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In mineral prospectivity mapping (MPM) logistic functions have been widely used to transform mineral exploration data or prospectivity values into the [0, 1] range to generate fuzzified evidential maps or to rank target areas as fuzzy prospectivity models. Recently researchers applied logistic functions to assign fuzzy weights of continuous-value spatial evidence. They assigned fuzzy weights to evidential features without using locations of known mineral occurrences (KMOs) as in data-driven MPM and without discretization of evidential values into some arbitrary classes as in knowledge-driven MPM to overcome exploration bias. However these methods suffer exploration bias resulting from expert judgments in defining slope ( s ) and inflection point ( i ) of the logistic function, which are defined by trial and error procedure. In this paper, the application of logistic transformation is demonstrated to assign continuous weights to evidential layers of geochemical and geological data. The weights were assigned without discretization of spatial evidence values and without using the locations of KMOs, while the i and s values of the logistic function were defined by a data-driven way. For this, we applied systems of equations including two equations and two unknown variables (i.e., i and s ). Thus by solving the system of equations the two unknown variables, i and s , were defined. [ABSTRACT FROM AUTHOR]

Abstract: Integration of stream sediment geochemical data with other types of mineral exploration data, especially in knowledge-driven mineral potential mapping (MPM), is a challenging issue. In this regard, multivariate analyses (e.g., factor analysis) are generally used to extract significant anomalous geochemical signature of the mineral deposit-type sought. In this study, we used stepwise factor analysis to generate a geochemical mineralization probability index (GMPI) through a new approach to create stream sediment geochemical evidential maps. GMPI is a weight that can be mapped, and hence, can be used as an evidential map in MPM. Using stepwise factor analysis enhances recognition of anomalous geochemical signatures, increases geochemical anomaly intensity and increases the percentage of the total explained variability of data. With the GMPI, we developed a new data-driven fuzzification technique for (a) effective assignment of weights to stream sediment geochemical anomaly classes, and (b) improving the prediction rate of mineral potential maps and consequently increasing exploration success. Furthermore, the predictive capacity of each stream sediment geochemical sample for prospecting the deposit-type sought upstream of its location can be evaluated individually using GMPI. In addition, the GMPI can be used efficiently in knowledge-driven MPM as a new exploratory data analysis tool to generate a weighted evidential map in less explored areas. In this paper, we successfully demonstrated the application of GMPI to generate a reliable geochemical evidential map for porphyry-Cu potential mapping in an area in Kerman province, southeast of Iran. [Copyright &y& Elsevier]

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]

Weighting and synthesizing exploration evidence criteria for mineral prospectivity mapping (MPM) are affected by complexity and ambiguity of ore mineralization processes. In this regard, fuzziness could facilitate the modeling of such vague processes for MPM. Furthermore, imprecise selection of the exploration criteria to be used in MPM has negative influence on the efficiency of the generated prospectivity models. In this paper, of various exploration criteria, a coherent set of exploration features were recognized by using the distance distribution analysis. Then, the application of cosine amplitude-based similarity procedure was adapted as a data-driven fuzzy logic approach for predictive mapping of porphyry-Cu prospectivity in Arasbaran metallogenic zone, NW Iran. In addition, a conventional data-driven fuzzy prospectivity model was generated for comparison purpose. Comparison of the two models demonstrated the superiority of the cosine amplitude-based fuzzy procedure for MPM. [ABSTRACT FROM AUTHOR]

• An efficient geochemical evidence layer was generated. • The layer was used as a response to ore deposition processes. • The layer was applied for mineral exploration targeting. Recognition of mineralization-related geochemical footprints and modeling their multi-element dispersion patterns are important aspects to consider when "vectoring" toward undiscovered ore deposits. The collection, analysis, and interpretation of stream sediment geochemical data together make an exploration method that has proven to be successful at the district scale mineral exploration targeting. Identifying the possible sources of stream sediment geochemical anomalies and mapping evidence (i.e., footprints) of the underlying ore-forming processes, however, are not trivial tasks. This is because stream sediment samples represent transported material reflecting the entire geology upstream from the sample locations. Furthermore, indicator element distribution patterns are commonly strongly affected by local factors such as regolith, topographic gradient, vegetation density, and/or climate. Therefore, there is a need for finding a better geochemical anomaly separation method regarding the nature of geochemical data obtained from the area sampled. The main objectives of this study were (1) evaluating and comparing the spatial U-statistic and concentration–area fractal modeling methods of anomaly identification, both amenable to spatial analysis, for recognizing geochemical footprints of porphyry copper mineralization, and (2) measuring their performance in the context of district-scale exploration targeting in an area located in southeast Iran. Subsequently, the methods were first evaluated on a dataset of element contents to decompose anomalous populations. Finally, the geochemical model that proved more efficient with respect to predicting the known mineral deposits was integrated with additional evidence maps to delineate exploration targets. Our evaluation of the resulting geochemical targeting model demonstrated that the targets derived from this method are robust and worthy of follow-up exploration. [ABSTRACT FROM AUTHOR]