Application of the k-Prototype Clustering Approach for the Definition of Geostatistical Estimation Domains

Heber Hernández, Elisabete Alberdi, Aitor Goti () and Aitor Oyarbide-Zubillaga
Additional contact information
Heber Hernández: Escuela de Ingeniería Civil en Minas, Facultad de Ingeniería, Universidad Santo Tomás, Santiago 8370003, Chile
Elisabete Alberdi: Department of Applied Mathematics, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain
Aitor Goti: Department of Mechanics, Design and Organization, University of Deusto, 48007 Bilbao, Spain
Aitor Oyarbide-Zubillaga: Department of Mechanics, Design and Organization, University of Deusto, 48007 Bilbao, Spain

Mathematics, 2023, vol. 11, issue 3, 1-17

Abstract: The definition of geostatistical domains is a stage in the estimation of mineral resources, in which a sample resulting from a mining exploration process is divided into zones that show homogeneity or minimal variation in the main element of interest or mineral grade, having geological and spatial meaning. Its importance lies in the fact that the quality of the estimation techniques, and therefore, the correct quantification of the mineral resource, will improve in geostatistically stationary areas. The present study seeks to define geostatistical domains of estimation for a mineral grade, using a non-traditional approach based on the k-prototype clustering algorithm. This algorithm is based on the k-means paradigm of unsupervised machine learning, but it is exempt from the one-time restriction on numeric data. The latter is especially convenient, as it allows the incorporation of categorical variables such as geological attributes in the grouping. The case study corresponds to a hydrothermal gold deposit of high sulfidation, located in the southern zone of Peru, where estimation domains are defined from a historical record of data recovered from 131 diamond drill holes and 37 trenches. The characteristics directly involved were the gold grade (Au), silver grade (Ag), type of hydrothermal alteration, and type of mineralization. The results obtained showed that clustering with k-prototypes is an efficient approach and can be used as an alternative or complement to the traditional methodology.

Keywords: clustering algorithms; homogeneity; stationarity; unsupervised machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/3/740/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/3/740/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:3:p:740-:d:1054668

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().