FluvDepoSet: A dataset of synthetic 3D models of fluvial deposits

Guillaume Rongier, Luk Peeters

Sediments deposited by rivers constitute a key source for the water, energy, and raw materials that fuel our everyday lives. To find and manage these resources, we need to predict the spatial distribution of those fluvial deposits in the subsurface. This remains a major challenge due to the scarcity and poor quality of subsurface data, but also because traditional modeling approaches struggle to reproduce the continuity of those deposits while conditioning those data. To help tackle this challenge, we introduce FluvDepoSet, a dataset of synthetic 3D models of fluvial deposits. To obtain plausible models consistent with geological principles, the 20200 samples in FluvDepoSet were generated using a landscape evolution model, CHILD, which uses empirical and simplified physical laws to reproduce the processes driving landscape evolution. This includes river lateral migration, aggradation, and incision with overbank deposition to simulate the evolution of a meandering river over tens of thousands of years. While all the samples share the same basic setting, seven parameters controlling those processes are randomly drawn from uniform distributions for each sample. This results in the build up of different stratigraphies through time, with coarse sediments deposited in point bars along the river and fine sediments deposited in the floodplain. Those stratigraphies are then transferred to a regular structured grid, which is common to all samples and includes the fraction of coarse sediments and the deposition time. These properties are stored in HDF5 files, each file corresponding to one sample. Thanks to its large number of samples, FluvDepoSet is well-suited for sensitivity analyses to better understand the impact of fluvial deposits on applications - for instance through subsurface flow and transport simulations - and for machine learning to better support subsurface characterization and decision-making - for instance through the generation of more plausible conditional models of fluvial deposits.