140 likes | 266 Views
How much does water depth control facies distribution? A case study from the Florida Shelf. Gene Rankey. The Assumption. “ Topography is a primary driver of lateral facies change ” Tinker and Kerans (2002)
E N D
How much does water depth control facies distribution? A case study from the Florida Shelf Gene Rankey
The Assumption “Topography is a primary driver of lateral facies change” Tinker and Kerans (2002) “Here we simply accept the premise that facies and groups of lithofacies elements in fact do reflect changing water depths” (Diedrich and Wilkinson, 1999). Rankey Facies-Water Depth, CSL Annual Review
Purposes Explore relations between bottom types and the water depths at which they occur Test facies-scale ‘interpretability’ of ancient analogs Quantify possible uncertainties Rankey Facies-Water Depth, CSL Annual Review
Major Findings Benthic habitats & facies occur across a spectrum of water depths Not considerably distinct from a random distribution Variables other than water depth have an important - even dominant - influence on the distribution of facies on this shelf Interpretations and process-based forward models that simulate analogous systems should include the possibility of variable responses to water depth Rankey Facies-Water Depth, CSL Annual Review
Methods Regional map of benthic habitats Bathymetric maps Compare using GIS Analyze statistically Rankey Facies-Water Depth, CSL Annual Review
Location and Data Rankey Facies-Water Depth, CSL Annual Review
Habitats, Facies, and Water Depth Habitats Interpreted Facies Present across spectrum of WD Rankey Facies-Water Depth, CSL Annual Review
Water Depth 0-2 2-4 4-6 1.0 A Predictable, “Deterministic” Habitats B 0.6 0.4 0.33 0.34 0.33 Unpredictable, “NOT Deterministic” C “Given habitat A, how well can we predict WD?” Habitats, Facies, and Water Depth Rankey Facies-Water Depth, CSL Annual Review
Quantifying Dependence Shannon-Weaver diversity measures contingencies rij = probability of facies i occurring in water depth j H = 0 indicates a perfectly ordered system (facies i occurs only in water depth j). Rankey Facies-Water Depth, CSL Annual Review
Quantifying Dependence Shannon Evenness measures contingencies relative to random • Hmax = ‘random’ : any water depth is equally probable • E ranges from zero (H=Hmax) to one • E values near one - the system is not diverse (H=0), but one class dominates Rankey Facies-Water Depth, CSL Annual Review
Quantifying Dependence • E=1 means high predictability: • given a condition (facies/habitat), we can predict the result (e.g., water depth) with certainty • E is proportional to the percentage that uncertainty has been reduced from the maximum. Rankey Facies-Water Depth, CSL Annual Review
Habitats, Facies, and Water Depth “Given water depth A, how well could we predict habitats?” Habitats C A B 0.33 0.33 0-2 0.34 Unpredictable, “NOT Deterministic” Water Depth 0.6 2-4 0.4 4-6 1.0 Predictable, “Deterministic” Rankey Facies-Water Depth, CSL Annual Review
Facies Diversity and Water Depth • Deterministic component is highest in deeper water • Decreases with decreasing water depth • Shallow water more heterogeneous
Summary & Implications Benthic habitats occur across a spectrum of water depths Not significantly distinct from a random distribution Variables other than water depth have an important - even dominant - influence on the distribution of facies on this shelf - Energy, spatial context, geologic history Quantified uncertainty in interpretation of ancient analogs and in populating forward models …Next steps TBA tomorrow…. Rankey Facies-Water Depth, CSL Annual Review