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Code accompanying the paper "Complex-valued neural networks for machine learning on non-stationary physical data".

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JesperDramsch/Complex-CNN-Seismic

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Complex-CNN-Seismic

This repository reproduces "Complex-valued neural networks for machine learning on non-stationary physical data".

Data

Obtained from https://github.com/olivesgatech/facies_classification_benchmark via

# download the files: 
wget https://zenodo.org/record/3755060/files/data.zip
# check that the md5 checksum matches: 
openssl dgst -md5 data.zip # Make sure the result looks like this: MD5(data.zip)= bc5932279831a95c0b244fd765376d85, otherwise the downloaded data.zip is corrupted. 

Preparation for training via src/data_prep.py.

Training

Training done on GPU cluster using src/mass_train.py.

Prediction

Use trained models to generate predictions src/save_predictions.py.

Analysis

Numerical and qualitative analysis generated via src/explore.py.

Citation

Please cite the according paper as

@article{dramsch2020complex,
  title={Complex-valued neural networks for machine learning on non-stationary physical data},
  author={Dramsch, Jesper S{\"o}ren and L{\"u}thje, Mikael and Christensen, Anders Nymark},
  journal={Computers \& Geosciences},
  pages={104643},
  year={2020},
  publisher={Elsevier}
}

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Code accompanying the paper "Complex-valued neural networks for machine learning on non-stationary physical data".

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