/seisbench

SeisBench - A toolbox for machine learning in seismology

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0


PyPI - License GitHub Workflow Status Read the Docs PyPI Python 3.7 DOI

The Seismology Benchmark collection (SeisBench) is an open-source python toolbox for machine learning in seismology. It provides a unified API for accessing seismic datasets and both training and applying machine learning algorithms to seismic data. SeisBench has been built to reduce the overhead when applying or developing machine learning techniques for seismological tasks.

Getting started

SeisBench offers three core modules, data, models, and generate. data provides access to benchmark datasets and offers functionality for loading datasets. models offers a collection of machine learning models for seismology. You can easily create models, load pretrained models or train models on any dataset. generate contains tools for building data generation pipelines. They bridge the gap between data and models.

The easiest way of getting started is through our colab notebooks.

Examples
Dataset basics Open In Colab
Model API Open In Colab
Generator Pipelines Open In Colab
Applied picking Open In Colab
Using DeepDenoiser Open In Colab
Training PhaseNet (advanced) Open In Colab

Alternatively, you can clone the repository and run the same examples locally.

For more detailed information on Seisbench check out the SeisBench documentation.

Installation

SeisBench can be installed in two ways. In both cases, you might consider installing SeisBench in a virtual environment, for example using conda.

The recommended way is installation through pip. Simply run:

pip install seisbench

Alternatively, you can install the latest version from source. For this approach, clone the repository, switch to the repository root and run:

pip install .

which will install SeisBench in your current python environment.

Contributing

There are many ways to contribute to SeisBench and we are always looking forward to your contributions. Check out the contribution guidelines for details on how to contribute.

Known issues

  • Some institutions and internet providers are blocking access to our data and model repository, as it is running on a non-standard port (2443). This usually manifests in timeouts when trying to download data or model weights. To verify the issue, try accessing https://dcache-demo.desy.de:2443/ directly from the same machine. We are working on a permanent solution for the issue. In the meantime, if you are having trouble, try downloading through another network/VPN if possible. You can also contact your network administrator to allow access to port 2443 on our server. Otherwise, reach out to us, and we will work on finding a solution.

  • Pytorch currently does not support python 3.10, making it impossible to install seisbench in python 3.10. We recommend using python 3.7, 3.8 or 3.9, until pytorch is available for python 3.10.

References

Reference publications for SeisBench: