From 2023-2025, SRK Exploration (SRK EX) was engaged by Albemarle to identify possible sources of longer-term lithium supply through generative exploration. Following an initial global lithium prospectivity study that defined and ranked geological provinces prospective for a variety of hard rock lithium deposit types, the focus turned to mapping absolute prospectivity at a higher resolution within selected provinces and identifying specific exploration licences, projects and companies with unrealised lithium potential.
An integrated prospectivity and targeting workflow for lithium-caesium-tantalum (LCT) pegmatites was applied over study areas including geological provinces in Australia and Canada. Study areas measured up to ~3.5 million square kilometres with 1x1 km model cell size.
The quality and reliability of any prospectivity analysis is fundamentally based on the even distribution of quality data and selection of datasets appropriate for the target deposit style. The extent of study areas across multiple jurisdictions, with geoscience data controlled by different administrations, presented a significant challenge to the collection, compilation, validation, interpretation and processing of the best datasets available.
The prospectivity analysis workflow integrated knowledge-led mineral system model and data-driven machine learning approaches. Machine learning used a balanced random forest algorithm to quantify spatial statistical relationships between geoscience datasets and the locations of known occurrences, and predict the probability of LCT pegmatite mineralisation.
Using both knowledge-led and data-driven prospectivity mapping approaches permits interpretation using the best of subjective expert knowledge and objective data analytics. Integral model validation and feedback provide insight on data source quality, coverage and density, ensuring results reflect underlying prospectivity and not input data variability.
The next stage of the prospectivity workflow evaluated absolute prospectivity using SRK EX’s Management and Valuation through Absolute Prospectivity tool (M-VAP). This tool harnesses Bayesian statistics and machine learning to quantify absolute prospectivity – the probabilities of discovering deposits of different magnitudes in an area of interest within a given time frame. M-VAP updates a prior prospectivity (i.e. the mineral system prospectivity), by incorporating information on exploration intensity/maturity, deposit location and deposit endowment analysis.
Using combined mineral systems and machine learning to identify areas of greatest prospectivity and absolute prospectivity (MVAP) to rank them, SRK EX attributed new exploration areas or active mineral tenements with quantitative probabilities of discovery and potential deposit sizes. This allowed a consistent and objective approach to ranking single tenements or company portfolios, identifying tangible value upside of potential partner companies, rather than a less tangible relative geological prospectivity.
A final project ranking incorporated the results of additional, non-geological studies mapping environmental, social, governance and infrastructure risks and their impact on project development and future mine construction.
