Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia

被引:85
作者
Kuhn, Stephen [1 ]
Cracknell, Matthew J.
Reading, Anya M.
机构
[1] Univ Tasmania, Sch Phys Sci Earth Sci, Canberra, ACT, Australia
基金
澳大利亚研究理事会;
关键词
ST IVES GOLDFIELD; YILGARN CRATON; CLASSIFICATION;
D O I
10.1190/GEO2017-0590.1
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The Eastern Goldfields of Western Australia is one of the world's premier gold-producing regions; however, large areas of prospective bedrock are under cover and lack detailed lithologic mapping. Away from the near-mine environment, exploration for new gold prospects requires mapping geology using the limited data available with robust estimates of uncertainty. We used the machine learning algorithm Random Forests (RF) to classify the lithology of an underexplored area adjacent to the historically significant Junction gold mine, using geophysical and remotesensing data, with no geochemical sampling available at this reconnaissance stage. Using a sparse training sample, 1.6% of the total ground area, we produce a refined lithologic map. The classification is stable, despite including parts of the study area with later intrusions and variable cover depth, and it preserves the stratigraphic units defined in the training data. We assess the uncertainty associated with this new RF classification using information entropy, identifying those areas of the refined map that are most likely to be incorrectly classified. We find that information entropy correlates well with inaccuracy, providing a mechanism for explorers to direct future expenditure toward areas most likely to be incorrectly mapped or geologically complex. We conclude that the method can be an effective additional tool available to geoscientists in a greenfield, orogenic gold setting when confronted with limited data. We determine that the method could be used either to substantially improve an existing map, or produce a new map, taking sparse observations as a starting point. It can be implemented in similar situations (with limited outcrop information and no geochemical data) as an objective, data-driven alternative to conventional interpretation with the additional value of quantifying uncertainty.
引用
收藏
页码:B183 / B193
页数:11
相关论文
共 31 条
[1]  
Barnett C., 2009, 3 GEOSCIENCE BC
[2]   Architecture and geodynamic evolution of the St Ives Goldfield, eastern Yilgarn Craton, Western Australia [J].
Blewett, R. S. ;
Squire, R. ;
Miller, J. M. ;
Henson, P. A. ;
Champion, D. C. .
PRECAMBRIAN RESEARCH, 2010, 183 (02) :275-291
[3]   SmcHD1, containing a structural-maintenance-of-chromosomes hinge domain, has a critical role in X inactivation [J].
Blewitt, Marnie E. ;
Gendrel, Anne-Valerie ;
Pang, Zhenyi ;
Sparrow, Duncan B. ;
Whitelaw, Nadia ;
Craig, Jeffrey M. ;
Apedaile, Anwyn ;
Hilton, Douglas J. ;
Dunwoodie, Sally L. ;
Brockdorff, Neil ;
Kay, Graham F. ;
Whitelaw, Emma .
NATURE GENETICS, 2008, 40 (05) :663-669
[4]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[5]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[6]  
Cherkassky V, 1997, IEEE Trans Neural Netw, V8, P1564, DOI 10.1109/TNN.1997.641482
[7]  
Connors K, 2002, AUSTR I GEOSCIENTIST, P29
[8]   The St Ives mesothermal gold system, Western Australia - a case of golden aftershocks? [J].
Cox, SF ;
Ruming, K .
JOURNAL OF STRUCTURAL GEOLOGY, 2004, 26 (6-7) :1109-1125
[9]   Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer-Mt Charter region, Tasmania, using Random Forests™ and Self-Organising Maps [J].
Cracknell, M. J. ;
Reading, A. M. ;
McNeill, A. W. .
AUSTRALIAN JOURNAL OF EARTH SCIENCES, 2014, 61 (02) :287-304
[10]  
Cracknell M. J., 2014, THESIS