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Abstract Stream sediments remain the preferred geochemical sampling media for regional mineral exploration programs in most parts of the world where there is a well-defined drainage system. In Au exploration the analysis of either Bulk Leach Extractable Gold (BLEG) or acid-extractable Au in the −80# (<180 μm) stream sediment fraction are the two most common approaches. Western Turkey contains a variety of mineral deposit types and geological settings. Using a geochemical database derived mainly from company exploration programs, a study is made on the efficiency of singularity mapping technique in detecting the known mineral deposits or prospects. The composite media of samples are representative of sampling locations but also are extremely diluted. Therefore, recognition and mapping of significant geochemical anomalies of composite media still remains a challenge. In this study, geochemical assay results were subjected to multifractal analyses to detect gold geochemical anomalies, which are related to gold mineralization. In this regard, a singularity mapping technique was applied to enhance the single element geochemical signatures of BLEG sand −80# stream sediment data. Since the singularity mapping technique does not define threshold values for delineation of anomalies, number-size (N-S) and concentration-area (C-A) fractal models were applied to delimit the geochemical anomalies as target areas for further exploration. A hundred percent of the known gold deposits and prospects discovered in the study area are delineated by BLEG Au stream sediment anomalies by N-S and C-A multifractal modeling. Moreover, 89% of known gold prospects of the study area are delineated within the recognized anomalous zones of Au in −80# stream sediment data. The comparison of the delineated geochemical anomalies of BLEG and −80# stream sediment with those of rock chip samples demonstrated the efficiency of the applied methodology. Graphical abstract Image 1 Highlights • Bulk leach extractable gold and −80# stream sediment sampling was carried out. • Concentration-area and number-size fractal modeling were applied. • Singularity mapping method was applied to delineate geochemical anomalies. • Singularity method applied to −80# stream sediments detected 90% of gold prospects. • Singularity method applied to BLEG also detected 100% of known gold prospects. [ABSTRACT FROM AUTHOR]

Recognition and mapping of the significant geochemical signatures of mineral deposits are the main objective of the exploration geochemical surveys. However, due to complexity of ore-forming processes, multiple significant geochemical signatures may reflect the deposit of the type sought. Therefore, combination of significant geochemical signatures to an enhanced one, which could be used for targeting undiscovered mineralization, is another challenging aspect of exploration geochemistry. This study tackles the foregoing challenges by (a) using the receiver operating characteristics (ROC) curves for discriminating significant and nonsignificant geochemical signatures and (b) proposing an ROC-based weighted aggregation matrix, called RWM, for synthesis of individual geochemical signatures to a single enhanced signature of the deposit-type sought. To demonstrate the effectiveness of the proposed methodology, stream sediment geochemical data from the Varzaghan District, northwestern Iran, were employed to target skarn copper deposits. The proposed methodology and fuzzy logic operators were applied to integrate individual geochemical signatures, the comparison of which revealed that the former was superior to the latter. The proposed RWM procedure not only effectively combines individual geochemical signatures, but also serves as a bivariate data-driven procedure for integration of layers of evidence for mineral prospectivity modeling. [ABSTRACT FROM AUTHOR]

In this study, stream sediment geochemical data have been subjected to robust principal components analysis (RPCA) and singularity mapping (SM) to enhance and map significant multivariate geochemical anomalies (i.e., mineralization-related) in Ahar area, NW Iran. The RPCA was applied to (a) account for the compositional nature of stream sediment geochemical data using suitable log-ratio transformation, (b) modulate the effect of outliers in component estimation and (c) derive a multivariate geochemical footprint of mineralization. The SM was applied to extract anomalous patterns of the multivariate geochemical footprint of mineralization. The exploration targets were then delineated using Student's t-statistics analysis. The correlations of mapped exploration targets with the known mineral occurrences and mineralization-related patterns were further evaluated using normalized density index and overall accuracy analyses. [ABSTRACT FROM AUTHOR]

Stream sediment geochemical data are usually subjected to methods of multivariate analysis (e.g. factor analysis) in order to extract an anomalous geochemical signature (factor) of the mineral deposit-type sought. A map of anomalous geochemical signature can be used as evidence, in combination with other layers of evidence, for mineral prospectivity mapping (MPM). Because factor analysis may yield more than one factor in a stream sediment dataset, it raises the challenge of how to recognize the factor that best indicates presence of the mineral deposit-type sought. In addition, MPM is faced with the challenge of how to assign weights to classes in a geochemical evidence map. Accordingly, a new approach is discussed in this paper for the extraction of significant anomalous geochemical signature of the mineral deposit-type sought and for assigning weights to anomaly classes in a geochemical evidence map. In this approach, we used a staged factor analysis and then applied a logistic function to transform factor scores representing an anomalous geochemical signature in order to derive a map of geochemical mineralisation prospectivity indices (GMPI) as a spatial evidence layer for MPM based on the theory of fuzzy sets and fuzzy logic. The GMPI is a fuzzy weight in the [0,1] range. We demonstrate the application of the GMPI for mapping prospectivity for Mississippi valley-type fluorite deposits in the Mazandaran province, north of Iran, which is a greenfield area. [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]

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]

The spectrum–area (S–A) fractal model is a powerful tool for decomposition of complex anomaly patterns of gridded geochemical data. Ordinary moving average interpolation techniques are commonly being used for gridding geochemical data; however, these methods suffer from two major drawbacks of (1) ignoring the locally high values and (2) smoothing the interpolated surface. Multifractal moving average interpolation methods have been developed to overcome the shortcomings of ordinary moving average methods. This study seeks to compare two sets of multifractal and ordinary gridded geochemical data using success rate curves and applies the S–A fractal model to decompose anomalous geochemical patterns. A set of stream sediment geochemical data in Ahar area, NW Iran, was used as a case study. Then, a mineralization-related multi-element geochemical signature was gridded by ordinary and multifractal approaches and considered for further analyses. The S–A fractal method was applied to decompose anomaly and background components of the resultant multi-element geochemical signature. Exploration targets were delimited and further evaluated using two bivariate statistical procedures of Student's t-value and normalized density index. The results revealed that (a) application of multifractal gridded data enhances the predicting ability of geochemical signatures, (b) application of S–A fractal model on multifractal gridded data allows for superior discrimination of geochemical anomalies, and (c) the multi-element geochemical anomalies in the Ahar area related to porphyry-Cu deposits were properly delineated through sequence application of multifractal interpolation and S–A fractal model. [ABSTRACT FROM AUTHOR]

Recognition of significant geochemical signatures and separation of geochemical anomalies from background are critical issues in interpretation of stream sediment data to define exploration targets. In this paper, we used staged factor analysis in conjunction with the concentration-number (C–N) fractal model to generate exploration targets for prospecting Cr and Fe mineralization in Balvard area, SE Iran. The results show coexistence of derived multi-element geochemical signatures of the deposit-type sought and ultramafic-mafic rocks in the NE and northern parts of the study area indicating significant chromite and iron ore prospects. In this regard, application of staged factor analysis and fractal modeling resulted in recognition of significant multi-element signatures that have a high spatial association with host lithological units of the deposit-type sought, and therefore, the generated targets are reliable for further prospecting of the deposit in the study area. [ABSTRACT FROM AUTHOR]

Dispersion pattern of geochemical elements in stream sediment data of a study area is affected by several factors, e.g., geological and geomorphological characteristics of the area. In this paper, we demonstrated recognition of efficient and inefficient mono-elemental geochemical signatures in a study area and exclusion of inefficient elements is worthwhile for increasing the probability of exploration success. For this, we adapted prediction-area (P-A) plot, normalized density and success-rate curve as tools that evaluate the ability of geochemical signatures in prediction of undiscovered mineral deposits and in delimiting exploration targets. After identification of efficient and inefficient elements, we combined efficient indicator elements to generate an effective prospectivity model. To illustrate the procedure we used a stream sediment data set for prospecting porphyry-Cu deposits in the Noghdouz area, Iran. [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]