No abstract

Underwater hyperspectral imaging is a relatively new method for characterizing seafloor composition. To date, it has been deployed from moving underwater vehicles, such as remotely operated vehicles and autonomous underwater vehicles. While moving vehicles allow relatively rapid surveying of several 10-1000 m 2 , they are subjected to short-term variations in vehicle attitude that often compromise image acquisition and quality. In this study, we tested a stationary platform that was landed on the seabed and used an underwater hyperspectral imager (UHI) on a vertical swinging bracket. The imaged seafloor areas have dimensions of 2.3 m × 1 m and are characterized by very stable UHI data of high spatial resolution. The study area was the TransAtlantic Geotraverse hydrothermal field at the Mid-Atlantic Ridge (26°N) in water depths of 3530-3660 m. UHI data were acquired a 12 stations on an active and an inactive hydrothermal sulfide mound. Based on supervised classification, 24 spectrally different seafloor materials were detected, including hydrothermal and non-hydrothermal materials, and benthic fauna. The results show that the UHI data are able to spectrally distinguish different types of surface materials and benthic fauna in hydrothermal areas, and may therefore represent a promising tool for high-resolution seafloor exploration in potential future deep-sea mining areas.

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 (10 th and 90 th 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.

No abstract

No abstract