We developed ‘rivabar’, a graph-based Python tool that automatically extracts river centerlines and banklines from time-lapse satellite imagery, and use it to investigate how sediment load drives channel migration patterns and stratigraphic complexity in single-thread and multi-thread rivers.

A number of meandering rivers in the Arctic, especially in Siberia, have point bars with strikingly structured permafrost polygon patterns. We use a Unet-type convolutional neural network to map the polygons.

In this talk, I first want to revisit and visualize the basic ideas about how time is recorded - or not recorded - in sedimentary deposits and then show how chronostratigraphic diagrams can be used as key elements of an automated workflow for correlating geophysical well logs.

We use experimental data from the ‘Jurassic Tank‘ flume (St. Anthony Falls Laboratory) to create three-dimensional chronostratigraphic diagrams, compute stratigraphic completeness, and test several sequence stratigraphic concepts.

This presentation explores the dynamics of meandering systems in both fluvial and submarine environments, focusing on the predictability of channel migration and the resulting stratigraphic heterogeneities.

‘meanderpy’ is a Python implementation of a simple kinematic model of meandering, based on the Howard and Knutson (1984) model.