Metallogenic information extraction and quantitative prediction process of seafloor massive sulfide resources in the Southwest Indian Ocean

• Favorable information were extracted to establish a prospecting prediction model.
• We adopted the method of weights-of-evidence.
• Central region of the Southwest Indian Ridge has the highest posterior probability.
• The prediction process provides a quantitative way to assess potential survey targets.

Seafloor massive sulfide (SMS) deposits have significant development potential. In 2011, the China Ocean Mineral Resources Research and Development Association (COMRA) and International Seabed Authority (ISA) signed a contract to explore a 10 000 km2 region of the seafloor along the Southwest Indian Ridge (SWIR) containing hydrothermal sulfides. As regulated by the contract, China will have to relinquish 50% and 75% of the contract area within 8 and 10 years, respectively. However, exploration for seafloor hydrothermal sulfide deposits in China remains in the initial stage. Based on quantitative prediction theory and the status of exploration for SMS, we assemble factors related to the deposits in terms of topography, geology, geophysics, and several other related aspects along the SWIR and extract the most favorable information to establish a prospecting prediction model for SMS. By employing the weights-of-evidence method, we obtain a weighting for each prediction factor and thereby obtain a posterior probability map for the SWIR. The prediction result suggests that the central region of the SWIR has the highest posterior probability, i.e., it is the most prospective for the formation of hydrothermal vents and related SMS. Known hydrothermal areas such as Mt. Jourdanne, area A and 10°E–16°E are all located in the regions with high posterior probability values. The Chinese contract area (47°–51°E) has the highest posterior probability value and thus can be selected as a reserved region for additional exploration. By narrowing the exploration area and improving exploration accuracy, the predictions made will provide a focus for further exploration of seafloor hydrothermal sulfide resources.

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