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[Display omitted] • Geological map layers derived using machine learning reveal geochemical anomalies. • ASTER and airborne radiometric data and a SOM generate a map layer of regolith type. • Airborne geophysic data and Random Forests construct a map layer of bedrock geology. • Regolith and bedrock map layers inform normalisation of soil geochemical data. The primary aim of mineral exploration is to focus the search space from regional scale (1000 s km2) to prospect scale (1–10 km2), 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 1,028 km2, 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. [ABSTRACT FROM AUTHOR]

Soil samples collected at 50–70 cm depth at 8 km-spaced sample sites over 40,000 km2 of southern New Zealand were analysed for their chemical composition. Nine pathfinder elements (Ag, As, Au, Bi, Mo, Sb, Sn, Te, W), which are useful in exploration for orogenic-style mineralisation, were z-score normalised using two different matrices to account for both lithology and metamorphic grade, which vary significantly across the survey area. Interrogation of interpolated plots of the normalised data, relative to known orogenic-style deposits (Au ± Sb ± W), highlights chemical associations that varied between deposit types, and between similar deposit types mineralised under varying pressure-temperature-time conditions. Active mining for orogenic-style Au along the Hyde-Macraes Shear Zone is coincident with a regionally-significant, multi-element soil anomaly (Ag, As, Au, Mo, Sn, Te, Sb, W). The chemical associations vary along strike of the mined-portion of the shear, such that the northwest has high Ag, Au, Mo, Sn, and Te relative to the southeast which has high Sb, W in soil anomalies, hinting at chemical complexity along the shear. The chemical footprint of this deposit is readily identified in the 8 km-spaced grid, considerably expanding the chemical footprint previously inferred from hard-rock studies. Historic orogenic W mines are associated with high As, Au, Bi, W in soil anomalies, and historic Sb mining with high Bi in soil anomalies. Chemical soil anomalies are likely related to the progressive metamorphic breakdown of minerals, especially rutile and pyrite. Several multi-element soil anomalies that are coincident with known orogenic mineralisation, also occur in areas that have not been mined, and explored only at a reconnaissance level. This study demonstrates the importance of normalisation of chemical data in complex geological settings and highlights the value that can be extracted beyond only plotting raw-concentration, single-element maps, and the significant opportunity in multielement studies of soil in mineral exploration for orogenic-style mineralisation. • Soil samples from 8 km-spaced sample sites over 40,000 km2 of New Zealand • Nine orogenic Au pathfinder elements standardised for lithology/metamorphic grade • The footprint of the Macraes deposit is readily identified in the 8 km-spaced grid. • Important to normalise chemical data, especially in complex geological settings • Significant value that can be extracted beyond raw-concentration, single-element maps [ABSTRACT FROM AUTHOR]