Incorporation of principal component analysis, geostatistical interpolation approaches and frequency-space-based models for portraying the Cu-Au geochemical prospects in the Feizabad district, NW Iran

Abstract

Delineation of mineralization-related geochemical anomalies of stream sediment data is an essential stage in regional geochemical exploration. In this study, principal component analysis (PCA) was applied to 12 selected elements to acquire a multi-element geochemical signature associated with Cu-Au mineralization in Feizabad district, NE Iran. The spatial distribution of enhanced multi-element geochemical signature of the second component (PC2) was modeled by different geostatistical procedures including variogram calculation, ordinary kriging (OK) and inverse distance weighting (IDW) interpolation techniques. Concentration-area (C-A) fractal and U-spatial statistics models were then applied to the continuous-value interpolated models for delineation of geochemical anomalies. Quantitative comparison of results based on the known mineral occurrences in the study area was carried out using normalized density index and success-rate curves. All generated models represent a high positive relation with known Cu (±Au) deposits in the study area, although, comparison of the results revealed that the OK-based U-spatial statistics model was superior to the rest of models. Besides, the low, moderate and high-intensity anomalies are spatially associated with geological-structural features in the study area.