CookStrait-DAS-Processing

DAS processing workflows and example datasets from the Cook Strait experiment, implemented using DASCore and documented through Jupyter notebooks.

Getting started

Clone the repository

Grab a local copy using HTTPS (works without SSH setup) or SSH if you use SSH keys with GitHub:

git clone https://github.com/Shihao-Yuan/CookStrait-DAS-Processing.git
# or
git clone git@github.com:Shihao-Yuan/CookStrait-DAS-Processing.git

cd CookStrait-DAS-Processing

Update the repository

Pull the latest changes regularly:

git pull

If you have local edits and git pull complains, commit your changes or stash them first.

Install Miniconda (Linux)

If you don't already have conda, install Miniconda:

mkdir -p ~/miniconda3
curl -fsSL -o ~/miniconda3/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -f ~/miniconda3/miniconda.sh
~/miniconda3/bin/conda init

Restart your shell, then verify:

conda --version

Install Miniconda (Windows)

If you don't already have conda, install Miniconda:

• Download and run the Miniconda installer from https://docs.conda.io/en/latest/miniconda.html
• Open Anaconda Prompt (miniconda3) (or Miniconda Prompt), then verify:

conda --version

Optional (PowerShell, via winget):

winget install -e --id Anaconda.Miniconda3

Prerequisites

• Conda (Anaconda/Miniconda)
• Python (recommended: 3.11)

Create a conda environment

Create and activate a clean environment:

conda create -n cookstrait-das python=3.11 -y
conda activate cookstrait-das

Install dependencies:

pip install --upgrade pip
pip install dascore matplotlib numpy jupyterlab ipykernel

Register the environment as a Jupyter kernel (so the notebooks run in the right env):

python -m ipykernel install --user --name cookstrait-das --display-name "cookstrait-das"

Run the notebooks

From the repository root:

jupyter lab

Then open notebooks in notebooks/ and select the kernel cookstrait-das.

Repository contents

Work in progress; the notebooks folder currently contains:

• notebooks/1-DAS_Preprocessing.ipynb: walks through the data-prep steps for the Cook Strait DAS experiment, including loading, calibration, and data quality checks.
• notebooks/2-DAS_Examples.ipynb: demonstrates a range of DAS signal types recorded during the experiment — cable geometry visualisation, GeoNet earthquake catalog queries and waterfall plots of matched events, and ambient noise examples (full-cable overview, traffic/anthropogenic noise with corresponding cable location map).

Notes

• Some notebooks may require you to update local file paths to point at your DAS data files.
• If you encounter a ModuleNotFoundError: No module named '...', install the missing package with pip install <package> (or conda install <package> if you are using a conda environment), then restart the kernel.

Running JupyterLab remotely via SSH tunnel

If the DAS data live on a remote server, you can run JupyterLab there and access it in your local browser through an SSH tunnel.

1. Start JupyterLab on the remote machine (no browser, fixed port):

jupyter lab --no-browser --port=8888

2. Open an SSH tunnel from your local machine (replace user and remote.host):

ssh -N -L 8888:localhost:8888 user@remote.host

The -N flag keeps the tunnel open without executing a remote command. If port 8888 is already in use on either machine, substitute a different port number (e.g. 8889) in both commands.

3. Open JupyterLab in your local browser:

http://localhost:8888

Copy the token or URL printed by JupyterLab on the remote machine if prompted for authentication.

Alternative: VS Code Remote SSH (recommended for regular use)

VS Code Remote SSH is easier and more comfortable than manually launching JupyterLab and setting up a browser tunnel every time. It handles the tunnel automatically and gives you a full IDE (file browser, terminal, debugger, and Jupyter notebook support) directly connected to the remote machine.

References

• DASCore documentation
• Chambers, D., Jin, G., Tourei, A., Issah, A.H.S., Lellouch, A., Martin, E.R., Zhu, D., Girard, A.J., Yuan, S., Cullison, T. and Snyder, T., 2024. Dascore: A python library for distributed fiber optic sensing. Seismica, 3(2), pp.10-26443.