FraCSPy

FraCSPy (Framework for Conventional microSeismic Processing) is an open-source Python library focused on providing conventional microseismic monitoring tools, particularly for the purpose of benchmarking newly proposed algorithms and workflows.

4
contributors

Cite this software

What FraCSPy can do for you

FracSPy offers a complete, user-friendly solution for microseismic processing. Designed for accessibility, FraCSPy integrates existing Python libraries into modular workflows, enabling users to manage the entire processing pipeline — from detection to event characterisation — with ease. By making established techniques broadly available, FraCSPy fosters innovation, education, and benchmarking within the microseismic research community.

Methods

Following the microseismic monitoring pipeline, key components have been implemented in FraCSPy, including microseismic data processing, detection, source localisation, and event characterisation. Additionally, it offers tools for forward modelling and visualisation of results and data.

Main features

  • Open-source: FraCSPy is a fully open-source software platform, hosted on GitHub and available via the pip package manager.
  • Comprehensive Coverage: Forward modelling, data processing, event detection, localisation and characterisation, as well as visualisation in one platform.
  • User-Friendly Software Design: Modular workflows integrate seamlessly with existing Python libraries, among others: 
    • NumPy
    • SciPy
    • pandas
    • PyLops
    • PyTorch
    • ObsPy
    • Numba
    • Matplotlib

Contributing

Join the FraCSPy community and contribute to the future of microseismic research. Explore our GitHub repository, share feedback, and collaborate on new features. Visit our website for tutorials and resources:
https://fracspy.github.io/FraCSPy

Citing

When using FraCSPy in scientific publications, please cite the following publication:

Birnie, C., Ravasi, M., Anikiev, D., & Saad, O. M. (2024). FraCSPy: An Open-Source Python Framework for Conventional Microseismic Processing (0.1.0). Zenodo. 10.5281/zenodo.13919709

Participating organisations

Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences
King Abdullah University of Science and Technology

Contributors

MR
Matteo Ravasi
King Abdullah University of Science and Technology
DA
Denis Anikiev
Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences
OS
Omar M. Saad
Arab Academy for Science Technology and Maritime Transport