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Geological knowledge discovery and minerals targeting from regolith using a machine learning approach
conference contribution
posted on 2023-05-23, 10:04 authored by Matthew CracknellMatthew Cracknell, Anya ReadingAnya Reading, de Caritat, PWe identify and understand the diverse nature of Ni mineralisation across the Australian continent using Self-Organising Maps, an unsupervised clustering algorithm. We integrate remotely sensed, continental-scale multivariate geophysical/mineralogical data and combine the outputs of our machine learning analysis with Ni mineral occurrence data. The resulting Ni prospectivity map identifies the location of Ni mines with an accuracy 92.58%. We divide areas of prospective Ni mineralisation into five clusters. These clusters indicate subtle but significant differences in regolith and bedrock geophysical/mineralogical footprints of Ni sulphide and Ni laterite deposits. This information is used to identify and understand the nature of potential Ni targets in regions where prospective bedrock mineralisation is concealed by regolith materials. Our machine learning approach can be applied to the analysis of other mineral commodities and at local-/prospect scales.
History
Publication title
ASEG-PESA 2015Pagination
1-4Department/School
School of Natural SciencesPublisher
CSIRO PublishingPlace of publication
AustraliaEvent title
24th International Geophysical Conference and ExhibitionEvent Venue
Perth, AustraliaDate of Event (Start Date)
2015-02-15Date of Event (End Date)
2015-02-18Rights statement
Copyright unknownRepository Status
- Restricted