Micrometer-scale sediment grain-size prediction using X-Ray Fluorescence geochemistry and Computed Tomography density scanning data

Micrometer-scale sediment grain-size prediction using X-Ray Fluorescence geochemistry and Computed Tomography density scanning data | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 8 August 2025 V2 Latest version Share on Micrometer-scale sediment grain-size prediction using X-Ray Fluorescence geochemistry and Computed Tomography density scanning data Authors : Andreea Gabriela Auer 0009-0008-2842-9083 [email protected] , Willem Godert Maria van der Bilt , Sebastien Bertrand 0000-0003-0374-4040 , and Katarzyna Hasal Authors Info & Affiliations https://doi.org/10.22541/au.174733600.07776559/v2 Published Paleoceanography and Paleoclimatology Version of record Peer review timeline 231 views 186 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract As larger particles require more energy for transport, grain size is a key indicator for the magnitude of geohazards and other depositional processes. However, sample size requirements and laborious laboratory procedures limit our ability to extract this information at human-relevant (years to decades) timescales. The emergence of non-destructive and high-resolution core scanning techniques offer a solution to upscale measurements by mapping grain size-sensitive parameters at μm instead of cm scales. These include X-Ray Fluorescence (XRF) – tracking variations in elemental geochemistry that are often linked to mineral grain size, and Computed Tomography (CT) – capturing differences in density that control size sorting during deposition. Recent work demonstrates that these relations can be captured with linear regression fits, thus paving the way for predictive grain-size modelling approaches. Here, we expand on this work by assessing the potential of CT greyscale density data as a predictor. To do so, we developed a controlled experiment using synthetic sediment records (phantoms) – varying grain size, geochemistry, as well as two major sources of noise: organic and water content. Our results show that CT data can be used as a sole predictor, especially in cases where a homogenous mineralogy limits the use of XRF geochemistry-based approaches. Applications on natural sediment cores containing reworked volcaniclastics confirm these findings under real-world conditions, and highlight the complementarity of CT and XRF data. Finally, we present a code-free web-based workflow to make the presented grain-size prediction approach readily accessible to the wider geoscience community. Supplementary Material File (1031282_0_merged_1744783910.pdf) Download 1.61 MB File (auer et al. (2025) - supporting information.pdf) Download 1.88 MB Information & Authors Information Version history V1 Version 1 15 May 2025 V2 Version 2 08 August 2025 Peer review timeline Published Paleoceanography and Paleoclimatology Version of Record 19 Aug 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords computed tomography grain-size prediction high-resolution measurements partial least squares regression sediment provenance x-ray fluorescence Authors Affiliations Andreea Gabriela Auer 0009-0008-2842-9083 [email protected] Universitetet i Bergen View all articles by this author Willem Godert Maria van der Bilt University of Bergen View all articles by this author Sebastien Bertrand 0000-0003-0374-4040 GEOPS, Paris-Saclay University View all articles by this author Katarzyna Hasal Universitetet i Bergen View all articles by this author Funding Information Trond Mohn stiftelse TMS2021STG01 Willem van der Bilt Metrics & Citations Metrics Article Usage 231 views 186 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Andreea Gabriela Auer, Willem Godert Maria van der Bilt, Sebastien Bertrand, et al. Micrometer-scale sediment grain-size prediction using X-Ray Fluorescence geochemistry and Computed Tomography density scanning data. Authorea . 08 August 2025. DOI: https://doi.org/10.22541/au.174733600.07776559/v2 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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