Reproducible material for An Optimization-Based Inverse Solver to Accelerate the Marchenko Method - Wang N. and Alkhalifah T.
1produce_data
- Marchenko_imaging.ipynb: Calculating input and output data for NNs
--subroutine: --anglegather.py --imaging.py --marchenko.py
2preprocessing_file
- data.ipynb: Arrange for training, validation and test data
- create_mask.ipynb: Calculate time mask
3train_NN_pred
- 1networking_training.py : The first stage: forward networking training
- 2inversion.py: The second stage: inversion
- 3arrange_data.py: Arrange for the total data
- model.py: The architecture of the U-Net
- train.py: Training process and validation process
4cal_downgoing
- 1cal_for_clean_fminus_total.py: De-noising: apply band-pass filter and windowing operator
- 2cal_for_fplus.py: Calculate f+ by using f-
5arrange_data_for_Marchenko_imaging
- imaging.py: Carry out Marchenko imaging
data_for_this_test
- simple_M.npz: Google drive link:
https://drive.google.com/file/d/1oUvpIscPw79gwg6blVzMMv7P92tDLGwo/view?usp=drive_link
To ensure reproducibility of the results, we have provided a environment-latest.yml file. Ensure to have installed Anaconda
or Miniconda on your computer.
After that simply run:
./install_env-latest.sh
It will take some time, if at the end you see the word Done! on your terminal you are ready to go!