Unleashing Source Scanning at the Speed of Python
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Welcome to the public repo for SSA2py.
SSA2py is an open-source python project that follows the Source-Scanning Algorithm (SSA). It provides interconnection with FDSN Compliant Web Services and it is adapted to run in GPU and CPU multiprocessing architectures. The aim of SSA2py is to provide rapid and accurate calculations of SSA method in near-realtime conditions.
The official documentation is hosted on Read the Docs.
The SSA2py Publication in SRL.
git clone https://github.com/ifountoul/SSA2py.gitcd SSA2pyconda env create -f environment.ymlconda activate SSA2PY- Create the events directory (Events Dir) and the traveltimes directory (Traveltimes/Save) declared in the configuration file.
python3 SSA2py.py --downloadpython3 SSA2py.py(Everything OK? Ready to go!)
- Install conda
- If you install SSA2py on a brand new system install the C and C++ compilers before installing Anaconda.
- Make sure that you have conda-forge in your channels (
conda config --show channels). You can add it by executingconda config --add channels conda-forge. - To use GPU install the cudatoolkit throught anaconda. Please check the CUDA and NVIDIA driver versions.
To learn more about using SSA2py, follow our guide here or see the examples.
Our software is developed on Ubuntu 22.04.2 LTS and tested on the following platforms:
- Linux (Ubuntu 22.04 LTS)
- Windows (Windows 11 Pro - WSL 2)
Note: SSA2py is not vigorously tested on macOS at the moment. Contributions and feedback related to macOS testing are welcome.
Have questions, comments or feedback? Start a discussion.
Found a bug? Please submit an issue.
Ioannis Fountoulakis
Christos Evangelidis
If you used SSA2py in your research, please use the following BibTeX entry:
@article{10.1785/0220230335,
author = {Fountoulakis, Ioannis and Evangelidis, Christos P.},
title = "{SSA2py: A High‐Performance Python Implementation of the Source‐Scanning Algorithm for Spatiotemporal Seismic Source Imaging}",
journal = {Seismological Research Letters},
year = {2024},
issn = {0895-0695},
doi = {10.1785/0220230335},
url = {https://doi.org/10.1785/0220230335}
}`
The research was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (SIREN, Project Number: 910).
Thanks to NOA for providing the infrastructure to develop this program!
Code released under the GNU GENERAL PUBLIC LICENSE Version 3