Machine Learning for Characterization of Fluvial Architectural Elements
Development of machine learning algorithms to characterize architectural elements of modern fluvial depositional systems
Main objectives: This project will develop machine learning algorithms to characterize architectural elements of modern fluvial depositional systems across a range of different climates and tectonic settings. The aim is to develop a database that will provide input for numerical models to study system behavior through time and the likely validity of modern systems to understand the preserved rock record.
Funding: AkerBP ASA
Project Period: 2021 – 2023
Project Coordinator: Björn Nyberg
People involved at UiB: Rob Gawthorpe
Project Partners: AkerBP ASA