APPLICATION OF THE PROCESS ICHNOLOGICAL FRAMEWORK TO ANCIENT DATASETS: AN EXAMPLE FROM THE LOWER CRETACEOUS BLUESKY AND MCMURRAY FORMATIONS, ALBERTA, CANADA

Marginal marine systems are subject to a diversity of physical processes and variations in chemical parameters that impart stresses on biological organisms, and result in a diversity of sedimentary features. While the product of certain processes may be identified through analysis of physical sedimentary structures, others may remain concealed in ancient datasets. The process ichnology framework allows for identification of these stresses, and when combined with process sedimentology leads to increased robustness in interpretations of palaeodepositional affinities.

The Lower Cretaceous Bluesky and McMurray formations of Alberta, Canada, represent continental through to marine siliciclastic deposits which are characterized by complex lateral and vertical stratal architecture. In order to identify the distribution and magnitude of physico-chemical stresses present in these strata and to refine the depositional interpretations of these deposits, the process ichnology framework was applied to a dataset of 19 wells (9 Bluesky wells and 10 McMurray wells). Within both datasets, detailed recording of trace fossil size, diversity, ichnogenera, ethology, bioturbation intensity (BI), and Size Diversity Index were recorded.

Comparison of these ichnological datasets elucidates the significant differences in spatial and temporal distribution and magnitude of physico-chemical stresses present during the evolution of each deposit. In particular, salinity fluctuation and rates of deposition, as inferred through SDI, ichnological diversity, and BI show significant differences between Bluesky and McMurray sediments. These studies highlight the utility (and importance) of combining ichnological and sedimentological characteristics to increase the robustness of palaeodepositional interpretations. Finally, these studies show the utility of applying the process ichnology framework to ancient datasets encompassing significant periods of geological time.