Most mining companies have registered important amounts of drill core composite spectra using different acquisition equipment and by following diverse protocols. These companies have used classic spectrography based on the detection of absorption features to perform semi-quantitative mineralogy. This methodology requires ideal laboratory conditions in order to obtain normalized spectra to compare. However, the inherent variability of spectral features—due to environmental conditions and geological context, among others—is unavoidable and needs to be managed. This work presents a novel methodology for geometallurgical sample characterization consisting of a heterogeneous, multi-pixel processing pipeline which addresses the effects of ambient conditions and geological context variability to estimate critical geological and geometallurgical variables. It relies on the assumptions that the acquisition of hyperspectral images is an inherently stochastic process and that ore sample information is deployed in the whole spectrum. The proposed framework is basically composed of: (a) a new hyperspectral image segmentation algorithm, (b) a preserving-information dimensionality reduction scheme and (c) a stochastic hierarchical regression model. A set of experiments considering white reference spectral characterization and geometallurgical variable estimation is presented to show promising results for the proposed approach.

Froth flotation is a widely used method for the concentration of sulfide minerals. The structure of the superficial froth is an indicator of the performance of froth flotation alongside with the operational conditions in which this process is carried out. The aim of this study is to explore how the different operational conditions that can be managed in a flotation plant could directly influence the observable characteristics of the superficial froth. For this purpose, a froth image database was created using a special laboratory cell, designed to emulate the conditions seen in an industrial flotation cell. The database contains 2250 images, distributed in 45 categories; each category has a specific combination of testing conditions, and the main visual characteristics are observed. It also includes a methodology used to assess the quality of each corresponding category.

Spatial interpolation is a frequent issue in geosciences, where the estimation of values of a variable of interest at unsampled locations is sought from some spatial samples. The techniques most frequently employed to address this issue, such as those considered in geostatistics, require an effort of modelling and characterisation of statistics. This has limited a greater use of these techniques in disciplines that work with spatial or spatio-temporal information. This paper presents a novel spatial analysis technique, which is an extension of a previously proposed ensemble spatial interpolation model. It aims to provide a methodology that is as data-driven as possible, useful for a more general geoscientific (or expert) audience, and capable of providing quality estimates without the need for specific classical geostatistical expertise, such as variographic analysis. Additionally, a reinterpretation of the ensemble spatial interpolation algorithm is presented as a generative Bayesian model, which offers a simple and insightful reinterpretation of the concept of spatial interpolation in general. Finally, an extensive series of experiments, using both real and synthetic data, is presented to test the limits of the proposed model in very demanding scenarios, comparing it with a traditional geostatistical model. The results obtained verify a good performance in the ability to capture the relevant spatial aspects, even in challenging conditions such as non-stationary cases or when there are few samples to perform the inference. In turn, the level of errors in validation contexts is similar to those obtained with traditional geostatistics (Ordinary Kriging method), in synthetic contexts that are suitable for the use of geostatistical techniques. In future work, further research can be considered to improve local spatial characterisation, as well as to use the proposed technique in spatial 3D case studies.