Seismic fault detection in real data using Transfer Learning from a Convolutional Neural Network pre-trained with synthetic seismic data
requirements.txt
To install all requirements in the environment use: pip install -r requirements.txt
base_model/model.json
base_model/model.h5
dataset/fault
dataset/nonfault
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.
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.py : computes quality metrics (accuracy, sensitivity, specificity = recall, F1-score, ROC AUC and we added precision)