This paper is the result of mineral exploration engineering.
[Objective]The Central African copper-cobalt metallogenic belt which straddles the border area between the Democratic Republic of the Congo (DRC) and Zambia, is the world's most famous sediment-hosted stratabound copper-cobalt metallogenic belt. It is the world's third-largest copper and first-largest cobalt producer, but its mineralization pattern and potential are still unclear. [Methods]In this paper, the geological setting, tectonic evolution and mineralization, temporal-spatial distribution rules of deposits, deposit models of the Central African copper-cobalt metallogenic belt were studied. 32 copper (cobalt) prospective areas were delineated by applying the fuzzy weight of evidence method with stratigraphic, tectonic, geochemical, remote sensing alteration and other geological elements that are closely related to mineralization. The number of undiscovered deposits in each prospective area was calculated based on the posterior probability of mineralization at different probabilities. [Results]Monte Carlo simulation combined with the tonnage-grade model indicate that the average undiscovered copper resource in this area is estimated to be 288 million tons and the average cobalt resource is estimated to be 19.92 million tons, respectively. [Conclusions]The complex evolutionary history of the Central African copper and cobalt metallogenic belt has resulted in the superposition of multiple metallogenic interactions such as sedimentary mineralization, hydrothermal mineralization and epigenetic enrichment in this region, with copper (cobalt) mineralization running through the evolution of the belt and mineralization closely related to stratigraphy and tectonics. In particular, the Likasi-Kolwezi area of the Democratic Republic of the Congo may have good prospects for mineralization.
Highlights: copper-cobalt mineralization in the Central African copper-cobalt metallogenic belt is closely related to stratigraphy and tectonics; the copper- cobalt resource potential is quantified using the fuzzy weight of evidence method and Monte Carlo simulation.