Defining geologic domains using cluster analysis and indicator correlograms: a phosphate-titanium case study

ABSTRACT

One of the first decisions to be made when building a mineral resource model is the definition of geological/geostatistical domains. Cluster analysis is a set of techniques in machine learning that can be especially suited for this matter. In order to compare different approaches, two clustering algorithms were investigated in this study: k-means and the dual-space clustering algorithm. Choosing the most appropriate method and the number of clusters can be challenging and some metrics are needed to support these decisions, including the validation of the spatial distribution of the clusters, which is not always appropriately discussed in the literature. We introduce the use of correlograms of the indicators for that matter. Although clustering techniques can be robust for an application in resource modelling, expert knowledge is still necessary when applying cluster analysis to resource modeling, since final decisions should not be based solely on statistical indexes, but also on experience. In this paper, the proposed methodology was tested in a three-dimensional dataset related to a phosphate/titanium deposit.

KEYWORDS:

• Cluster analysis
• Estimation domains
• Stationarity
• Geostatistics
• Mining
• Geology
• Machine learning
• Data science

Acknowledgements

The authors would like to thank the Mineral Exploration and Mining Planning Laboratory (LPM) at the Federal University of Rio Grande do Sul (UFRGS), for providing the necessary conditions for the development of this work. Luiz Englert Foundation (FLE), the Coordination for the Improvement of Higher Education Personnel (Capes) and the National Council for Scientific and Technological Development (CNPq) are acknowledged for their financial support. We would like also to thank Dr. Ryan Martin for giving access to his code for the dual space clustering algorithm, and the very instructive conversations via e-mail.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Fundação Luiz Englert (FLE).