GAN_for_PorousMedia

This code is partially borrowed from the package named PorousMediaGAN, which is developed by Lukas Mosser, Olivier Dubrule, and Martin J. Blunt from the department of Earth Science and Engineering, Imperial College London. (https://github.com/LukasMosser/PorousMediaGan)

1. Preparation before training the generative adversarial neural network model

Install jupyter notebook

pip install jupyter notebook

Since the neural network is trained by pytorch, you should also install the torch packages hdf5 and dpnn.

pip install hdf5
pip install dpnn

Plus, h5py and tifffile are also needed.

pip install h5py
pip install tifffile

Then, clone this repository to your PC:

git clone https://github.com/LiXueying0309/GAN_for_PorousMedia
cd GAN_for_PorousMedia

2. How to train the model (in the folder GAN/code)

1. Run generator.py to generate training images from the original data sample

python generator.py --seed 42 --imageSize 64 --ngf 32 --ndf 16 --nz 512 --netG [path to generator checkpoint].pth --experiment sand --imsize 9 --cuda --ngpu 1

Please note that the sand here refers to the dataset name, which lies in the GAN/data folder in our package.

2. Run create_training_images.py to segment the image size into 646464 voxel

python create_training_images.py --image data/sand/sand.tif --name sand --edgelength 64 --stride 32 --target_dir sand_ti

3. Run main.py to train the GAN model

python main.py --dataset 3D --dataroot [path to training images] --imageSize 64 --batchSize 128 --ngf 64 --ndf 16 --nz 512 --niter 1000 --lr 1e-5 --workers 2 --ngpu 2 --cuda 

3. Data analysis (in the folder GAN/analysis)

Please note that our data analysis include morphological calculation and permeability calculation.

The morphological calculation is based on the MorpholibJ plugin from the ImageJ software (https://github.com/ijpb/MorphoLibJ).

The permeability calculation is based on the OpenFOAM software, version 8 (https://github.com/OpenFOAM/OpenFOAM-dev).

We conducted all the batch calculation via a single script with Linux command line. The example of a batch calculation for Minkowski functionals by ImageJ could be like this:

for i in `seq 0 100 %03g`;do imagej -b Macro_$i.ijm;done

The example of a batch calculation for permeability by OpenFOAM could be like this:

cd PermCal/batchCal/sand_org/sand_1319/a/
snappyHexMesh
cd ..
cd b
simpleKFoam

Please note that the solver, simpleKFoam, is used to calculate the permeability of the example case in the folder sand_org/sand_1391/. The file batcha.sh and batchFolder.sh could be applied for batch calculation in the Linux system if you want to do the OpenFOAM simulation. Plus, this work is based on Gentoo Prefix, and the code we provided might not be directly applied on other Linux systems.

4. Citation

@article{pmgan2017,
	title={Reconstruction of three-dimensional porous media using generative adversarial neural networks},
	author={Mosser, Lukas and Dubrule, Olivier and Blunt, Martin J.},
	journal={arXiv preprint arXiv:1704.03225},
	year={2017}
}