Geochemistry at the Edge of Discovery

The prosperity of future generations relies on strategic metals, non-metals, and minerals essential for technological advancements and clean energy. As social and environmental expectations grow, exploration must become more predictive, focusing on targeted critical mineral resources. Mining operations must minimise environmental impact through more efficient ore processing and metallurgical practices. Commodities like lithium, cobalt, rare earths, and platinum group elements require the sustainable development of new ore deposits. Current exploration and mining methods may not suffice, so future mines will need to leverage automation, digitalisation, and decarbonised processes to meet environmental, social, and governance (ESG) standards. Exploration and extraction strategies currently rely heavily on empirical knowledge of known mineral systems, limiting our ability to meet the rapidly increasing global demand for critical minerals.

In this context, it is crucial to develop research partnerships that prioritise the trans-disciplinary education of future geoscientists, equipping them with the forward looking skills that are needed for predictive exploration and sustainable extraction practices, which are crucial for addressing global demands for critical minerals. Future research avenues will explore commonalities among very diverse mineral deposit “types”, challenging conventional models that treat such mineralised systems as unrelated. The focus on highlighting differences rather than similarities has likely hindered effective exploration targeting and impacted our ability to successfully predict the location of mineralised environments, regardless of the commodity of interest. Process-based science highlighting ore forming mechanisms that transcend different mineral systems offers a clearer view of the lithospheric-scale first-order drivers that control mineralisation, processes that would otherwise be obscured by rigid classification schemes.

Such an approach bridges the perceived chasm among different scientific disciplines and promotes trans-disciplinarity. Through AI and machine learning avenues it will be possible to transform the way we look at datasets and extract knowledge from them, effectively extending the “use by end date” of data that are often interrogated for very limited scopes. As an example, data that are commonly used to define the metallurgical properties of a mineral deposit, which are generally important in the downstream segments of the mining value chain when a resource has already been defined and needs to be processed in the most efficient and sustainable way, can be used to constrain the genetic processes that led to the formation of the mineral deposit. This approach fosters collaboration among researchers with different backgrounds and expertise, offering a new way to maximise use of existing mineralogical, geochemical and isotopic datasets to improve mineral exploration, a priority for both industry and society.