GEOSTATISTICAL MODELING OF HETEROGENEOUS GEO-CLUSTERS IN A COPPER DEPOSIT INTEGRATED WITH MULTINOMIAL LOGISTIC REGRESSION: AN EXERCISE ON RESOURCE ESTIMATION

Simple item page
dc.contributor.authorMadani, Nasser
dc.contributor.authorMaleki, Mohammad
dc.contributor.authorSoltani-Mohammadi, Saeed
dc.date.accessioned2023-02-08T09:42:46Z
dc.date.available2023-02-08T09:42:46Z
dc.date.issued2022
dc.description.abstractResource estimation is the main and primary step in the development of a mining project. Principally, it is necessary to first identify the geological domains through boreholes, model them at unsampled locations, and then evaluate the grade(s) of interest inside each built domain. The traditional determination of these categorical domains over the sampling points is suboptimal as it considers mostly-one or two variables from core logging. This leads to the neglect of the influence of other significant variables. To circumvent the problem of estimation domain identification, spatially dependent clustering machine learning algorithms can be of great help in detecting such domains. However, one problem that may appear when using these techniques is that the resulting geo-domains (geo-clusters) obtained by the clustering technique might be heterogeneous and show a non-stationary property. The reason is that the aim of these spatially dependent techniques is to produce compact and spatially contiguous clusters, which are well suited to establishing non-stationary geo-domains. This makes the procedure of modelling challenging as it necessitates the use of advanced geostatistical techniques to propagate the heterogeneous geo-clusters at unsampled locations. An algorithm is presented in this study that employs a non-stationary sequential indicator simulation paradigm to model such complex variability of heterogeneous geo-clusters. Since the spatial trends of underlying geoclusters are required in this simulation method, in this study, we propose the use of multinomial logistic regression to infer these trends. The algorithm was tested using an actual case study from a porphyry copper deposit in Iran, where Cu, Mo, Au, Rock Quality Designation (RQD), mineralization zones, alteration types, and rock types were employed to identify and spatially model the heterogeneous geo-domains in the entire deposit. The results were compared with a conventional sequential indicator simulation where no trend was used. An examination of the resulting maps using several evaluation criteria including visual inspection of the realizations, probability maps, reproduction of proportion of each geo-cluster, connectivity measures, and trend analysis, showed that the findings of the proposed algorithm were superior in modelling heterogeneous geo-domains.en_US
dc.identifier.citationMadani, N., Maleki, M., & Soltani-Mohammadi, S. (2022). Geostatistical modeling of heterogeneous geo-clusters in a copper deposit integrated with multinomial logistic regression: An exercise on resource estimation. Ore Geology Reviews, 150, 105132. https://doi.org/10.1016/j.oregeorev.2022.105132en_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/6939
dc.language.isoenen_US
dc.publisherOre Geology Reviewsen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectType of access: Open Accessen_US
dc.subjectMultinomial logistic regressionen_US
dc.subjectNon-stationaryen_US
dc.subjectHeterogeneityen_US
dc.subjectSequential indicator simulationen_US
dc.subjectPorphyry copper depositen_US
dc.titleGEOSTATISTICAL MODELING OF HETEROGENEOUS GEO-CLUSTERS IN A COPPER DEPOSIT INTEGRATED WITH MULTINOMIAL LOGISTIC REGRESSION: AN EXERCISE ON RESOURCE ESTIMATIONen_US
dc.typeArticleen_US
workflow.import.sourcescience

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1-s2.0-S0169136822004401-main.pdf
Size:
14.26 MB
Format:
Adobe Portable Document Format
Description:
article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
6.28 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Articles