Spatial prediction of orebody characteristics can often be challenging given the commonly complex geological structure of mineral deposits. For example, a high nugget effect can strongly impact variogram modelling. Geological complexity can be caused by the presence of structural geological discontinuities combined with numerous lithotypes, which may lead to underperformance of grade estimation with traditional kriging. Deep learning algorithms can be a practical alternative in addressing these issues since, in the neural network, calculation of experimental variograms is not necessary and nonlinearity can be captured globally by learning the underlying interrelationships present in the dataset. Five different methods are used to estimate an unsampled 2D dataset. The methods include the machine learning techniques Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network; the conventional geostatistical methods Simple Kriging (SK) and Nearest Neighbourhood (NN); and a deep learning technique, Convolutional Neural Network (CNN). A comparison of geologic features such as discontinuities, faults, and domain boundaries present in the results from the different methods shows that the CNN technique leads in terms of capturing the inherent geological characteristics of given data and possesses high potential to outperform other techniques for various datasets. The CNN model learns from training images and captures important features of each training image based on thousands of calculations and analyses and has good ability to define the borders of domains and to construct its discontinuities.

As a versatile metal, nickel will experience increased demand in the coming years, with a specific focus on its importance in the battery industry and its role in achieving net-zero emissions. Recognizing the need to ensure sustainable resource management, this study analyses the flow dynamics of nickel’s supply and demand by employing a modelling approach. This is done with a focus on understanding how recycling can contribute to meeting the growing nickel demand. By considering the interaction between mining production, product applications, and recycling rates, this study contributes to a better understanding of the long-term prospects for meeting the nickel demand. It can assist policymakers, industry stakeholders, and investors in making informed decisions regarding resource management and developing sustainable practices in the nickel industry. The results revealed that mining would still play an important role in the supply of nickel for at least the next 40 years. Nickel mining and recycling practices are sufficient to meet future nickel demand if sufficient recycling practices are (rapidly) implemented. Modelling results show that nickel recycling will account for 90% of the total demand (primary nickel mining accounts for the remaining 10%) between the years 2062 and 2096.