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

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Abstract

To study current marine sedimentary processes and depending on the field of application, two principal approaches exist. The first is favoured by geochemists who increasingly use GIS (Geographic Information System) methodology combined with multivariate analysis (most often Principal Component Analysis and Cluster Analysis) applied to a geochemistry dataset, to analyse the spatial distribution of the chemical elements. The interpretation of results can remain complex, and the implementation 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, to assess the vectors of sedimentary transport. In the current study, these two distinct approaches are combined to propose a new methodology, integrating the geochemical data into a GSTA model, to assess concentration gradients. This adapted GSTA approach, 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. The results of the “classical” GSTA approach performed with granulometric parameters were compared with those from the innovative GSTA* approach, using initially one element, Total Organic Carbon (TOC), and subsequently, three combined chemical elements, Total Organic Carbon, Calcium and Silicium (TOC, Ca and Si). The suitability of geochemical tracers in the study of coastal 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-scale processes and to determine sediment sources. It is a robust, non-subjective and informative methodology that can improve the interpretation of sediment sources and transport.

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Acknowledgments

The authors are grateful to research RIN (“Réseaux d’Intérêts Normands”) for their funding of the SELINe project. We wish to thank to the “Grand Port Maritime du Havre” and the crews of the two ships, “Le Marais” and the “Côtes de la Manche”. We thank all the staffs for assistance in technical operations. The authors would like to express their gratitude to Dr. Colin Burton for his detailed correction of the English used in the manuscript. At least, we greatly thank the anonymous reviewer whose comments and suggestions helped to improve and clarify this manuscript.

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Authors and Affiliations

  1. Normandie univ., UNICAEN, Laboratoire des Sciences Appliquées de Cherbourg, EA 4253, 50100, Cherbourg, France

    Noémie Baux, Anne Murat, Emmanuel Poizot, Yann Méar & Gwendoline Gregoire

  2. Conservatoire National des Arts et Métiers, INTECHMER, 50100, Cherbourg, France

    Noémie Baux, Anne Murat, Emmanuel Poizot, Yann Méar & Gwendoline Gregoire

  3. Normandie Univ, UNICAEN, UNIROUEN, CNRS, Morphodynamique Continentale et Côtière (M2C), UMR CNRS 6143, 14000, Caen, France

    Sandric Lesourd & Jean-Claude Dauvin

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  1. Noémie Baux
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  2. Anne Murat
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Highlights

• Aim: Adaptation of existing GSTA approach with geochemical parameters.

• Study of sedimentary processes, sources and transport.

• New methodology enabled the identification of new short-scale processes

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Baux, N., Murat, A., Poizot, E. et al. An innovative geostatistical sediment trend analysis using geochemical data to highlight sediment sources and transport. Comput Geosci 26, 263–278 (2022). https://doi.org/10.1007/s10596-021-10123-5

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