SoftwareApplication
name: FluvGAN: Python scripts to test generating and inverting fluvial deposits using GANs
description: FluvGAN: Python scripts to test generating and inverting fluvial deposits using GANs
description: FluvGAN is a set of Python scripts to test generating and inverting fluvial deposits using generative adversarial networks (GANs) in the context of predicting the physical properties of the subsurface. It builds upon FluvDepoSet (https://doi.org/10.25919/4fyq-q291) - a dataset of 3D representations of fluvial deposits simulated with CHILD (https://github.com/childmodel/child), a landscape evolution model - for training, and upon voxgan (https://github.com/grongier/voxgan), a Python package making it easier to define and train GANs with PyTorch. Installation You can install all the packages needed to run all the scripts using conda and the file environment.yml included in this repository: conda env create -f environment.yml In addition, the file environment_cluster.yml contains the full list of packages with which the scripts were run on a cluster with a Linux system: conda env create -f environment_cluster.yml This environment misses some packages to visualize the results, because those steps were not done on the cluster, and it misses voxgan, because it was used during voxgan's development (now you can install it using pip, see https://github.com/grongier/voxgan). Usage FluvGAN is subdivided into three compressed folders: scripts.zip contains all the Python scripts and Jupyter notebooks to train and test the GANs and to generate some figures showcasing the results.data.zip contains the well & seismic data used for testing GAN inversion; the training and testing samples need to be downloaded from FluvDepoSet at https://doi.org/10.25919/4fyq-q291, which can be done using the script fluvgan_0_data.py.outputs.zip contains all the outputs from all the scripts, including the pretrained GAN models; the outputs' names follow the same convention as the scripts, and the scripts showcase how to reuse those outputs using Python.You can find more information in the file README.md. License Copyright notice: Technische Universiteit Delft hereby disclaims all copyright interest in…
author: https://orcid.org/0000-0002-5910-6868
author: https://orcid.org/0000-0002-1776-3173
keywords: Stratigraphic modeling
keywords: GAN
keywords: GAN inversion
keywords: deep generative modelling
keywords: python
keywords: PyTorch
keywords: deep learning
license:
  creative work: creativeWork
    name: MIT

https://orcid.org/0000-0002-5910-6868
type: Person
given name: Guillaume
family name: Rongier
name: Guillaume Rongier

https://orcid.org/0000-0002-1776-3173
type: Person
given name: Luk
family name: Peeters
name: Luk Peeters