Quantifying sedimentary deposits is crucial to fully test generic trends cited within facies models. To date, few studies have quantified downstream trends alongside vertical and lateral variations within distributive fluvial systems (DFS), with most studies reporting qualitative trends. This study reports on the generation of a quantitative dataset on the Huesca DFS, Ebro Basin, Spain, in which downstream, vertical and lateral trends in channel characteristics are analyzed using a fusion of field data and virtual outcrop model derived data (VOM). Vertical trend analysis reveals that the exposed portion of the Huesca DFS does not show any systematic changes through time, which suggests autogenic-driven local variability. Proximal-to-distal trends from field data display a downstream decrease in average channel body thicknesses (13.1–0.7 m), channel deposit percentage (70–4%), and average storey thicknesses (5.2–0.7 m) and confirm trends observed on other DFS. The VOM dataset shows a similar downstream trend in all characteristics. The range in values are, however, larger due to the increase in amount of data that can be collected, and trends are thus less clear. This study therefore highlights that standard field techniques do not capture the variability that can be present in outcrops. Channel percentage was found to be most variable (37% variation) in the medial setting, whereas channel body thickness is most variable (∼15 m range) in the proximal setting. Storey thickness varied in both the proximal and medial settings (range of 9 and 11 m for field and VOM data respectively) becoming more consistent downstream. Downstream shifts in architecture are also noted from massive, highly amalgamated channel-body sandstones in proximal regions to isolated or offset-stacked channel-bodies dominating the distal region. Trends are explained by spatial variability in DFS processes and preservation potential. The overlap present indicates that no single value is representative of position within a DFS, which has important implications for interpreting the location that a data point sits within a DFS when using limited (i.e., single log) datasets. These comparative results contribute to improving the accuracy of system-scale downstream predictions for channel characteristic variability within subsurface deposits.
Author Ben Martin thanks the University of Glasgow for providing funding for this project through the ‘Stressed Environments’ scholarship fund. The SAFARI consortium (https://safaridb.com/home) are thanked for providing virtual outcrop models that have been analyzed within this paper. Two anonymous reviewers are thanked for their thorough and constructive comments on this paper.
- distributive fluvial system