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In a recent assignment, SRK provided Capricorn Copper Pty Ltd with staged independent reviews of the development and reporting of resource estimates under the JORC Code for the various orebodies of its Capricorn Copper Mine, in North Queensland. The mine, which commenced mining activity in early 2017, is one of the largest copper development projects in Australia. The reviews took place in stages, to cover several groups of technical criteria as Capricorn Copper attains the various milestones in the development of the resources. This ongoing and concurrent review process allows any issues or concerns to be highlighted early in the process rather than only once the entire resource estimation process is complete.
The various stages of review are guided by both the logical breakdown provided by the JORC Code and the practical process and timelines associated with developing the resources. The JORC Code identifies groupings of criteria and development stages that benefit from audits and reviews, including sampling techniques and data (including historic data), exploration data and reporting; and estimation and reporting of Mineral Resources.
However, since the resource development process may be iterative, the review stages SRK undertook were further separated into three main activities, namely:
1. Inputs: the various inputs into the Mineral Resource estimation
2. Process: the Mineral Resource estimation process / methodology
3. Execution: the Mineral Resource estimation, reporting activities and reporting.
Within these review stages, SRK compiled and presented key observations during a review of the sites’ exploration, mine geology and resource development functions; geology and mineralisation controls; mineral assemblages; geometallurgy; drilling, logging, sampling and data capture procedures; exploration data QA/QC; sampling and data security; mine geology, grade control and reconciliation; and plant reconciliation.
SRK partnered with DeepIQ in an initiative to tailor a combined approach to improve prosepctivity modelling for BHP. By combining advanced data processing, knowledge compilation, geological interpretation and machine learning (ML), the team investigated multiple approaches to support the discovery of large porphyry copper deposits in a well-explored region in the northern Chilean Andes.
Delineation of artisanal and small-scale mining (ASM) sites from satellite imagery, tracking growth over time.
Field based workshop in alteration training, IRS data collection and analysis, and structural controls on the alteration at the Bozshakol Cu-Mo porphyry open pit.
Supported the project with multiple geology and resource assignments since the year of its discovery.
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