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Lithologic mapping using random forests applied to geophysical and remote-sensing data; a demonstration study from the Eastern Goldfields of Australia

Stephen Kuhn, Matthew J. Cracknell and Anya M. Reading
Lithologic mapping using random forests applied to geophysical and remote-sensing data; a demonstration study from the Eastern Goldfields of Australia
Geophysics (August 2018) 83 (4): B183-B193

Abstract

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 remote-sensing 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.


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 83
Serial Issue: 4
Title: Lithologic mapping using random forests applied to geophysical and remote-sensing data; a demonstration study from the Eastern Goldfields of Australia
Affiliation: University of Tasmania, School of Physical Sciences, Hobart, Australia
Pages: B183-B193
Published: 201808
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 31
Accession Number: 2018-090959
Categories: Applied geophysicsEconomic geology, geology of ore deposits
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 3 tables, sketch map
S32°00'00" - S31°00'00", E121°10'00" - E122°15'00"
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2019, American Geosciences Institute.
Update Code: 201849
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