Welcome to SeisBench
SeisBench is an open-source python toolbox for machine learning in seismology. It provides a unified API for applying deep learning models to seismic waveforms, and for accessing and training machine learning algorithms on seismic datasets. SeisBench has been built to alleviate traditional bottlenecks when applying machine learning techniques to seismic data, in particular the steps of data preparation, collection and labelling. At the same time it aims to foster interoperability, interchangeability and comparability of different models and datasets.
To learn more about the dataset API, including the datasets currently integrated see Benchmark Datasets. If you would like to know more about how to apply, build, and train models, along with information on the machine learning models readily available through SeisBench, see SeisBench Model API. For an extensive reference of all features, check the code Documentation.
SeisBench is built to be extensible and modular, allowing for easy incorporation of new models and datasets. Users are explicitly encouraged to contribute, as any future iterations will be based of community feedback.
Citation…
Please cite any work in which SeisBench was used:
Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giuchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J., & Soto, H. (2022). SeisBench - A Toolbox for Machine Learning in Seismology. in Seismological Research Letters https://doi.org/10.1785/0220210324
The pre-print is also available for those who don’t have access: https://arxiv.org/abs/2111.00786,