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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
Applied picking
Using DeepDenoiser
Depth phases and earthquake depth
Training PhaseNet (advanced)
Creating a dataset (advanced)
Building an event catalog (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

SeisBench is build on pytorch. As of pytorch 1.13.0, pytorch is by default shipped with CUDA dependencies which increases the size of the installation considerably. If you want to install a pure CPU version, the easiest workaround for now is to use:

pip install torch==1.12.1 seisbench

We are working on a more permanent solution that allows to use the latest pytorch version in a pure CPU context.

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.

• We've recently changed the URL of the SeisBench repository. To use the new URL update to SeisBench 0.4.1. It this is not possible, you can use the following commands within your runtime to update the URL manually:

  import seisbench
  from urllib.parse import urljoin
  
  seisbench.remote_root = "https://hifis-storage.desy.de:2880/Helmholtz/HelmholtzAI/SeisBench/"
  seisbench.remote_data_root = urljoin(seisbench.remote_root, "datasets/")
  seisbench.remote_model_root = urljoin(seisbench.remote_root, "models/v3/")

• On the Apple M1 and M2 chips, pytorch seems to not always work when installed directly within pip install seisbench. As a workaround, follow the instructions at (https://pytorch.org/) to install pytorch and then install SeisBench as usual through pip.
• Some institutions and internet providers are blocking access to our data and model repository, as it is running on a non-standard port (2880). This usually manifests in timeouts when trying to download data or model weights. To verify the issue, try accessing https://hifis-storage.desy.de:2880/ 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 2880 on our server. Otherwise, reach out to us, and we will work on finding a solution.
• EQTransformer model weights "original" in version 1 and 2 are incompatible with SeisBench >=0.2.3. Simply use from_pretrained("original", version="3") or from_pretrained("original", update=True). The weights will not differ in their predictions.

Reference publications for SeisBench:

• SeisBench - A Toolbox for Machine Learning in Seismology

  Reference publication for software.

• 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.

The initial version of SeisBench has been developed at GFZ Potsdam and KIT with funding from Helmholtz AI. The SeisBench repository is hosted by HIFIS - Helmholtz Federated IT Services.