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Application of MLReal for low-frequency extrapolation

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MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning

This repository accompanies publication [Alkhalifah, Wang and Ovcharenko, 2021] and provides forward / backword methods for MLReal transformation in application to the task of low-frequency data extrapolation.

workflow

How to start

Download the data and run notebooks. All notebooks are set for inference / view by default. Meaning that these will not run any heavy calculations unless reset otherwise. Instead, these will use the pre-trained weights and data to partially reproduce results from the paper.

Filename Description
ex0_create_training_dataset.ipynb Generate training dataset of synthetic waveforms
ex1_multi.ipynb Train Multi-column network to predict low-frequency data
ex2_mlreal_forw.ipynb Same as ex1 but with MLReal applied to inputs only
ex3_mlreal_forw_back.ipynb Same as ex1 but with MLReal applied to both inputs and targets
shared_data_loading.ipynb Shared data loading snippets
assets Folder with images for README
pretrained_files Download and place pre-trained data here
utils Code components

Prerequsites and dependencies

  • Python 3.8
  • PyTorch 1.8
  • CUDA 11.0

For the rest of Python dependencies check requirements.txt.

Installation

git clone https://github.com/swag-kaust/mlreal.git
cd mlreal/
python -m venv env
source env/bin/activate
pip install -r requirements.txt

jupyter notebook .

Downloads

Unzip files by running tar -xvf arhive.tar.gz and place complete folders according to the table

Link Size Destination Description
data.tar.gz ~ 13 Gb ./pretrained_files/data/* training and validation datasets
trained_nets.tar.gz ~ 300 Mb ./pretrained_files/trained_nets/* Pre-trained network weights

Acknowledgments

Our implementation is heavily influenced and contains code blocks from Inpainting GMCNN.

Citation

@misc{alkhalifah2021mlreal,
      title={MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning}, 
      author={Tariq Alkhalifah and Hanchen Wang and Oleg Ovcharenko},
      year={2021},
      eprint={2109.05294},
      archivePrefix={arXiv},
      primaryClass={physics.geo-ph}

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