Towards justifying unsupervised stationary decisions for geostatistical modeling: Ensemble spatial and multivariate clustering with geomodeling specific clustering metrics

Martin, Ryan; Boisvert, Jeff B.

doi:10.1016/j.cageo.2018.08.005

Cited by 9 publications

(16 citation statements)

References 19 publications

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“…Each metric is calculated independently for a given clustering configuration, but both metrics must be evaluated simultaneously to assess spatial clustering [10], which can be done by plotting spatial entropy versus WCSS.…”

Section: Methodsmentioning

confidence: 99%

“…Then the traditional k-means algorithm is applied to this new database, so that clusters that have both spatial and statistical coherence are defined. The algorithm is available on GitHub, hosted in the account mentioned in Martin and Boisvert [10].…”

Section: Introductionmentioning

confidence: 99%

“…II. The dual space metrics [10], which accounts for the geographic entropy and within clusters sum of squares;…”

Section: Introductionmentioning

confidence: 99%

“…The measure of the geographic distribution of a given configuration of clusters in geographic space, the "spatial In the last decades, clustering techniques have been applied to spatially-related samples (e.g. Scrucca [7], Romary et al [8], Fouedjio [9], Martin and Boisvert [10]) with the objective of developing algorithms that take into consideration not only the relationships of samples in the multivariate space, but also in the geographic space, generating clusters that are spatially contiguous and show distinct multivariate properties.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of geology and geostatistics, the geographic contiguity of the clusters and the multivariate delineation are both important factors for the resulting domains to be modeled [11]. According to Martin and Boisvert [10], two criteria should be considered when measuring the "goodness" of the resulting clusters: i. The contiguity of the domains in the Cartesian (geographic) space;…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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%

Section: Introductionmentioning

confidence: 99%

“…II. The dual space metrics [10], which accounts for the geographic entropy and within clusters sum of squares;…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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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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An optimized iterative clustering framework for recognizing speech

Palanivinayagam

Nagarajan

2020

Int J Speech Technol

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Towards justifying unsupervised stationary decisions for geostatistical modeling: Ensemble spatial and multivariate clustering with geomodeling specific clustering metrics

Cited by 9 publications

References 19 publications

A workflow for defining geological domains using machine learning and geostatistics

A workflow for defining geological domains using machine learning and geostatistics

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

An optimized iterative clustering framework for recognizing speech

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