Micrometer‐Scale Sediment Grain‐Size Prediction Using X‐Ray Fluorescence Geochemistry and Computed Tomography Density Scanning Data

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 modeling 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.