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.
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 |  |
| Model API | |
| Generator Pipelines | |
| Training PhaseNet (advanced) | |
Alternatively, you can clone the repository and run the same examples locally.
For more detailed information on Seisbench check out the SeisBench documentation.
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.
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.
Reference publications for SeisBench:
-
SeisBench - A Toolbox for Machine Learning in Seismology
Reference publication for software (pre-print).
-
Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers
Example of in-depth bencharking study of deep learning-based picking routines using the SeisBench framework (pre-print).