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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.Key points 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. Refereed/Peer-reviewed