| dc.description.abstract | Marine mineral resources, such as seafloor massive sulfides (SMS), may address future needs in raw materials for nations aiming to sustain industrial and economic development. Past exploration of these seabed resources, which are rich in copper (Cu) and zinc (Zn), and to some degree, gold (Au) and silver (Ag), revealed numbers of depositional sites worldwide in the deep ocean, together with their possible economic significance. Yet, there is currently no active SMS mining. Conjointly, the lack of exploratory data makes it difficult to estimate the potential economic value of these mineral resources since many known, but also unknown, mineral occurrences have uncertain metal tonnage and grade. Addressing these uncertainties requires to fully explore and understand the seafloor, but this will not be easily overcome without exploration strategies and significant investments. Norway, especially, has within its sovereign territorial waters a large deep-sea area off its coast (above 200 m depth below sea surface) that may offer great potential for mineral resources to trigger future deep-sea mining activities. The nation's mineral wealth also extends on land, where known (and undiscovered) metal-rich deposits occur throughout the Norwegian mainland. As these onshore and offshore mineral resources are not easy to locate and evaluate, more rigorous analyses of geoscience data, with the help of adequate exploratory tools and concepts, are needed to mitigate both (1) the risks of exploring minerals and (2) uncertainties related to the determination of in-place ore metals.
This thesis undertakes the evaluations of the above-mentioned mineral resources within a restricted region of Norway's deep-sea and land territories, with the help of well-formulated investigative tools and techniques. The evaluation process, factored into multiple research papers, involves (1) an understanding and quantitative analysis of existing geological information, and (2) the application of probabilistic methods (e.g., characteristic analysis and radial basis function neural network or RBFNN) and analytical concepts (e.g., the three-part assessment) to address uncertainties inherent to both, the location and importance of mineral resources. Probabilistic results are presented as (1) prospectivity maps indicating, in the form of favorability rating or risk measure, explorative targets or mineral prospects, and (2) cumulative frequencies of ore metal content (i.e., Cu, Zn, Au and Ag) of undiscovered mineral occurrences. The investigated deep seafloors constitute 2922 km2 of tectono-volcanic terrains along a mid-ocean ridge, and cover about 75 exploration target zones that are yet to be visited. These volcanic terrains are estimated to contain about 11 undiscovered SMS deposits and on average 447,000 tonnes of metals. However, statistical (and geological) uncertainties are included in the resource quantification, notably those related to the estimation model and style used.
From regional to deposit scale, the risk of exploring some of the exploration target zones can be mitigated provided knowledge on (1) regional geology, sometimes indicative of existing ore-forming features (e.g., heat source, pathway, trap and reservoir) and/or a specific(s) geological setting favorable for mineralization, and (2) the type(s) of mineral resource observed (or assumed to exist) in the region investigated, which serves to estimate in-place mineral potential. To do so, a multi-scale quantitative risk analysis method, formerly established for oil and gas exploration, is adapted to deep-sea mineral investigation with the help of a newly formulated mineral system.
On land, data-driven prospectivity mapping of Cu-rich mineral resources has been conducted for about 44 thousand km2 of bedrock in the Finnmark region, northern Norway, with the help of RBFNN. The metallogeny of Finnmark is characterized by the presence of several metal resources, including Cu-rich occurrences, some of which are hosted in mafic host-rocks. Prospectivity results identified up to 885 km2 of mafic units favorable for Cu mineralization and possibly of interest for future detailed ground survey | nb_NO |
