ENHANCING RESOURCE MODELLING OF TAILINGS STORAGE FACILITIES THROUGH FACTORIAL GEOSTATISTICS
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Date
2024-04-18
Authors
Tileugabylov, Aidyn
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Mining and Geosciences
Abstract
In the modern economy, mining is essential for providing the chemicals needed to produce daily commodities. Due to technological constraints, significant minerals that were not effectively recovered during the initial extraction and processing steps are typically found in tailings in residual amounts. However, it is now essential to investigate the accumulated tailings deposits to reprocess them for an additional economic benefit due to the large decline in cut-off grade over the last few decades along with advancements in mineral processing techniques. However, traditional methods of mineral resource estimation struggle with the modelling of multiple cross-correlated variables that are present inside of tailings storage facilities. More precisely, traditional methods of cosimulation struggle with the inference of cross-covariance and solving the cokriging matrix system. This study proposes a factor-based algorithm to model cross-correlated geochemical variables based on the forward and backward MAF transformations that would eliminate two issues associated with traditional methods of modelling. The performance of the algorithm is applied to a real case study of Cu-Au tailings storage facility and is compared with the outcome of traditional cosimulation approach. Consequently, the proposed algorithm presents results similar to the ones obtained from the traditional cosimulation in terms of model validation and reproduction. Jackknife validation has been the primary model validation technique employed in this study along with the accuracy plot, and statistical reproduction. The mineral resource estimation of a tailings storage facility was conducted, and the results show promising metal quantity, mean grade and tonnage for most of the elements.
Description
Keywords
Type of access: Embargo, Geostatistics, mining tailings, resource estimation
Citation
Tileugabylov, A. (2024). Enhancing Resource Modelling of Tailings Storage Facilities through Factorial Geostatistics. Nazarbayev University School of Mining and Geosciences