximately 30 metres of rock sample laboratory test results for 16 lithological domains from 28 holes. After using ROCKRay the team had access to 5690 metres available.




Richard Jackson Maptek
Richard Jackson – Maptek

“This is an exciting step forward for UCS modelling!

With any measurement that is poorly represented in a dataset, we typically can only apply an average value to the entire domain and have to accept the high level of uncertainty a single average gives us. Using machine learning to find how UCS correlates with geological, geotechnical and analytical measurements and then predicting UCS for the entire length of a drillhole moves us towards utilising our data fully and understanding our deposit in a lot more detail.”

Richard Jackson, – Technical Lead, Geology at Maptek




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