An Integrated Geological Knowledge and Machine Learning Approach
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. Other challenges facing the industry include increasing mining complexity as mining operations deepen and grades for key commodities such as copper decline and requirements to demonstrate strong environmental, social and governance principles. At the same time, the industry is also facing the challenge of meeting material growth forecasts as the world economy transitions to the emerging green economy. As the industry increases their undercover exploration efforts, to help support the generation of new targets and ultimately new discoveries, 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. As a result, ML has attracted a lot of interest. 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.
BHP, SRK Consulting and DeepIQ have partnered in an initiative to tailor a combined approach to improve prospectivity modelling. By combining advanced data processing, knowledge compilation, geological interpretation and ML, the team investigated multiple approaches to support the discovery of large porphyry copper deposits (PCDs) in a well-explored region in the northern Chilean Andes.
An overview of the approach, the different steps and of the successes and challenges encountered are presented. Implications for ongoing development and modern mineral exploration are discussed.
