Potential of hyperspectral-based geochemical predictions with neural networks for strategic and regional exploration improvement

Abstract

This paper summarises an evaluation of the application of artificial intelligence to hyperspectral drill-core scans for more effective mineral exploration. The dataset used was based on publicly available core scans and related geochemical analysis from Australia. Prior to unification, a detailed quality assessment of the geochemical data was undertaken. Special focus was paid to gold, silver, copper, iron, uranium, nickel, lead, tin, antimony, arsenic and bismuth contents. The dataset was labelled with defined ore grades related to economic cutoff values. The impact on predictions of different setups is related to the amounts of data used for learning, data design and implementation of the geological domains. Based on 1-metre bins, the results from more than 700 km of drill cores were used and analysed with the potential for geological exploration in different scenarios discussed. The results indicate the enormous potential of the use of hyperspectral scans in combination with artificial intelligence for the development of exploration scenarios and to provide support for exploration geologists and target detection. The application of predictors on scanned drill cores from Australia also indicates mineralised zones that have not been analysed chemically for all metals above economic cutoffs. This result shows the enormous potential of the approach for strategic exploration but also mining operations. Prediction of geochemical concentrations for gold, copper and iron based on a neural network in drill cores is possible. Using mineral abundances from hyperspectral core scans as learning records, and existing elemental geochemical analyses as labels, the predictions are given with an accuracy of better than 80–90%.

KEY POINTS

1. The trained artificial intelligence system has for the first time enabled direct estimation of metal grades from hyperspectral scans.

2. It also shows potential for applications to analyse airborne hyperspectral data for direct mapping of metal grades.

3. Finally, it may pave the way for better plant management by the usage of hyperspectral data for direct grade estimations in operational mining and ore sorting.

Keywords:

• exploration
• drill core
• hyperspectral
• core scan
• artificial intelligence
• neural network
• metals

Acknowledgements

Authors show their gratitude towards staff members of all Australian Geological State surveys, especially Alan Mauger (SA, retired), Lena Hankock (WA), Mick Ramsey (NT), Dr Joseph Tang (Queensland), David Masters (NSW), Colin Marson (Victoria) and David Green (Tasmania). Special thanks to Dr Frederik Beuth, for his introduction and help with the implementation of the neural networks, and Joanne Ho, for preparation of parts of the material. We further thank Carsten Laukamp, for his review of the manuscript and advice for improvement.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This project was funded internally by Dimap Group and G.E.O.S. Engineering.