This code is a Keras implementation of PhaseNet, dedicated to "Automatic arrival time picking for seismic inversion with unlabeled data". This version allows to deal with new datasets having different sizes and number of channels, especially, to implement transfer learning using the pretrained model from NCEDC data (Northern California Earthquake Data Center) and deal with small labeled datasets or unlabeled datasets using semi-supervised learning. Robust linear regression methods and SVR (Support Vector Regression) are used to correct labels in the pseudo-labeling (see correct_label directory), that helps significantly enhance the quality of pseudo labels.

The model stored in model/220217-182847 has been trained with 9,216 seismograms labeled using transfer and semi-supervised learning. The data in dataset/raw/data is an extraction from this dataset.

This Git repository contains the code related to the paper available at [https://doi.org/10.1007/s10712-023-09783-y].

0. Installing packages

Setting up a virtual environment using Anaconda:

conda create --name venv python=3.8
conda activate venv
conda install scikit-learn=0.24 tensorflow=2.5 pandas=1.3 matplotlib=3.4

1. Pre-processing

In the terminal command, go to the dataset directory:

cd dataset

• For generating labeled data (for train and test):

conda activate venv
python run.py --mode=mode --data_augmentation --data_dir=raw/data --label_list=raw/label_time.csv

where mode is either train (for generating train test), or test (for generating test set). The action --data_augmentation is used in order to increase the size of train or test set. We can further add the action --plot_figure and set a value for the parameter --plot_rate in order to verify the data preprocessing results.

• For generating unlabeled data (for prediction):

conda activate venv
python run.py --mode=pred --data_dir=raw/data

Notes:

• The csv file dataset/raw/label_time.csv should be contained 2 columns: itp, its. Its arrival times should be sorted in ascending alphabetical order of the file name in the dataset/raw/data directory.
• The data files in dataset/raw/data should be contained 2 columns (the one with the time and the other with the amplitude of the signal).
• If you want to preprocess a new raw data, you can modify some parameters of the Config() class in dataset/data_preprocessing.py.

2. Training

Now, go back to the main directory:

cd ..

Training from scratch:

• Training by splitting data into train and valid set with 0.8 of training and 0.2 of validation:

conda activate venv
python train_model.py --valid=0.2 --data_dir=dataset/train/data --data_list=dataset/train/fname.csv --batch_size=100 --epochs=50 --optimizer=adam

• Training on the whole dataset:

conda activate venv
python train_model.py --data_dir=dataset/train/data --data_list=dataset/train/fname.csv --batch_size=100 --epochs=50 --optimizer=adam

Transfer learning:

• Initializing weights from a pretrained model. By default, the process will load weights from all layers of pretrained model and select all layers of new model for fine-tuning:

conda activate venv
python train_model.py --valid=0.2 --data_dir=dataset/train/data --data_list=dataset/train/fname.csv --batch_size=100 --epochs=50 --model_dir=model/pretrained_from_NCEDC

• For more options of this process, you can see the function ind_layers() in train_model.py along with the summary file of pretrained model in order to select the index of layers for loading and freezing weights when using transfer learning. In this scenario, you can modify the TO IMPLEMENT part in this function and set the action --tune_transfer_learning as the command below:

conda activate venv
python train_model.py --valid=0.2 --data_dir=dataset/train/data --data_list=dataset/train/fname.csv --batch_size=100 --epochs=50 --model_dir=model/pretrained_from_NCEDC --tune_transfer_learning

Notes:

• While using transfer learning, make sure that you load weights into layers of the new model that have the same dimension with those of the pretrained model.
• For training with a new data, you can modify some parameters of the Config() class in data_reader.py.

3. Test

• Testing the performance of the model on the test set:

conda activate venv
python prediction_model.py --test --model_dir=model/220217-182847 --data_dir=dataset/test/data --data_list=dataset/test/fname.csv --batch_size=100 --save_result --plot_figure

4. Prediction

• Predicting arrival times for the prediction set using a trained model:

conda activate venv
python prediction_model.py --model_dir=model/220217-182847 --data_dir=dataset/pred/data --data_list=dataset/pred/fname.csv --batch_size=100 --save_result --plot_figure

5. Post-processing (correcting picks)

This part allows to improve the quality of pseudo labels for semi-supervised learning. In order to correct picks after predicting, go to the correct_label directory.