Wu, Yen-HsiYen-HsiWuJYH-JAAN HUANGDegenhart, GeraldGeraldDegenhartSu, Chih-ChiehChih-ChiehSuYu, Neng-TiNeng-TiYuYen, Jiun-YeeJiun-YeeYenChyi, Shyh-JengShyh-JengChyiChen, Jia-HongJia-HongChen2026-02-262026-02-262025-12-07https://www.scopus.com/pages/publications/105027405685https://scholars.lib.ntu.edu.tw/handle/123456789/736018Reconstructing past environments requires accurate interpretation of sedimentary variability in cores. Identifying features such as grain-size and compositional changes is essential for understanding depositional processes, but surface-based observations and point measurements may miss internal heterogeneity, affecting sampling representativeness. X-ray computed tomography (CT) provides a non-destructive approach to visualizing internal structures based on X-ray attenuation. While CT has been widely adopted in sedimentological studies, many applications remain qualitative and dependent on interpreter experience. This study introduces a framework integrating multiple CT-derived parameters, applied to sediment cores from Dapeng Bay, southwestern Taiwan. CT intensity values were classified into six attenuation-based regions of interest (ROIs), and parameters including volume fraction, statistical metrics (mean, coefficient of variation), and morphological descriptors (mean particle thickness of selected ROIs) were extracted on a slice-by-slice basis. These parameters were integrated to produce the high-resolution (~ 100 μm) vertical profiles that capture internal textural variability. The results demonstrate how CT-based metrics can support visual core description by quantitatively delineating sedimentary structures, improving sampling design, and enabling systematic comparison between cores. Together, these advances yield a novel, parameter-based quantitative framework that offers a reproducible, three-dimensionally informed workflow for the numerical reading of full-core CT volumes, remains transferable across settings through CT-parameter initialization and refinement, and provides a foundation for future data-driven classification.CT-derived parametersQuantitative core analysisSedimentary featuresX-ray computed tomography (CT)journal article10.1007/s44195-025-00117-6