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

Author:

Hernández Heber1ORCID,Alberdi Elisabete2ORCID,Goti Aitor3ORCID,Oyarbide-Zubillaga Aitor3ORCID

Affiliation:

1. Escuela de Ingeniería Civil en Minas, Facultad de Ingeniería, Universidad Santo Tomás, Santiago 8370003, Chile

2. Department of Applied Mathematics, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain

3. Department of Mechanics, Design and Organization, University of Deusto, 48007 Bilbao, Spain

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.

Funder

Basque Government

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference23 articles.

1. Rossi, M.E., and Deutsch, C.V. (2014). Mineral Resource Estimation, Springer.

2. Emery, X., and Ortiz, J. (2004). Defining Geological Units by Grade Domaining, Universidad de Chile. Technical report.

3. Edwards, A.C. (2014). Mineral Resource and OreReserve Estimation—The AusIMM Guide to Good Practice, The Australasian Institute of Mining and Metallurgy.

4. Journel, A.G., and Huijbregts, C.J. (1978). Mining Geostatistics, Academic Press.

5. Sterk, R., de Jong, K., Partington, G., Kerkvliet, S., and van de Ven, M. (2019, January 25–26). Domaining in Mineral Resource Estimation: A Stock-Take of 2019 Common Practice. Proceedings of the Mining Geology 2019 Conference, Perth, Australia.

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