Experiments for "Learning by example: fast reliability-aware seismic imaging with normalizing flows", submitted to the SEG 2021 Annual Meeting.

To start running the examples, clone the repository:

$ git clone https://github.com/slimgroup/Software.SEG2021
$ cd Software.SEG2021/ReliabilityAwareImaging.jl/

Here, we heavily rely on InvertibleNetworks.jl, a recently-developed, memory-efficient framework for training invertible networks in Julia.

Before starting installing the required packages in Julia, make sure you have matplotlib and seaborn installed in your Python environment since we depend on PyPlot.jl and Seaborn.jl for creating figures.

Next, run the following commands in the command line to install the necessary libraries and setup the Julia project:

julia -e 'using Pkg; Pkg.add("DrWatson")'
julia -e 'using Pkg; Pkg.Registry.add(RegistrySpec(url = "https://github.com/slimgroup/SLIMregistryJL.git"))'
julia --project -e 'using Pkg; Pkg.instantiate()'

After the last line, the necessary dependencies will be installed. If you happen to have a CUDA-enabled GPU, the code will run on it. The training dataset will also download automatically into data/training-data/ directory upon running your first example describe below.

Run the script below for training the normalizing flow:

$ julia scripts/train_hint_imaging.jl

To perform conditional (posterior) sampling via the pretrained normalizing flow (obtained by running the script above), run:

$ julia scripts/test_hint_imaging.jl

If you find the code in this repository useful in your research, please cite:

@unpublished {siahkoohi2021SEGlbe,
    title = {Learning by example: fast reliability-aware seismic imaging with normalizing flows},
    year = {2021},
    month = {04},
    url = {https://arxiv.org/pdf/2104.06255.pdf},
    author = {Ali Siahkoohi and Felix J. Herrmann}
}

Ali Siahkoohi (alisk@gatech.edu)