Orogenic gold prospectivity mapping using machine learning. Issue 1 (1st December 2019)

• Record Type: Journal Article
• Authors: McMillan, Mike; Fohring, Jen; Haber, Eldad; Granek, Justin
• Is Part Of: ASEG Extended Abstracts (Online). Volume 2019:Issue 1(2019)
• Page Start: 1
• Page End: 4
• Publication Date: 2019-12-01
• Subjects: machine learning -- artificial intelligence -- prospectivity -- mining -- gold; Prospecting -- Geophysical methods -- Periodicals; Prospecting -- Geophysical methods; Periodicals
• DOI: 10.1080/22020586.2019.12073020
• Languages: English
• ISSNs: 2202-0586

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.