Enhancing Lithofacies Identification Through X-Ray Computed Tomography

Understanding lithofacies, defined by distinct sedimentary characteristics, is fundamental for deciphering depositional processes and reconstructing paleo-environments. While conventional methods like visual core description and grain size analysis are widely employed, they often face challenges, such as incomplete observation of internal structures, discrete sampling intervals, and potential for subjective interpretations, which may hinder the accuracy of lithofacies identification. X-ray Computed Tomography (CT), a non-destructive, high-resolution, and widely applicable imaging technique, addresses these limitations by enabling three-dimensional visualization and quantitative analysis of sediment cores. This study evaluates the feasibility of CT in assisting lithofacies identification by analyzing two sediment cores from the Dapeng Bay region in southwestern Taiwan. Beyond its qualitative capability to reveal imperceptible structures, CT-derived parameters were employed to quantify sedimentary features, such as grain size variability and internal structures, including ratios of different CT-intensity ranges (indicating materials of varying density or composition), mean CT intensity, coefficient of variation, and morphological characteristics. By combining these parameters, fourteen lithofacies were identified and further grouped into three sedimentary facies: lagoonal, channel, and tidal flat. This facilitates the reconstruction of a 500-year depositional history, highlighting the transition from lagoonal to tidal flat systems and elucidating depositional mechanisms influenced by high-energy events. This study establishes an objective and standardized framework that integrates qualitative CT imaging with quantitative parameterization to assist lithofacies identification. By reliably capturing subtle sedimentary variations and enabling consistent application across multiple sediment cores, this approach also supports systematic examination of spatial coverage and provides valuable insights into sedimentary environments across temporal and spatial scales.