crs3s/deepdenoiser

DeepDenoiser: Seismic Signal Denoising and Decomposition Using Deep Neural Networks

DeepDenoiser: Seismic Signal Denoising and Decomposition Using Deep Neural Networks

1. Install miniconda and requirements

• Download DeepDenoiser repository

git clone https://github.com/wayneweiqiang/DeeoDenoiser.git
cd DeepDenoiser

• Install to default environment

conda env update -f=env.yml -n base

• Install to "deepdenoiser" virtual envirionment

conda env create -f env.yml
conda activate deepdenoiser

2. Pre-trained model

Located in directory: model/190614-104802

3. Related papers

• Zhu, Weiqiang, S. Mostafa Mousavi, and Gregory C. Beroza. "Seismic Signal Denoising and Decomposition Using Deep Neural Networks." arXiv preprint arXiv:1811.02695 (2018).

4. Interactive example

See details in the notebook: example_interactive.ipynb

5. Batch prediction

See details in the notebook: example_batch_prediction.ipynb

6. 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 deepdenoiser/train.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=20

Please let us know of any bugs found in the code. Suggestions and collaborations are welcomed