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Soil-sample geochemistry normalised by class membership from machine-learnt clusters of satellite and geophysics data


Gazley, M and Hood, SB and Cracknell, MJ, Soil-sample geochemistry normalised by class membership from machine-learnt clusters of satellite and geophysics data, Ore Geology Reviews, 139, (Part A) Article 104442. ISSN 0169-1368 (2021) [Refereed Article]

Copyright Statement

ę 2021 Published by Elsevier B.V.

DOI: doi:10.1016/j.oregeorev.2021.104442


The primary aim of mineral exploration is to focus the search space from regional scale (1000ásákm2) to prospect scale (1ľ10ákm2), identify areas that warrant further investigation, and ultimately drill a target. Mineral deposits typically display geochemical anomalism related to the conditions of their formation, or the weathering and mass transport of sediment away from a mineral deposit. However, this anomalism can be obscured in soil-sample surveys due to background variations in host bedrock chemistry and the transformation and redistribution of original minerals during surface weathering, regolith development, and supergene processes. This contribution uses machine-learnt map products to aid in the interpretation of a regional-scale soil-sample dataset. ASTER (satellite spectral) data were combined with airborne radiometric data using Self-Organising Maps (SOM) to generate a map of regolith type. A map of bedrock geology was previously derived by the authors from airborne geophysical data using Random Forests. These two products are used to classify soil samples with an objective bedrock-geology and regolith-type classification. A z-score normalisation was implemented on the soil-sample geochemical data, using two different matrices (regolith type and bedrock geology) to remove the bias of bedrock geology and regolith type from the soil sample chemistry. The signal that remains likely reflects metasomatic processes, and associated elemental anomalism, potentially linked to mineralisation. These data can then be plotted to provide maps of (pathfinder) element anomalies, thus identifying potential exploration targets. We explore this approach to normalise geochemical data in a dataset of 9,924 soil samples collected over an area of 1,028ákm2, from Kerkasha, Eritrea (Nubian Shield). The Kerkasha project is an early-stage exploration project, with a limited understanding of the local geology and mineral deposit types; however, it contains many Au and Cu prospects that have received minimal exploration work.

Item Details

Item Type:Refereed Article
Keywords:Z-score, centred log ratio, soil sample, self-organising map, Random forests, Regolith
Research Division:Earth Sciences
Research Group:Geochemistry
Research Field:Exploration geochemistry
Objective Division:Mineral Resources (Excl. Energy Resources)
Objective Group:Mineral exploration
Objective Field:Copper ore exploration
UTAS Author:Hood, SB (Mr Shawn Hood)
UTAS Author:Cracknell, MJ (Dr Matthew Cracknell)
ID Code:146969
Year Published:2021
Deposited By:Earth Sciences
Deposited On:2021-10-06
Last Modified:2022-10-03

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