Related paper:
Zhu, W., Mousavi, S. M., & Beroza, G. C. (2018). Seismic Signal Denoising and Decomposition Using Deep Neural Networks. arXiv preprint arXiv:1811.02695.
1. Install
The code is tested under python3.6
Using virtualenv
pip install virtualenv
virtualenv .venv
source .venv/bin/activate
pip install -r requirements.txtUsing ananconda
conda create --name venv python=3.6
conda activate venv
conda install tensorflow=1.10 matplotlib scipy pandas tqdm2. Demo data
Located in directory: Dataset/
3. Model
Located in directory: Model/190614-104802
4. Prediction
Data format:
Required a csv file and a directory of npz files.
The csv file contains one column: "fname"
The npz file contains one variable: "data"
The variable "data" is one component sample with a length of 3000 with sampling rate of 100 Hz.
python run.py --mode=pred --model_dir=Model/190614-104802 --data_dir=./Dataset/pred --data_list=./Dataset/pred.csv --output_dir=./output --plot_figure --save_result --batch_size=20Notes:
- For large dataset and GPUs, larger batch size can accelerate the prediction.
- Plotting figures is slow. Removing the argument of --plot_figure can speed the prediction
5. Train
Data format
Required: two csv files for signal and noise, corresponding directories of the npz files.
The csv file contains four columns: "fname", "itp", "channels"
The npz file contains four variable: "data", "itp", "channels"
The shape of "data" variables has a shape of 9001 x 3
The variables "itp" is the data points of first P arrival times.
Note: In the demo data, for simplicity we use the waveform before itp as noise samples, so the train_noise_list is same as train_signal_list here.
python run.py --mode=train --train_signal_dir=./Dataset/train --train_signal_list=./Dataset/train.csv --train_noise_dir=./Dataset/train --train_noise_list=./Dataset/train.csv --batch_size=20Please let us know of any bugs found in the code. Suggestions and collaborations are welcomed!