A performance study of local outlier detection methods for mineral exploration with geochemical compositional data
Puchhammer, Patricia; Kalubowila, Charmee; Braus, Lorena; Pospiech, Solveig; Sarala, Pertti; Filzmoser, Peter (2024-01-28)
Elsevier
28.01.2024
Patricia Puchhammer, Charmee Kalubowila, Lorena Braus, Solveig Pospiech, Pertti Sarala, Peter Filzmoser, A performance study of local outlier detection methods for mineral exploration with geochemical compositional data, Journal of Geochemical Exploration, Volume 258, 2024, 107392, ISSN 0375-6742, https://doi.org/10.1016/j.gexplo.2024.107392
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202402121707
https://urn.fi/URN:NBN:fi:oulu-202402121707
Tiivistelmä
Abstract
In exploration geochemistry, mineral deposits are typically characterised by an enrichment of the targeted elements, and thus their element composition differs from that of samples in a local neighbourhood. Local outlier detection methods aim at identifying local changes. In contrast to conventional outlier detection procedures, local outlier detection methods are multivariate methods for outlier identification that incorporate the spatial neighbourhood of the samples. It is essential that geochemical data are treated as compositional data, and the requirements for their treatment depend on the specific local outlier detection method. We demonstrate how prominent local outlier detection methods can be used for mineral exploration with geochemical data that vary in scale, in the sampling density, and in data quality. The methods are compared based on known mineralisations, and recommendations for their usefulness are provided.
In exploration geochemistry, mineral deposits are typically characterised by an enrichment of the targeted elements, and thus their element composition differs from that of samples in a local neighbourhood. Local outlier detection methods aim at identifying local changes. In contrast to conventional outlier detection procedures, local outlier detection methods are multivariate methods for outlier identification that incorporate the spatial neighbourhood of the samples. It is essential that geochemical data are treated as compositional data, and the requirements for their treatment depend on the specific local outlier detection method. We demonstrate how prominent local outlier detection methods can be used for mineral exploration with geochemical data that vary in scale, in the sampling density, and in data quality. The methods are compared based on known mineralisations, and recommendations for their usefulness are provided.
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