Characterization of metal elements in deep-seabed polymetallic nodules: A multivariate statistical approach

Deep-seabed polymetallic nodules have been recognized as a potential solution to the depletion of many metals that are produced by terrestrial minerals. Mineral resources obtained by deep-seabed mining vehicles significantly affect the economic viability of underwater mining activities. Therefore, an accurate prediction of the harvested mineral resources is significantly important. Probabilistic approach-based prediction, which enhances the accuracy of the economic evaluation, requires a statistical model of the variability of each metal element in the harvested polymetallic nodules. However, the probability distribution of the metal elements in the polymetallic nodules has rarely been studied thus far. A multivariate joint probability distribution must be adopted because the variabilities of these metal elements is correlated with each other. However, multivariate statistical approaches have not been actively studied owing to their highly sophisticated theories. The objective of this study was to establish a systematic framework for modeling a multivariate joint probability distribution of correlated random variables. A case study was performed to characterize the metal elements of the polymetallic nodules using the proposed approach.