Probabilistic estimates of permissive areas for undiscovered seafloor massive sulfide deposits on an Arctic Mid-Ocean Ridge

Norway explores its seabed mining potential including exploration studies on seafloor massive sulfides (SMS) at the outermost parts of its continental shelf, the Mohn's Ridge. Owing to the significant development potential and the general lack of knowledge of the SMS deposits, the evaluation of exploration targets and resource abundance are more than ever necessary. Given current exploration status, this study proposes to (1) develop a mineral prospectivity map (MPM) indicating favorable geologic environments for the occurrence of SMS deposits, and (2) estimate the number of yet-to-be found hydrothermal mineral deposits within volcanically active areas. The first part of this research focuses on the development of the MPM using a knowledge-driven approach. For this purpose, we apply the quantitative prediction framework characteristic analysis developed for terrestrial mining exploration. In this methodology, data must be captured and compiled into a relevant spatial data set that will be transformed, combined and weighted for prediction modeling. The data consist of morpho-structures and terrain attributes obtained from an interpreted bathymetric map. A multivariate analysis on the integrated data signature allow to calculate favorability values that will be projected on an exploratory grid. Each grid cell is given a likelihood of mineralization to indicate where SMS deposits might be located. The second part of the paper estimates probabilistically how many SMS deposits remain to be found within neo-volcanic zones. These volcanic areas are geologically favorable to the occurrence of SMS deposits (permissive tracts) and provide the spatial basis for the probabilistic calculations. Estimates and associated confidence limits (10th and 90th percentiles) on the number of undiscovered deposits are calculated using regression equations. The resulting probability distribution function presents an expected amount of 11 SMS occurrences undiscovered.