Automated Classification of Estuarine Sub‐Depositional Environment Using Sediment Texture

Abstract

AbstractInterpretation of unconsolidated Quaternary sedimentary core is difficult if key diagnostic features are obscured or not present, therefore traditional facies analysis is challenging. However, sediment texture remains a universal attribute which can be used to interpret sedimentary core. Here we present an automated classification workflow which implements Extreme Gradient Boosting and Bayesian Optimization of hyperparameters to differentiate estuarine sub‐depositional environments. We use 19 textural attributes, measured using laser particle size analysis of surface sediment samples from the Ravenglass Estuary, Cumbria, northwest England, to make unbiased classification of sub‐depositional environment and estuarine zone. Two predictive models created using the automated workflow are presented and evaluated using a suite of evaluation metrics, confusion matrices, and spatial analysis to understand their geological implications. Model 1 keeps all sub‐depositional environments discrete and has an overall accuracy of 68.96%. Model 2 merges related sub‐depositional environments to form inner‐coarse and outer‐estuary zones and has an overall accuracy of 84.14%. Both models have been applied to textural data obtained at 5 cm intervals from a Holocene core drilled through a tidal bar in the Ravenglass estuarine succession, NW England, to classify palaeo sub‐depositional environment. Predictive output of the models suggests that the core consistently experienced inner estuary deposition; all inner estuary environments are represented in the core. The workflow presented here could be applied to datasets from other marginal marine depositional systems to enhance the interpretation of their subsurface deposits. Ultimately, detailed interpretations of ancient, buried deposits could be made using models derived from analogous modern systems.