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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: In stream sediment geochemical exploration, anomalies can be recognized by ‘undiluting’ concentrations of indicator elements in every stream sediment sample catchment basin (SCB) as a function of topographic, geomorphologic, and geologic factors. However, this SCB modeling, like contour mapping, of stream sediment geochemical anomalies depends on sampling locations and sampling density. These arbitrary aspects of stream sediment sampling can render SCB or contour mapping of stream sediment anomalies inefficient. In this paper, instead of evaluating the relative exploration importance of each sample per SCB, we evaluated the relative exploration importance of samples per natural drainage catchment basin (DCB). Accordingly, we developed a new fuzzy weighting scheme for each DCB based on the distribution of anomalous and background samples in each DCB. In this new approach of weighted drainage catchment basin (WDCB) mapping of stream sediment geochemical anomalies, individual DCBs are given fuzzy weights representing their relative importance for prospecting the deposit-type sought. Hence, a map of WDCB can be used directly as a geochemical evidence layer in fuzzy-based mineral prospectivity mapping. In this regard, we demonstrated that the prediction rate of prospectivity map obtained by using WDCB approach with respect to known mineral deposit occurrences is higher than that of prospectivity map obtained by using a SCB or contour map. [Copyright &y& Elsevier]

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]

In this study, fractal/multifractal modeling methods have been applied for preliminary hydrothermal Ni prospection using a set of stream sediment geochemical data of Ahangaran district, which is an arid terrain in eastern Iran. The study area has a complex geological and morphological setting, which is dominated by the presence of aeolian sands. Cretaceous ophiolitic rocks hosting nickel endowment are influenced by extensive listwanitic alteration. Hydrothermal nickel mineralization occurs as veinlets and disseminated forms within the listwanite units. Among the analyzed elements, Ni, Cr and, Co have been recognized as being associated with listwanitic alteration and Ni mineralization, through robust factor analysis of compositional data. The regional background patterns of these mineralization-related elements were delineated via the concentration – area (C-A) fractal modeling method. Different multifractal characteristics of the selected elements were recognized using the multifractal spectrum f ( α ) curves and different multifractal indices. The enrichment and depletion patterns of the related elements in different parts of the study area were also identified using the local singularity mapping technique. The application of local singularity mapping manifested different geochemical patterns that were not recognized via the C-A fractal modeling method. The superiority of the local singularity mapping technique in delineating geochemical populations over the C-A fractal modeling method, was revealed by quantification of the correlation between geochemical anomalies and geological evidence of the mineralization. [ABSTRACT FROM AUTHOR]

Diversity and complexity of geological processes in different areas result in different element associations for prospecting a certain mineral deposit-type sought. In this regard, because element associations are affected by the diversity of geological characteristics of different areas, it is important to analyze and recognize significant geochemical signatures that represent the deposit-type sought. This paper aims to recognize significant multi-element geochemical signatures of porphyry-Cu deposits in the Noghdouz area, Iran, using stream sediment data. For this, we used factor analysis and two modeling methods of geochemical anomalies, sample catchment basin and contour map. Then, to recognize significant geochemical signatures of the deposit-type sought and evaluate the anomaly mapping methods, we adapted prediction-area (P-A) plot, normalized density, and success rate curve. By using these processes, we recognized the best geochemical signature of the deposit-type sought in the study area. The proposed methods in this paper can efficiently be used in other areas to recognize significant geochemical signatures of different types of mineral deposits. [ABSTRACT FROM AUTHOR]

Identification of geochemical anomalies using singularity theory is a topic of interest in the field of mineral exploration. The ordinary singularity mapping technique does not consider the critical role of geological structures in the ore-forming environment. Therefore, models of spatially weighted singularity have been developed through the incorporation of fault systems, which might play an important role in ore-forming processes, and thus, are key factors in spatial analysis of mineralization indicators. In this study, we proposed a strengthened singularity mapping technique that was spatially enhanced and weighted by mineralization-efficient fault systems. For this, we applied distance distribution analysis to distinguish "efficient" (linked to mineralization) and "inefficient" (not linked to mineralization) fault systems, and then, used the former to generate the strengthened spatially weighted singularity models of geochemical indicator elements. The rationale is that faults with different orientations are the result of various geological processes, of which only some are linked to the ore-forming processes. We also integrated the strengthened models of geochemical anomalies by using random forest (RF) and principal component analysis (PCA) techniques to produce a stronger geochemical clue. Comparison of the integration results demonstrated that the former, a strengthened anisotropic geochemical singularity modeling technique proposed in this paper through the incorporation of efficient fault systems and RF method, is superior to the later, an existing anisotropic geochemical singularity modeling technique through the incorporation of all faults and PCA method. Data related to porphyry copper mineralization in Sarduiyeh district, Iran, was used to illustrate the procedure applied. [Display omitted] • Efficient fault systems are incorporated by distance-distribution analysis. • A strengthened anisotropic geochemical singularity is produced. • Spatially weighted singularity mapping technique was improved. • Random forest algorithm can robustly integrate anisotropic geochemical signatures. [ABSTRACT FROM AUTHOR]