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An innovative geostatistical sediment trend analysis using geochemical data to highlight sediment sources and transport

Abstract : To study current marine sedimentary processes and depending on the field of application, two principal approaches exist. Thefirst is favoured by geochemists who increasingly use GIS (Geographic Information System) methodology combined withmultivariate analysis (most often Principal Component Analysis and Cluster Analysis) applied to a geochemistry dataset, toanalyse the spatial distribution of the chemical elements. The interpretation of results can remain complex, and the implemen-tation of chemical elements is limited. The second performed for sedimentary studies considers three granulometric parameters(mean, sorting and skewness) that are frequently used, which are processed by Grain Size Trend Analysis (GSTA) approach, toassess the vectors of sedimentary transport. In the current study, these two distinct approaches are combined to propose a newmethodology, integrating the geochemical data into a GSTA model, to assess concentration gradients. This adapted GSTAapproach, named “GSTA*”, has been tested on an existing dataset obtained from study in the eastern part of the Bay of Seine(Normandy, France) in an anthropogenic context (presence of a dumping site) to highlight the sediment dynamic processes. Theresults of the “classical” GSTA approach performed with granulometric parameters were compared with those from the inno-vative GSTA* approach, using initially one element, Total Organic Carbon (TOC), and subsequently, three combined chemicalelements, Total Organic Carbon, Calcium and Silicium (TOC, Ca and Si). The suitability of geochemical tracers in the study ofcoastal sedimentary dynamic and anthropogenic disturbance, according to concentration gradients is highlighted. The GSTA*approach confirmed previous observations by Baux et al. [1] observations and enabled the identification of new short-scaleprocesses and to determine sediment sources. It is a robust, non-subjective and informative methodology that can improve theinterpretation of sediment sources and transport.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-03548764
Contributor : Emmanuel Poizot Connect in order to contact the contributor
Submitted on : Monday, January 31, 2022 - 8:49:33 AM
Last modification on : Wednesday, March 9, 2022 - 2:14:04 PM

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Noémie Baux, Anne Murat, Emmanuel Poizot, Yann Méar, Gwendoline Gregoire, et al.. An innovative geostatistical sediment trend analysis using geochemical data to highlight sediment sources and transport. Computational Geosciences, Springer Verlag, 2022, ⟨10.1007/s10596-021-10123-5⟩. ⟨hal-03548764⟩

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