۱- Introduction

۲- Exploration information systems (EIS)

۳- Discussion

۴- Concluding remarks

References

Abstract

The advent of modern data collection and storage technologies has brought about a huge increase in data volumes with both traditional and machine learning tools struggling to effectively handle, manage and analyse the very large data quantities that are now available. The mineral exploration industry is by no means immune to this big data issue. Exploration decision-making has become much more complex in the wake of big data, in particular with respect to questions about how to best manage and use the data to obtain information, generate knowledge and gain insight. One of the ways in which the mineral exploration industry works with big data is by using a geographic information system (GIS). For example, GIS platforms are often used for integration, interrogation and interpretation of diverse geoscience and mineral exploration data with the goal of refining and prioritising known and identifying new targets. Here we (i) briefly discuss the importance of carefully translating conceptual ore deposit models into effective exploration targeting maps, (ii) propose and describe what we term exploration information systems (EIS): a new idea for an information system designed to better integrate the conceptual mineral deposit model (i.e., the critical and constituent processes of the targeted mineral system) with data available to support exploration targeting, and (iii) discuss how best to categorise mineral systems in an EIS as scale-dependent subsystems to form mineral deposits. Our vision for the future use of EIS in exploration targeting is one whereby the mappable ingredients of a targeted mineral system are translated and combined into a set of weighted evidence (or proxy) maps automatically, resulting in an auto-generated mineral prospectivity map and a series of ranked exploration targets. We do not envisage the EIS replacing human input and ingenuity; rather we envisage the EIS as an additional tool in the exploration toolbox and as an intelligence amplifying system in which humans are making use of machines to achieve the best possible results.

Introduction

Mineral exploration targeting requires the compilation, integration and interrogation of diverse, multi-disciplinary data (e.g., Hronsky and Groves, 2008). In today’s world, this is commonly done using a geographic information system (GIS), the light table environment of our modern computer-age. A GIS is a powerful computer-based system designed to capture, store, manipulate, analyse, manage, and present spatial data (e.g., Groves et al., 2000) and their non-spatial attributes that can be stored in linked, interrogatable tables. In mineral exploration targeting, as in many other fields of applied geoscience, GIS has surpassed the human ability to integrate and quantitatively analyse the ever-growing amount of geospatial data available, thereby progressively replacing the traditional working methods. Over the past two decades and driven by significant improvements in soft- and hardware capabilities, powerful GIS-based, algorithm-driven methods have been developed in support of exploration targeting. These methods fall under the umbrella term of mineral prospectivity mapping (MPM), also known as mineral prospectivity analysis, spatial predictive modelling or mineral potential modelling (e.g., Bonham-Carter, 1994; Pan and Harris, 2000; Carranza, 2008; Porwal and Kreuzer, 2010; Yousefi and Nykänen, 2017; Hronsky and Kreuzer, 2019).