Bayesian Data Fusion: a Reliable Approach for Descriptive Modeling of Ore Deposits

Recognition of ore deposit genesis is still a controversial challenge for economic geologists. Here, this task was addressed by the virtue of Bayesian data fusion (BDF), implementing available proofs: semi-schematic examples with two (Cu and Pb + Zn) and three (Cu, Pb + Zn, and Ag) evidences. The data, in the current paper being just concentrations of the indicated elements, was collected from the Angouran deposit in Iran at the prospecting and general exploration stages. BDF was used for discrimination between the three geneses of Massive Sulfide, Mississippi, and SEDEX types. A better genesis recognition with clear discrimination between the geneses was achieved by BDF, as compared to the earlier studies. The results obtained showed that uncertainties were reduced from 50% to less than 30%, and deposit recognition was greatly improved. Furthermore, we believe that using more properties can have a beneficial effect on the overall outcome. The comparison made between 2 and 3 properties showed that the amount of probable belonging values to any type of deposit was greater in 3 properties. It was also confirmed that using the completed information from the various stages of exploration progress can be amplified and be used for genesis recognition via BDF.