Abstract
A first pass workflow for placing cover sequence materials into four broad lithological groups using A–CNK–FM (Al2O3–CaO + Na2O + K2O–Fe2O3+MgO) and A–CN–K (Al2O3–CaO + Na2O–K2O) ternary diagrams in conjunction with the SiO2 vs Al2O3, Fe2O3 and CaO plus the Ca versus Sr XY diagrams has been derived from lithological logging and laboratory whole-rock geochemistry of 2346 samples. These lithological groups include siliciclastics (quartz sands and clays), weathered material, carbonate lithologies (marine limestone, pedogenic carbonate and compositions intermediate between the two end members) and ferruginous lithologies (including ferricrete and ferruginous sediments). Depositional, weathering and groundwater processes influence sample geochemistry during or after the formation of the material and will influence where the sample will plot on the geochemical diagrams used in this study. Geochemical data for discrete lithologies targeted during sampling (e.g. carbonate lithologies) clusters on diagrams used in the geochemical workflow, whereas data from targeted stratigraphic horizons (e.g. basement–cover interface) are more variable. The ability to characterise rock types that are texturally defined using the geochemical workflow is limited. For instance, the geochemistry of diamictite/conglomerate samples is dependent on the matrix versus clast content and composition as well as the source rock and catchment area. This workflow has the potential to be applied in a mineral exploration environment for rapid interpretation of real-time geochemical (e.g. portable XRF) data to gain an understanding of background geochemistry and geochemical signatures that may be related to underlying mineralisation.
A workflow for broad lithogeochemcial characterisation of cover sequence materials has been developed.
Uses commonly collected major element plus strontium data acquired by laboratory or portable techniques.
Categorises materials into siliciclastics, weathered material, carbonate lithologies and ferruginous lithologies.
Applications include a mineral exploration environment for interpretation of large geochemical datasets to understand geology and mineralisation vectors.
Key points
Acknowledgements
This research forms part of the PhD projects of Eline Baudet, Ashlyn Johnson, Stephanie McLennan, Charlotte Mitchell, Verity Normington, Katherine Stoate, Ben van der Hoek and Keryn Wolff; Honours projects of Kym Custance and Rebecca Hill; and research projects of Ravi Anand, David Giles, Steve Hill, Walid Salama, Caroline Tiddy and Ben van der Hoek. All research was undertaken within DET CRC Project 3.3: Geochemical Sampling of Deep Cover. The authors acknowledge and appreciate the thorough reviews by two anonymous reviewers that significantly improved the manuscript. Brett Harris is thanked for his editorial handling of the manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.