Search

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]

• 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]