Geological knowledge discovery and minerals targeting from regolith using a machine learning approach

Matthew J. Cracknell; Anya M. Reading; Patrice de Caritat

doi:10.1071/ASEG2015ab283

24th International Geophysical Conference and Exhibition – Geophysics and Geology Together for Discovery

ISSN: 2202-0586
E-ISSN:

oa Geological knowledge discovery and minerals targeting from regolith using a machine learning approach
Authors Matthew J. Cracknell^a, Anya M. Reading^b and Patrice de Caritat^c
View Affiliations Hide Affiliations

^a UTAS / CODES Private Bag 79 Hobart, , TAS, , 7001, [email protected] ^b UTAS / CODES Private Bag 79 Hobart, , TAS, , 7001, [email protected] ^c Geoscience Australia, GPO Box 378 Canberra, , ACT, , 2601, [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2015, Issue 24th International Geophysical Conference and Exhibition – Geophysics and Geology Together for Discovery, Dec 2015, p. 1 - 4
DOI: https://doi.org/10.1071/ASEG2015ab283
- Published online: 01 Dec 2015

Abstract

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.

Article metrics loading...

/content/journals/10.1071/ASEG2015ab283

2015-12-01

2026-01-14

From This Site

/content/journals/10.1071/ASEG2015ab283

dcterms_title,dcterms_subject,pub_keyword

-contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution

10

5

Full text loading...

References

Cracknell, M.J., Reading, A.M. and McNeill, A.W., 2014, Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer-Mt Charter region, Tasmania, using Random ForestsTM and Self-Organising Maps: Australian Journal of Earth Sciences, 61, 287-304.
Cudahy, T.J., 2012, ASTER Geoscience Product Notes for Australia - Version 1 (No. EP-30-07-12-44): CSIRO ePublish.
Cudahy, T.J., Caccetta, M., Lau, I., Rodger, A., Laukamp, C. and Ong, C., 2012, Satellite ASTER Geoscience Map of Australia - Version 1: CSIRO.
Davies, D.L. and Bouldin, D.W., 1979, A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1: 224-227.
Ewers, G.R., Evans, N., Hazell, M. and Kilgour, B., 2002, OZMIN Mineral Deposits Database: Geoscience Australia, Canberra, ACT.
Gallant, J. and Austin, J., 2012, Topographic Wetness Index (3” resolution) derived from 1” SRTM DEM-H v1 (Data Collection): CSIRO.
Kiviluoto, K., 1996, Topology preservation in self-organizing maps: IEEE International Conference on Neural Networks, 1996, 294-299.
Kohonen, T., 2001: Self-Organizing Maps, 3rd ed, Springer series in information sciences, Springer, Berlin.
Minty, B., Franklin, R., Milligan, P., Richardson, L.M. and Wilford, J., 2009, The Radiometric Map of Australia: Exploration Geophysics 40, 325-333.
Percival, P.J., 2013, Index of Airborne Geophysical Surveys (Thirteenth Edition) (No. Record 2013/12): Geoscience Australia, Canberra, ACT.
Tracey, R., Bacchin, M. and Wynne, P., 2007, AAGD07: A new absolute gravity datum for Australian gravity and new standards for the Australian National Gravity Database: ASEG Extended Abstracts, ASEG2007 - 19th Geophysical Conference, CSIRO Publishing, 1-3.
Vesanto, J. and Alhoniemi, E., 2000, Clustering of the self-organizing map: IEEE Transactions on Neural Networks, 11, 586-600.

/content/journals/10.1071/ASEG2015ab283

Article Type: Research Article

Keyword(s): geophysics; regolith; Self-Organising Maps

Most Cited This Month Most Cited RSS feed

- Inversion of data from electrical imaging surveys in water-covered areas
  
  Authors M. H. Loke and J. W. Lane
- Inversion of Magnetic Data from Remanent and Induced Sources
  
  Authors Robert G. Elllis, Barrry de Wet and Ian N. Macleod
- A comparison of smooth and blocky inversion methods in 2-D electrical imaging surveys
  
  Authors M. H. Loke, Ian Acworth and Torleif Dahlin
- Designing matched bandpass and azimuthal filters for the separation of potential-field anomalies by source region and source type
  
  Authors Jeffrey D. Phillips
- Bad Colour Maps Hide Big Features and Create False Anomalies
  
  Authors Peter Kovesi
- Practical 3D EM inversion – the P223F software suite
  
  Authors Art Raiche, Fred Sugeng and Glenn Wilson
- Estimating Noise Levels in AEM Data
  
  Authors Andy Green and Richard Lane
- Target delineation using Full Tensor Gravity Gradiometry data
  
  Authors Colm A. Murphy and James Brewster
- Transdimensional Monte Carlo Inversion of AEM Data
  
  Authors Ross C Brodie and Malcolm Sambridge
- The geotech VTEM time domain helicopter em system
  
  Authors Ken Witherly, Richard Irvine and Edward Morrison
More Less

oa Geological knowledge discovery and minerals targeting from regolith using a machine learning approach

Abstract

Most Read This Month

Most Cited This Month Most Cited RSS feed