Mengyi Ren, Jianping Chen, Ke Shao, Miao Yu, Jie Fang. Quantitative prediction process and evaluation method for seafloor polymetallic sulfide resources[J]. Geoscience Frontiers, 2016, 7(2): 245-252. DOI: 10.1016/j.gsf.2015.04.004
Citation: Mengyi Ren, Jianping Chen, Ke Shao, Miao Yu, Jie Fang. Quantitative prediction process and evaluation method for seafloor polymetallic sulfide resources[J]. Geoscience Frontiers, 2016, 7(2): 245-252. DOI: 10.1016/j.gsf.2015.04.004

Quantitative prediction process and evaluation method for seafloor polymetallic sulfide resources

  • Seafloor polymetallic sulfide resources exhibit significant development potential. In 2011, China received the exploration rights for 10,000 km2 of a polymetallic sulfides area in the Southwest Indian Ocean; China will be permitted to retain only 25% of the area in 2021. However, an exploration of seafloor hydrothermal sulfide deposits in China remains in the initial stage. According to the quantitative prediction theory and the exploration status of seafloor sulfides, this paper systematically proposes a quantitative prediction evaluation process of oceanic polymetallic sulfide resources and divides it into three stages: prediction in a large area, prediction in the prospecting region, and the verification and evaluation of targets. The first two stages of the prediction process have been employed in seafloor sulfides prospecting of the Chinese contract area. The results of stage one suggest that the Chinese contract area is located in the high posterior probability area, which indicates good prospecting potential area in the Indian Ocean. In stage two, the Chinese contract area of 48°–52°E has the highest posterior probability value, which can be selected as the reserved region for additional exploration. In stage three, the method of numerical simulation is employed to reproduce the ore-forming process of sulfides to verify the accuracy of the reserved targets obtained from the three-stage prediction. By narrowing the exploration area and gradually improving the exploration accuracy, the prediction will provide a basis for the exploration and exploitation of seafloor polymetallic sulfide resources.
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