Work

Soil Geochemical Characterization Associated with Fe–Cu Mineralization (Northeastern Aksaray, Türkiye) Using Geochemical Anomaly Classification and Multivariate Statistical Analyses

Year: 2026

Type: article

Abstract: This study investigated the soil geochemistry associated with magnetite-bearing Fe–Cu mineralization in northeastern Aksaray (Türkiye). Reservoir normalization, threshold-based geochemical anomaly classification, and multivariate statistical analyses were combined to evaluate the effects of parent lithology, weathering, and mineralization. Reservoir normalization analysis showed pronounced Cu enrichment, together with high geochemical variability, suggesting an influence beyond lithological inheritance alone. The depletion of relatively mobile components and the variable behavior of S further suggested modification of primary geochemical signatures during weathering. Threshold-based anomaly classification showed that positive Cu and Zn anomalies are closely associated with the known Fe–Cu mineralization zones, whereas Fe2O3, Co, and V anomalies primarily reflect basaltic parent rocks and the positive Zr anomaly reflects felsic lithological influence. The multivariate statistical analyses, including correlation analysis (CA), hierarchical cluster analysis (HCA), and principal component analysis (PCA), revealed coherent geochemical associations corresponding to basaltic parent rocks, felsic lithologies, and mineralization-related element assemblages. PCA resolved three principal components reflecting a mafic–felsic lithological contrast (PC1), the survey-scale compositional variability in the As–Pb–Rb–Zr association (PC2), and a Cu–Zn–S mineralization-related signature (PC3). Overall, the results indicate that soil geochemical patterns reflect the overlapping influences of parent lithology, weathering, and Fe–Cu mineralization, emphasizing the importance of local geological background and lithological variability in geochemical anomaly interpretation.

Source: Minerals

DOI: 10.3390/min16090929

Authors: Mustafa Haydar Terzi, Emre Turfan

Institution: Aksaray University

Language: English

Topic: Geochemistry and Geologic Mapping

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Aksaray Üniversitesi