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[Display omitted] • Survival bias concept is adapted for mineral exploration. • Next generations of mineral deposits are defined. • Analysis of the exploration search spaces in terms of Null and Negative tests is proposed. • Targeting mineral deposits of the future is discussed. • The findings have implications for the future development of exploration information systems. Broad consensus exists amongst mineral explorers that most outcropping mineral deposits have been found. The next generation of discoveries will rely on our ability to recognize the subtle or cryptic signals of deep-seated deposits. Exploration targeting under such conditions requires greater knowledge of the processes that formed the targeted mineral deposit types and new or improved exploration methods designed to effectively test for buried mineralization. Survival bias is a form of selection bias that is defined as the logical error resulting from neglecting data or information because of their "lack of visibility". In this study, "lack of visibility" refers to situations where (i) mineral explorers ignore or overlook particular terrain because it lacks or contains only weak signals of a mineralizing system, and (ii) such areas are excluded from further exploration as the existing data or information neither confirm nor support the targeting model. Therefore, it is critical to more comprehensively analyze a search space to more confidentially determine whether a terrain without the desired targeting signals satisfies the criteria of a null or negative test. The idea for this study is based on the notion that if a buried mineral deposit was present in an overlooked terrain it would nevertheless comprise distinctive geological features and targeting signals to guide the explorer, although, more likely than not, these signals would be very weak. Here we used a porphyry copper (Cu) district in Iran to explain and illustrate the adaptation of the survival bias concept. More specifically, in this study we tested the usefulness of a recently proposed targeting criterion, namely sites of potential focused fluid flux, as an input to mineral prospectivity analysis and exploration targeting. The findings of our study have implications for the future development of regional- to global-scale exploration information systems (EIS), designed to improve the performance of mineral exploration targeting. [ABSTRACT FROM AUTHOR]

The concept of the "Exploration Information System" (EIS), first postulated by Yousefi et al. (2019), is now being realised and progressively implemented by various geological survey organisations with a particular focus on mineral prospectivity mapping (MPM) and exploration targeting. This Virtual Special Issue (VSI) of Applied Geochemistry showcases a collection of articles about new ideas and computational requisites designed to propel EIS from concept to reality, and which offer possible solutions to the challenges ahead. This Editorial not only serves to introduce the concept of EIS but also the contributions to this VSI, which was organized to showcase the current status quo of EIS-related research. The coherent set of contributions assembled in this VSI presents novel concepts and ideas by both mathematical modellers and geoscientists. [ABSTRACT FROM AUTHOR]

[Display omitted] • Majority voting ensemble technique is proposed for mineral prospectivity analysis. • Confidence index is proposed to mitigate uncertainty associated with prospectivity models. • The methods facilitate robust extraction of exploration targets. • The proposed approaches can be included in exploration information system (EIS). • The framework deals with problem solving in mineral exploration targeting. Various mineral prospectivity modelling (MPM) approaches are available for targeting mineral deposits, each method capable of predicting areas of high prospectivity. Given the diversity of MPM approaches, the modelled areas of high prospectivity can differ across different MPMs. However, rather than a negative, different MPM outputs can benefit mineral exploration targeting because each method has its advantages. Rather, the problem lies in the lack of consensus over how to best select and delimit mineral exploration targets from different MPM results. Here we aim to address the challenges outlined above whilst quantifying and mitigating the effects of inherent uncertainties. We first generate eleven different prospectivity models utilising deep learning, machine learning, fuzzy logic, and geometric average integration methods. Then, we adopt a majority voting ensemble technique to incorporate and combine the predictions of each prospectivity model. Next, we propose a confidence index designed to mitigate uncertainty associated with our multi-technique approach to MPM. The confidence index quantifies variation in prospectivity values for each cell of the MPM target area. The conjunction of a confidence index and majority voting model facilitates consistent and robust algorithm-driven extraction of exploration targets based on an ensemble of prospectivity models. [ABSTRACT FROM AUTHOR]

Mississippi Valley-type (MVT) Pb-Zn deposits are a subtype of sedimentary-hosted mineralization. These deposits are hosted by carbonate sequences in passive-margin tectonic settings. This paper uses the Fry technique and distance distribution analysis to model the spatial distribution pattern of MVT Pb-Zn deposits in the west of Semnan province (Iran) and their association with some geological features, aiming at mapping mineral prospectivity in the area. The modeling results reveal that NE–SW trending faults and Permian-Cretaceous dolomites and limestone are, respectively, major structural and lithological controlling factors of mineralization that operate as conduits and physicochemical subsystems of ore formation. The integration of the corresponding evidence maps of the controlling factors with a model of the geochemical signature of MVT Pb-Zn deposits through a supervised random forest approach, a machine learning technique, gains an exceptional prospectivity map predicting 100% of the known MVT Pb-Zn deposits in only 15% of the study area, which is an achievement. The recognized targets can be planned for further exploration. [ABSTRACT FROM AUTHOR]