An integrated fuzzy–fractal geochemical index for gold exploration targeting in the Saqqez mineralization belt, Iran

Abstract In complex geochemical environments, separating anomalies from background values remains a major challenge. This study proposes an innovative integrative framework combining concentration-number (C–N) fractal modeling and fuzzy c-means (FCM) clustering to improve gold geochemical anomaly mapping in the Saqqez mineralization area, Sanandaj–Sirjan Zone. While fractal modeling is used for data-driven anomaly identification and background suppression, FCM clustering identifies multielement patterns related to mineralization. The main novelty of this research is the development of the Integrated Fractal–FCM Anomaly Index (IFFAI). This continuous, dimensionless index integrates standardized fractal-based anomaly intensity and FCM membership degrees, enabling the simultaneous quantification of anomaly strength and classification confidence. The results demonstrate that while standalone fractal modeling and FCM clustering fail to fully delineate all mineralized zones, the integrated IFFAI approach successfully identifies all known gold mineralization zones in the Saqqez region with a coherent and continuous pattern. Furthermore, by combining fractal anomaly classes with FCM-derived mineralization affinity factors, geochemical samples were systematically prioritized, showing strong spatial correlation with active exploration targets. Compared to standalone methods, the proposed framework provides significant improvements in spatial resolution, anomaly continuity, and background noise reduction. Overall, this method serves as an effective decision-support tool for geochemical anomaly mapping and exploration target prioritization.