ENHANCING RESOURCE MODELLING OF TAILINGS STORAGE FACILITIES THROUGH FACTORIAL GEOSTATISTICS

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dc.contributor.authorTileugabylov, Aidyn
dc.date.accessioned2024-06-27T07:09:06Z
dc.date.available2024-06-27T07:09:06Z
dc.date.issued2024-04-18
dc.description.abstractIn 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.en_US
dc.identifier.citationTileugabylov, A. (2024). Enhancing Resource Modelling of Tailings Storage Facilities through Factorial Geostatistics. Nazarbayev University School of Mining and Geosciencesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/8034
dc.language.isoenen_US
dc.publisherNazarbayev University School of Mining and Geosciencesen_US
dc.subjectType of access: Embargoen_US
dc.subjectGeostatisticsen_US
dc.subjectmining tailingsen_US
dc.subjectresource estimationen_US
dc.titleENHANCING RESOURCE MODELLING OF TAILINGS STORAGE FACILITIES THROUGH FACTORIAL GEOSTATISTICSen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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