
TY  - JOUR
A1  - J. Cracknell, Matthew 
A1  - M. Reading, Anya 
A1  - de Caritat, Patrice 
T1  - Geological knowledge discovery and minerals targeting from regolith using a machine learning approach
JF  - ASEG Extended Abstracts
JO  - ASEG Extended Abstracts
Y1  - 2015
VL  - 2015
IS  - 1
SP  - 1
EP  - 4
KW  - 
UR  - https://doi.org/10.1071/ASEG2015AB283 
N2  - We 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. 
ER  -

