Application of Ranking Algorithms for Mapping of Multi-element Geochemical Anomalies at the 1:100,000 Scale Sechangi Sheet in the Southern Khorasan Province

The integration of exploration layers can be used to determine the multi-element geochemical anomalies that represent areas with mineralization potentiality. This is especially true for the combination of geochemical data. This paper aims to introduce ranking algorithms as an alternative to statistical data mining and multi-criteria decision-making methods for integrating exploration data. For this purpose, 362 stream sediment samples were used from Sechangi map sheet of South Khorasan Province. Each sample was analyzed for 23 elements. The implementation of six ranking algorithms including CA, MA, VSA, PA, LA, and CAA on the dataset shows that the obtained mineralization zones have overlapping and almost similar locations. These zones are located on tuff, andesitic, rhyolitic, and quaternary sedimentary rock units. Quantitative comparisons such as permutation and robust principal component analysis methods reveal the relative superiority of CA and PA algorithms. This is also the case with qualitative comparisons that deal with comparing the results of the ranking algorithms with geological mapping and mineral indices of the same study area. Combining six maps through weighted averaging suggested two zones. While the first zone has higher mineralization potentiality of about 24 km2, the second zone shows lower mineralization potentiality of approximately 311 km2 for the next exploration phase in the study area.