taner45/sfd-cnn-tl

Seismic fault detection in real data using Transfer Learning from a Convolutional Neural Network pre-trained with synthetic seismic data -- Authors: Augusto Cunha, Axelle Pochet, Helio Lopes, Marcelo Gattass

This page is available as an executable or viewable Jupyter Notebook:

This code allows to reproduce results of our article:

Seismic fault detection in real data using Transfer Learning from a Convolutional Neural Network pre-trained with synthetic seismic data https://doi.org/10.1016/j.cageo.2019.104344

Publications

If you find this work helpful in your research, please cite:

@article{cunha2020SFD,
title = "Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data",
journal = "Computers & Geosciences",
volume = "135",
pages = "104344",
year = "2020",
issn = "0098-3004",
doi = "https://doi.org/10.1016/j.cageo.2019.104344",
url = "http://www.sciencedirect.com/science/article/pii/S0098300418307040",
author = "Augusto Cunha and Axelle Pochet and Hélio Lopes and Marcelo Gattass",
keywords = "Transfer learning, Convolutional neural network, Seismic fault"
}

Notebook visualization

We provide two option to easily open and see our ipython notebooks:

• Visualize with NbViwer
• Visualize and modify with Binder

We strongly recommend using NbViwer to visualize our notebooks instead open in GitHub, because you will see the interactive plots.

Dependencies

environment.yml

To install all requirements in the environment use:

conda env create -f environment.yml

Sometimes is needed to create a kernel of the environment to jupyter notebooks:

python -m ipykernel install --user --name sfd --display-name "SFD-CNN-TL"

Our F3 Block pre-trained model can be found in:

base_model/model.json
base_model/model.h5

Our F3 Block slice manualy interpreted dataset can be found in:

dataset/fault
dataset/nonfault

Transfer learning methods:

ft.py : full fine tuning (FFT)
mlp.py : feature extractor with Multi Layer Perceptron (FE-MLP)
svm.py : feature extractor with Support Vector Machine (FE-SVM)

Default parameters are set to produce the results presented in the article.

Generated models can be saved by setting the boolean value save=true in functions create_model(). they will be save in the output/ directory.

Classification results:

classify.py : generates a classification file for models saved as .json and .h5
classify_with_SVM.py : generates a classification file for models saved as .pkl

Classification files is saved in directiry classification/output/ It contains patches coordinates associated to a class value (1 for fault, 0 otherwise)

We provide a region of a real section where a fault is clearly visible as demo in the classification/ directory. Other sections can be classified modifying the classify.py and classify_with_SVM.py files.

Metrics:

metrics.py : computes quality metrics (accuracy, sensitivity, specificity = recall, F1-score, ROC AUC and we added precision)

Interactive examples:

classifyAndViewF3.ipynb: Classify selected sections of F3 seismic data with all pretrained methods and show the results
classifyAndViewGSB.ipynb: Classify selected sections of GSB seismic data with all pretrained methods and show the results
TrainAndSave.ipynb: Train all methods with one interpreted section of a real data and save network weights