Multi-scale Quantitative Risk Analysis of Seabed Minerals: Principles and Application to Seafloor Massive Sulfide Prospects

The potential for mining hydrothermal mineral deposits on the seafloor, such as seafloor massive sulfides, has become technically possible, and some companies (currently not many) are considering their exploration and development. Yet, no present methodology has been designed to quantify the ore potential and assess the risks relative to prospectivity at prospect and regional scales. Multi-scale exploration techniques, similar to those of the play analysis that are used in the oil and gas industry, can help to fulfill this task by identifying the characteristics of geologic environments indicative of ore-forming processes. Such characteristics can represent a combination of, e.g., heat source, pathway, trap and reservoir that all dictate how and where ore components are mobilized from source to deposition. In this study, the understanding of these key elements is developed as a mineral system, which serves as a guide for mapping the risk of the presence or absence of ore-forming processes within the region of interest (the permissive tract). The risk analysis is carried out using geoscience data, and it is paired with quantitative resource estimation analysis to estimate the in-place mineral potential. Resource estimates are simulated stochastically with the help of available data (bathymetric features in this study), conventional grade–tonnage models and Monte Carlo simulation techniques. In this paper, the workflow for a multi-scale quantitative risk analysis, from the definition to the evaluation of a permissive tract and related prospect(s), is described with the help of multi-beam data of a known hydrothermal vent site

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

Resource Assessment of Undiscovered Seafloor Massive Sulfide Deposits on an Arctic Mid-Ocean Ridge: Application of grade and tonnage models

Norway has started ongoing initiatives related to deep sea mining. These include the evaluation of mineral resources within its sovereign territorial waters on the deep ocean floor of the Arctic region, i.e. the Mohns Ridge (71–73°N). Among the variety of possible seabed resources, seafloor massive sulfides (SMS) attract particular interest. Owing to the significant development potential and the general lack of knowledge of the SMS deposits, the present study aims at assessing mineral resources (i.e., Cu, Zn, Au and Ag) from a number of undiscovered sulfide deposits with the help of a revised model for mafic-hosted volcanogenic massive sulfide (VMS) deposits that adequately corresponds to geological settings of slow-spreading ridges. Application of the model requires a priori knowledge of the seafloor terrain to depict favorable geologic environments for the occurrence of SMS deposits. In such context, estimates are conducted for the easily accessible volcanically active parts of the ridge where 11 undiscovered deposits are expected. In these areas, total metal endowments are calculated to be on average 447,000 tonnes using a Monte Carlo simulation that combines the probabilistic estimates of number of undiscovered deposits with the grade and tonnage models. Estimates of in-place metal resources are generated in cumulative distribution form to present expected amount of undiscovered metals at 90% confidence intervals

Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural Networks

In this paper, the radial basis function neural network (RBFNN) is used to generate a prospectivity map for undiscovered copper-rich (Cu) deposits in the Finnmark region, northern Norway. To generate the input data for RBFNN, geological and geophysical data, including up to 86 known mineral occurrences hosted in mafic host-rocks, were combined at different resolutions. Mineral occurrences were integrated into “deposit” and “non-deposit” training sets. Running RBFNN on different input vectors, with a k-fold cross-validation method, showed that increasing the number of iterations and radial basis functions resulted in: (1) a reduction of training mean squared error (MSE) down to 0.1, depending on the grid resolution, and (2) reaching correct classification rates of 0.9 and 0.6 for training and validation, respectively. The latter depends on: (1) the selection of “non-deposit” training data throughout the study area, (2) the scale at which data was acquired, and (3) the dissimilarity of input vectors. The “deposit” input data were correctly identified by the trained model (up to 83%) after proceeding to classification of non-training data. Up to 885 km2 of the Finnmark region studied is favorable for Cu mineralization based on the resulting mineral prospectivity map. The prospectivity map can be used as a reconnaissance guide for future detailed ground surveys.Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural NetworkspublishedVersion© The Authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)

Economic block model development for mining seafloor massive sulfides

To support open-pit studies related to seafloor massive sulfides mining projects, an economic block-model is required. A modular framework is proposed to produce economic block models accommodating various levels of data. The framework is illustrated on a site of interest located on the Arctic Mid-Ocean Ridge. Random sampling based on literature datasets is performed to assign grades, porosity and grain density to the model. Other required parameters are produced using relationships found in the literature. Revenues are estimated using literature values within a net smelter return methodology. Mining costs are determined using the cost of a mining system and the estimated time required for excavating the ore. The excavating time is assessed through the specific energy for the ore and the mining machines. The specific energy is calculated with a hyperbaric rock-cutting model. An economic block value of each mining block is then provided. The mining block database resulting from the study constitutes a valuable input into further studies on resource development. The framework has also been used to support a sensitivity study. The availability of the marine assets has been found as having the greatest influence on the economic value of the study case