Authors / CoAuthors
McKinley, J. | Hron, K. | Grunsky, E. | Reimann, C. | de Caritat, P.
Abstract
Single component maps are the most basic representation of spatial geochemical distributions used in numerous applications, from environmental baseline determination, through soil or water health guidelines analysis, to mineral exploration. Yet, the compositional nature of geochemical data imposes several limitations that make single component maps unsuitable for several of these tasks, especially when statistical methods are involved. Such problems relate to closure, and the inherently multivariate relative information conveyed by compositional data. Illustration examples of these problems are provided, such as for instance the tendency of all heavy metals to show lower values in soils with significant contributions of diluting elements (silt-sized glacial-related or wind-transported quartz, water in wet soils, etc.); or the contrary effect, apparent enrichment in many elements due to removal of potassium during weathering. In light of these considerations, the validity of these single component maps has been investigated in a recent workshop, as well as the reasonable alternatives that would honour the compositional character of geochemical concentrations. This paper presents the results obtained. The ubiquity of single component maps, however, means that they will continue to be used, but it is recommended that each be accompanied by a cautionary note on the lack of independence with respect to the other elements. Moreover, a number of alternative compositionally compliant representations based on the (log-)ratio approach is proposed to accompany the classical single component maps. The first recommended such method relies on knowledge-driven log-ratios, chosen to highlight certain geochemical relations or to filter known artefacts (e.g. dilution with SiO2 or volatiles). This is similar to the classical normalisation approach to a single element. The second approach uses the (called log-contrasts), that employ suitable statistical methods (such as classification techniques, regression analysis, principal component analysis, clustering of variables, etc.) to extract potentially interesting geochemical summaries. Examples using the TELLUS regional soil data provide log-contrast examples that identify several aspects of the geology and environment of Northern Ireland: a discrimination of granitic bedrock areas from Ordovician- Devonian sandstone units; maps highlighting peat coverage; maps delimiting the basaltic units, etc., which clearly show the potential of this approach for general application.
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nonGeographicDataset
eCat Id
83163
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Keywords
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- External Publication
- ( Theme )
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- marine
- Australian and New Zealand Standard Research Classification (ANZSRC)
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- Earth Sciences
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- Published_Internal
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2015-01-01T00:00:00
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geoscientificInformation
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[-90.0, 90.0, -180.0, 180.0]
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