Interpreting the whole range of fluvial, wave, and tidal interactions recorded in shallow-marine, coastal successions can be challenging. The complexity arises because sedimentary structures produced by all three processes can be fully or partially preserved in the same stratal packages, and many of these structures are not diagnostic of a specific process. We therefore need an improved method of capturing the internal facies complexity that characterizes mixed-process coastal systems.
We propose a new methodology that assigns a percentage or probability to the likelihood for a bed or stratal unit to be formed by wave (w), tide (t), and fluvial (f) processes via a library of sedimentary structures and their non-unique generating processes. The library was generated through an intensive literature review of ancient, modern, and physical experiment works; the total frequency of association of each structure to each process (wave, tidal, fluvial) is used to calculate the percentage values. Each bed or bedset can be characterized by a specific structure or multiple structures (taking also into account lateral variations). Percentage values of wave/tide/fluvial processes of various structures can be averaged to create a final compound process probability for each bed. Vertical integration of process probability for individual beds in a rock succession creates probability graphs. This methodology has been tested on a 15-meters thick parasequence of the Jurassic Las Lajas Fm., Argentina, and on sedimentary logs of the Cretaceous lower Sego Sandstone, USA, and it is seen to efficiently couple classical facies analysis and surficial-process studies to quantify process variability in ancient systems. Additionally, we assessed the likelihood of association of sedimentary structures not only to hydrodynamic processes but also to depositional sub-environments, through a collection of published sedimentary logs (in modern and ancient deposits) from various basins worldwide. The methodology presented here better quantifies changing process dominance through time, improves the prediction of depositional environment evolution, and helps future studies that aim at a quantification of process variability.