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.

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.

Similarity analysis using neural networks has emerged as a powerful technique for understanding and categorizing complex patterns in various domains. By leveraging the latent representations learned by neural networks, data objects such as images can be compared effectively. This research explores the utilization of latent information generated by fully convolutional networks (FCNs) in similarity analysis, notably to estimate the visual resemblance of objects segmented in 2D pictures. To do this, the analytical scheme comprises two steps: (1) extracting and transforming feature patterns per 2D object from a trained FCN, and (2) identifying the most similar patterns through fuzzy inference. The step (2) can be further enhanced by incorporating a weighting scheme that considers the significance of latent variables in the analysis. The results provide valuable insights into the benefits and challenges of employing neural network-based similarity analysis for discerning data patterns effectively.