Introduction to the special issue on spatial modelling and analysis of ore-forming processes in mineral exploration targeting

Ore deposits are diverse with much of their diversity attributable to the complex interplay of ore-forming processes with a variety of geological environments, over a range of scales and both in space and time. This diversity makes it difficult for geoscientists to accurately predict the location of undiscovered ore deposits. Improving our understanding of the processes that are critical to ore deposit formation would help us to hone our predictive capabilities. However, this task is difficult to achieve as we cannot observe these genetic processes first-hand and different parameters and ingredients are important at different scales. Modelling offers a means of simulating and analysing ore-forming processes and their mappable expressions. This knowledge can then be used to build a predictive model by translating key process components into spatial proxies that can be mapped or recognized in mineral exploration data. Modelling and analysis of ore-forming processes are therefore critical for the future success of mineral exploration. Currently underutilized in exploration targeting, the application of statistical and mathematical concepts can help steer geoscientists towards a better understanding of the complex geological processes critical in the formation of mineral deposits and, ultimately, improved exploration success rates. This editorial article presents a brief introduction to the main concepts that support a collection of articles published in a virtual special issue (VSI) of Ore Geology Reviews entitled "Spatial modelling and analysis of oreforming processes in mineral exploration targeting". The articles examine three critical themes: (1) Translating the expressions of ore-forming processes and critical parameters of mineral systems into mappable spatial proxies; (2) identifying mineral deposit footprints through geochemical and geophysical data analysis; and (3) targeting and improving the discovery chance of mineral deposits by way of spatial data analysis