LAUSR

Integration of exploration layers, especially the combination of geochemical data, can be used to determine the multi-element geochemical anomalies that represent areas with potential mineralization. This paper seeks 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, from Sechangi map sheet of South Khorasan Province were used. Each sample was analyzed for 23 elements. Implementation of the six ranking algorithms such as 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 (permutation and robust principal component analysis methods) and qualitative (compared to geological mapping and mineral indices of the study area) comparisons show the relative superiority of CA and PA algorithms. Combining six maps by weighted averaging has suggested two zones, one with higher mineralization potential of about 24 km2 area and the other with lower mineralization potential of approximately 311 km2 area for the next exploration phase in the study area.