Abstract:
The challenges facing the mining industry are varied not the least being trends toward undercover and deeper exploration as the rate of viable near-surface deposit!discoveries declines, especially in well-established and -endowed mining jurisdictions. As the industry increases their undercover exploration efforts, to help support the!generation of new targets and ultimately new discoveries (near-mine, brownfields and greenfields), new technologies that integrate with more traditional approaches are!being developed, adapted and/or evaluated.
One of these approaches is the use of Machine Learning (ML) in prospectivity!analysis, which promises an objective approach that minimises human bias and the!ability to rapidly interrogate large and disparate data sets. In real world applications,!however, prospectivity analysis is subject to a number of other biases that can be!attributed to the data used, the complicated and tenuous relationship between these!data and the mineral system proxies they aim to map and the sometimes equally!tenuous relationship between proxies and the mineral system processes of interest.!Ill-informed or a perceived lack of links between data, proxy and process is a weakness that can be minimised with a nuanced combination of geoscience and data science.
By combining advanced data processing, knowledge compilation, geological interpretation and ML, multiple approaches have been investigated to support the discovery of large porphyry copper deposits. Outcomes of case studies to date include:
- A productive collaborative approach allows for powerful cross-fertilisation of ideas with no one specialist or specialist team driving outcomes.
- Enhanced and new datasets, based on geological knowledge, are very important to outcomes achieved.
- ML-based Fusion models are an effective means of combining knowledge-based models that capture expert knowledge and deep learning models that are data-driven and have been instrumental in generating more optimal mineral potential/prospectivity models.
An overview of the approach and implications for modern mineral exploration are discussed.
