1887
2nd Australasian Exploration Geoscience Conference: Data to Discovery
  • ISSN: 2202-0586
  • E-ISSN:
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Abstract

Summary

As major mineral discoveries have become rarer over the last two decades, the industry has begun to turn to new technologies to assist in the exploration process. One such advancement is the application of machine learning and artificial intelligence (AI) to geoscience data. Mineral prospectivity mapping has been around for decades but with the increase in computer power, recently it has gained traction again as a means for exploration teams to take full advantage of the numerous datasets at their disposal. Although having a team of human experts with a wealth of geoscience knowledge and experience is still fundamental to the exploration process, the ability to robustly integrate and analyse large geoscience datasets over vast spatial regions quickly becomes unwieldy if done manually.

In this study, we developed a new algorithm for mineral prospectivity mapping using a VNet deep convolutional neural network and applied it to finding gold at the Committee Bay greenstone belt in the Canadian Arctic. The machine learning network took all the geoscience data available from the area and generated a prospectivity map for targeting economic orogenic gold mineralization. The results were subsequently validated on a separate nearby region where the machine predictions were compared to gold assay values from drilling. The gold assays from this region were not included in the training process, and the method demonstrated good success in predicting where the highest gold mineralization occurred.

A subsequent gold prospectivity map was produced for the main area in question, and in addition to many new targets the VNet algorithm predicted many targets that the exploration team had previously generated. This suggests that this process assists the exploration team in vetting old targets while opening their eyes to new targets as well. In this way, the algorithm helps to vector in on prospective new and old areas while maximizing the value of all available geoscience data.

© 2019 Taylor and Francis Group LLC
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/content/journals/10.1080/22020586.2019.12073020
2019-12-01
2026-01-22

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References

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  • Article Type: Research Article
Keyword(s): artificial intelligence; gold; machine learning; mining; prospectivity

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