Non‐unique stratal geometries: implications for sequence stratigraphic interpretations

Abstract

There is now strong evidence that stratal geometries on basin margins are most likely a consequence of multiple controls, not just variations in accommodation. Consequently, correct sequence stratigraphic interpretation of stratal geometries requires an understanding of how multiple different controls may generate similar geometries. Using a simple numerical stratigraphic forward model, we explore the impact of time variable sediment supply and different sediment transport rates on stratal geometries. We demonstrate how four common types of stratal geometry can form by more than one set of controlling parameter values and are thus likely to be non‐unique, meaning that there may be several sets of controlling factors that can plausibly explain their formation. For example, a maximum transgressive surface can occur in the model due to an increase in rate of relative sea‐level rise during constant sediment supply, and due to a reduction in rate of sediment supply during a constant rate of relative sea‐level rise. Sequence boundaries, topset aggradation and shoreline trajectories are also examples of non‐unique stratal geometries. If the model simulations in this work are sufficiently realistic, then the modelled stratal geometries are important examples of non‐uniqueness, suggesting the need for a shift towards sequence stratigraphic methods based on constructing and evaluating multiple hypotheses and scenarios.

Exploring coral reef responses to millennial-scale climatic forcings: insights from the 1-D numerical tool pyReef-Core v1.0

“…many parameters) and multi-modal (i.e. non-unique solutions) forward model where numerous combinations of interacting parameters could potentially produce identical sequences (Burgess et al, 2006;Burgess and Prince, 2015). Given a specific reef core dataset and pyReef-Core, the task of finding the model parameter space that best describes the reef core data can be defined as the inverse modelling problem (Jessell, 2002).…”

“…However, SFM has become a popular heuristic tool to better understand and quantify parameters regulating peritidal carbonates (Burgess and Prince, 2015), the development of coral reef environments (Bosscher and Southam, 1992;Clavera-Gispert et al, 2017) as well as microbial (Parcell, 2003) and coral reef growth (Paulay and McEdward, 1990;Bosscher and Southam, 1992;Dalmasso et al, 2001). Early forward models were 1-D (Schwarzacher, 1966) or 2-D in formulation (Bosence and Waltham, 1990;Kendall et al, 1991), but improvements in computing led to the development of more complex, 3-D models (e.g.…”

Uncertainty management in stratigraphic well correlation and stratigraphic architectures: A training-based method

“…This conceptual knowledge is very useful to obtain consistent interpretations, but it may also, if inappropriate, be a source of errors in the correlations and in further analysis. In the forward sense, it has also been shown that several depositional scenarios can produce the same succession of stratigraphic units (Helland-Hansen and Gjelberg, 1994;Armitage et al, 2015;Burgess and Prince, 2015), which motivates the consideration of multiple correlation hypotheses. Even under the same set of hypotheses, correlation lines can be uncertain and are not easily formulated in a systematic and repeatable way when drawn manually by experts (Borgomano et al, 2008;Koehrer et al, 2011;Colombera et al, 2012;Lallier et al, 2016).…”

Exploring coral reef responses to millennial scale climatic forcings: insights from a 1-D numerical tool pyReef-Core v1.0

“…However, SFM has become a popular heuristic tool to better understand and quantify parameters regulating peritidal carbonates (Burgess and Prince, 2015), the development of factors affecting growth have been classified by Veron (2011) as latitude-correlated factors, and those that are regional or local in character. Latitude-correlated factors include sea surface temperatures (SSTs), solar radiation and water chemistry (Kleypas et al, 1999).…”

“…many parameters) and multi-modal (i.e. non-unique solutions) forward model where numerous combinations of interacting parameters could potentially produce identical sequences (Burgess et al, 2006;Burgess and Prince, 2015). Given a specific reef core dataset and pyReef-Core, the task of finding the model parameters space that best describes the reef core data can be defined as the inverse modelling problem (Jessell, 2002 (Andrieu et al, 2003).…”