AVALIAçãO DE TéCNICAS DE AGRUPAMENTO PARA DEFINIçãO DE DOMíNIOS ESTACIONáRIOS COM O AUXíLIO DE GEOESTATíSTICA

Modena, R. C. C.; Moreira, Gabriel de Castro; Marques, Diego Machado; Costa, João Felipe Coimbra Leite

doi:10.5151/2594-357x-33405

Cited by 3 publications

(3 citation statements)

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“…Although an important aspect in the clustering of spatial data, the formal validation of the geographic distribution of the clusters is not typically discussed in the literature and here we present a method for this verification by measuring the spatial continuity of the clusters (by their indicators correlograms). The semivariogram [2] is the standard instrument for that purpose, as applied by Modena et al [16]. However, it can be affected by short-distance noise.…”

Section: Methodsmentioning

confidence: 99%

A workflow for defining geological domains using machine learning and geostatistics

Moreira¹,

Modena²,

Costa³

et al. 2021

Tecnol. Metal. Mater. Min.

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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.

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Section: Methodsmentioning

confidence: 99%

A workflow for defining geological domains using machine learning and geostatistics

Moreira¹,

Modena²,

Costa³

et al. 2021

Tecnol. Metal. Mater. Min.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Indicator correlograms have also been used to verify the geographic connectivity of the clusters by measuring their spatial continuity. It should be noted that the semivariogram is the standard instrument to measure spatial continuity, as applied by Modena et al (2019), but it can be affected by short-distance noise. Therefore, the correlogram (Srivastava and Parker 1989) was chosen because it is standardized and more robust than the semivariogram.…”

Section: Correlograms Of the Indicatorsmentioning

confidence: 99%

“…This method was also applied in the current study, as will be discussed in the following sections. Another approach to validate clustering configurations is to use some form of measurement of the spatial continuity of the clusters, as proposed by Modena et al (2019), in which variograms of the indicators were used. In this paper, we used correlograms (Srivastava and Parker 1989) since they are more stable than variograms.…”

Section: Introductionmentioning

confidence: 99%

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

Moreira

Costa

Marques

2020

Applied Earth Science

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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 threedimensional dataset related to a phosphate/titanium deposit.

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Applying Clustering Techniques and Geostatistics to the Definition of Domains for Modelling

Moreira

Costa

Marques

2023

Springer Proceedings in Earth and Environmental Sciences

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Machine learning is a broad field of study that can be applied in many areas of science. In mining, it has already been used in many cases, for example, in the mineral sorting process, in resource modeling, and for the prediction of metallurgical variables. In this paper, we use for defining estimation domains, which is one of the first and most important steps to be taken in the entire modeling process. In unsupervised learning, cluster analysis can provide some interesting solutions for dealing with the stationarity in defining domains. However, choosing the most appropriate technique and validating the results can be challenging when performing cluster analysis because there are no predefined labels for reference. Several methods must be used simultaneously to make the conclusions more reliable. When applying cluster analysis to the modeling of mineral resources, geological information is crucial and must also be used to validate the results. Mining is a dynamic activity, and new information is constantly added to the database. Repeating the whole clustering process each time new samples are collected would be impractical, so we propose using supervised learning algorithms for the automatic classification of new samples. As an illustration, a dataset from a phosphate and titanium deposit is used to demonstrate the proposed workflow. Automating methods and procedures can significantly increase the reproducibility of the modeling process, an essential condition in evaluating mineral resources, especially for auditing purposes. However, although very effective in the decision-making process, the methods herein presented are not yet fully automated, requiring prior knowledge and good judgment.

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AVALIAçãO DE TéCNICAS DE AGRUPAMENTO PARA DEFINIçãO DE DOMíNIOS ESTACIONáRIOS COM O AUXíLIO DE GEOESTATíSTICA

Cited by 3 publications

References 0 publications

A workflow for defining geological domains using machine learning and geostatistics

A workflow for defining geological domains using machine learning and geostatistics

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

Applying Clustering Techniques and Geostatistics to the Definition of Domains for Modelling

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