Lithium-caesium-tantalum (LCT) Pegmatites are one endmember of the Rare-element (REE) pegmatite family, capable of hosting economically significant concentrations of Li. There is a strong record in the literature documenting the petrogenesis, metallogeny, geodynamic, and tectonic settings of granite-related mineralisation systems including pegmatites. Lithium-caesium-tantalum pegmatites occur on all continents; most frequently associated with Archean Cratons. These pegmatites formed in pulses that coincide with periodicity of orogenic granite, and detrital zircons during times of supercontinent assembly and major collisional orogenic events (Duuring 2018 and references therein). The granitic parental magmas of LCT pegmatites are dominantly peraluminous and inferred to be derived from melting continental crust at depth (Cerny et al. 2005). Enrichment in lithium is attributed to extensive fractional crystallisation of compositionally evolved granitic magmas. Archean Cratons in Western Australia are highly prospective and host some the of world’s largest LCT pegmatite deposits. Recently we have seen attempts to translate this knowledge of world-class examples into a mineral systems framework for REE pegmatites that would enable a more systematic targeting approach for mineral exploration (Sweetapple 2017; Duuring 2018 and Turnbull et al. 2018). At the same time, the application of machine learning on big data is being used to generate targets for Li-bearing pegmatites in a computing environment that is agnostic of empirical knowledge presented above and independent of any conceptual framework of the mineralising system. Accurate predictions in lithium exploration can be achieved through machine learning techniques applied to lithogeochemistry, mineral chemistry, and geophysics data, with extensive data cleaning and pre-processing as the crucial foundation. This keynote will present our findings for targeting Li-bearing pegmatites, illustrating how this work is impacting concepts embedded in a mineral systems framework.

Reference(s)

Bradley, DC, McCauley, AD, Stillings, LM, 2017, Mineral-Deposit Model for Lithium-CesiumTantalum Pegmatites. In Scientific Investigations Report 2010–5070–O; US Geological Survey: Reston, WV, USA, 32p.

Černý P, Ercit, TS, 2005, The Classification of Granitic Pegmatites Revisited.: The Canadian Mineralogist, vol. 43, p. 2005-2026 Duuring P, 2020, Rare-Element Pegmatites: A Mineral Systems Analysis.: Record 2020/7, Geological Survey of Western Australia, 13p. 

Sweetapple, MT, 2000, Characteristics of Sn-Ta-Be-Li-Industrial Mineral Deposits of the Archaean Pilbara Craton, Western Australia. Australian Geological Survey Organisation Record 2000/44, 54 p. 

Sweetapple, MT, 2017, Granitic pegmatites as mineral systems: Examples from the Archaean. NGF Abstr. Proc. 2017, 2, 139–142. 

Turnbull RE, Morgenstern R, Hill MP, Durance PMJ, Rattenbury MS. 2018. Lithium Mineral Potential in New Zealand. Lower Hutt (NZ): GNS Science. 210 p. (GNS Science consultancy report; 2018/63).