Quantitative textural assessment for sample selection guided by drill core hyperspectral imagery

Introduction Mining operation success is reliant on making adequate decisions throughout the mining value chain, frommineral exploration to metal refinement and waste treatment. Current technological advances (Schodlok et al.,2016) facilitate an increasingly data-driven decision-making process (Suazo, Kracht, and Alruiz, 2010),potentially improving productivity and environmental outcomes (Cracknell et al., 2018; Merrill et al., 2017).Despite the extensive rock property data collected as part of a typical mining operation, metallurgical andenvironmental test data is relatively scarce. This is largely due to the high costs associated with samplecollection, preparation, transportation, and laboratory testing. Moreover, metallurgical sample selection protocolsare often selected based on grade and spatial distribution, resulting in units that are critical for generatinginformative and reliable metallurgical and environmental models being underrepresented or completely missed(Parbhakar-Fox and Lottermoser, 2015). In this study we propose a method for quantitatively assessing textural differences derived from hyperspectraldrill core mineral maps. Statistical analyses of mineral maps was used to define discrete spatial patterns, whichmay have implications for mineral processing (Lamberg et al., 2013). Methodology Mineral co-occurrence probability fields Based on the grey level co-occurrence matrix (Eichkitz, Amtmann, and Schreilechner, 2013) algorithm, a featureextraction method was developed to derive mineral co-occurrence probability fields (MCOPF), which computesthe angle-distance dependent probability that a pixel is one of the target minerals with respect to a referencepixel in a given mineral map (Fig. 1). MCOPF are calculated for every pair of minerals, accounting for not onlytextural relations within minerals themselves but also with other minerals. The input parameters for the calculation of the MCOPF are as follows: 1. Maximum distance: corresponds to the farthest pixel pairs considered, e.g., the drill core diameter 2. Radial step: the number of pixels between the reference pixel and its pair on each step until reaching themaximum distance 3. Angular step: increment of the angle on each step, which starts from 0 until reaching π radians (180°) Processing time increases with decreasing radial and angular step or increasing maximum distance, resulting inhigher resolution MCOPF. Minimum rotational difference The quantitative textural difference between two sections of drill core was calculated using a developed methodminimum rotational difference (MRD; Fig. 2). This method proceeds by finding the minimum value obtained forthe sum of the absolute difference between the MCOPF of two sections, while rotating one with respect to theother from 0 to π radians, that way the quantitative difference measurement becomes independent from theorientation of both: the drill hole perforation and the rock structures. This allows for the result to exclusivelyrepresent textural differences, separate from other spatial phenomena. Results Hyperspectral imagery of 700 m of drill core was sectioned into 30-cm pieces, each representing a samplecandidate and MCOPF were calculated. The minimum rotational difference was derived for all samplecandidates, allowing to distinguish individual units. The textural difference matrix in Figure 2 combines 100 sections (100 x 100) of drill core. Samples that areunique with respect to the common textures observed are represented with high values, whereas more commontextures are displayed with low values. This visualisation provides geological information that focuses attentionon drill core sections with unique properties. Discussion and Conclusions The proposed methodology successfully establishes a quantitative textural comparison among drill core samplecandidates, enabling the future development of automated sampling protocols. Metallurgical and environmentalknowledge may be incorporated through phenomenological understanding of the effect of specific minerals andtextural patterns into downstream processes, allowing to correlate test data with textural and compositional datafrom hyperspectral or other imaging characterization techniques. We hope this sampling protocol improves datarepresentation and relevance across the entire mining value chain from feasibility studies and plant design,through process control and optimization, to mine closure and waste management.