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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]

• 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]