A workflow for defining geological domains using machine learning and geostatistics
Gabriel de Castro Moreira, Rudi César Comiotto Modena, João Felipe Coimbra Leite Costa, Diego Machado Marques
Abstract
Determining geological domains to be modeled is one of the first steps in the mineral resource evaluation process. Prior knowledge regarding the geology of the deposit is fundamental but, in most cases, not enough for a reasonable definition of these domains. A careful statistical analysis of the available data (e.g. geochemical samples) is also of great importance. In order to avoid mixing different populations of data, samples with similar characteristics should be grouped together. In the context of supervised machine learning, cluster analysis can be especially suited for this matter and there are many different algorithms available in the literature. In this paper, two clustering techniques were investigated: the first is the k-means algorithm, one of the most widely used methods in machine learning, based on the iterative analysis of the statistical distribution, while the other one is based on spatial autocorrelation statistics, which takes into consideration the geographic distribution of samples. The choice of the most appropriate technique, as well as the number of domains can be challenging when performing cluster analysis, and the evaluation of an expert is still necessary, as the results are subjective.
Keywords
Referências
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Submetido em:
20/08/2020
Aceito em:
21/11/2021
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