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Mineral potential targeting using geographical information system is an efficient technique to delimit a study area for further exploration of mineral deposits. This introduction presents an overview of the mineral potential modeling methods and future perspectives of research in the fields of target generation and summarizes the papers that have been incorporated into this Special Issue of the Journal of African Earth Sciences. [ABSTRACT FROM AUTHOR]

Two common problems affect integration of exploration criteria for mineral prospectivity mapping (MPM) in geographic information system (GIS): (a) stochastic error associated with sufficiency in number of known mineral occurrences (KMOs) used to estimate evidential weights and (b) systemic error associated with subjectivity of expert judgment applied to process, analyze, and assign weights to evidential data. In this paper we used logistic sigmoid (or S-shaped) function to transform continuous-value evidential data into logistic space without using KMOs as in data-driven MPM and without discretization of evidential data into classes by using arbitrary intervals based on expert judgment as in knowledge-driven MPM. We generated a prospectivity model using discretized evidential data as well. Then, we compared the prospectivity models generated using continuous- and discretized-value evidential data and demonstrated that the former is better model for selecting target areas for further exploration. [ABSTRACT FROM AUTHOR]

Techniques for GIS-based multicriteria decision-making (GIS-MCDM), like mineral prospectivity mapping (MPM), are concerned with combining information from several criteria into a single evaluation model to solve certain problems in the fields of geosciences. In this paper, we introduce the geometric average method for MPM, as a GIS-MCDM approach, and demonstrate its advantage over the expected value MPM method. The comparative analysis shows that the geometric average MPM method yields better prediction of mineral prospectivity and it overcomes the limitation of the expected value method in terms of using spatial evidence values with the same unit. [ABSTRACT FROM AUTHOR]

Mineral exploration targeting is a highly complex decision-making task. Two key risk factors, the quality of exploration data and robustness of the underlying conceptual targeting model, have a strong impact on the effectiveness of this decision-making. Geographic information systems (GIS) can be used not only for compiling, integrating, interrogating and interpreting diverse exploration data, but also for targeting by employing powerful mathematical algorithms, an approach that is commonly referred to as mineral potential modelling or mineral prospectivity mapping (MPM). Here, we pose and examine key aspects around the question of " how can we get better at mineral exploration targeting using GIS? " We do this by (1) reviewing the fundamental aspects of MPM, (2) identifying significant deficiencies of MPM, and (3) discussing possible solutions to alleviating or eliminating these deficiencies. In particular, we discuss how these deficiencies can be overcome by adopting an intelligence amplification system , such as the recently proposed exploration information system (EIS) for translating critical ore-forming processes into spatially predictive criteria (i.e., predictor maps and spatial proxies) and improving decision-making in mineral exploration targeting. [Display omitted] • Geographic information systems (GIS) are related to exploration information system (EIS). • The current status of study in GIS-based mineral exploration targeting is reviewed. • Use and abuse of mineral deposit models in mineral exploration targeting are discussed. • Mineral system-based exploration targeting using EIS is discussed. • Future research directions are provided. [ABSTRACT FROM AUTHOR]

• A new "information system" is proposed for mineral exploration targeting. • The system is called "exploration information system" (EIS). • EIS uses "mineral system" concepts in conjunction with GIS. • Ore-forming processes are classified into pre-, syn-, and post-mineralization parts. • EIS aims to automated translation of ore-forming processes into spatial proxies. 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. [ABSTRACT FROM AUTHOR]