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SeisBench is an open-source Python toolbox for applying and training machine-learning models on seismic data. It brings together model interfaces, dataset access, and data-generation pipelines, so researchers can work with existing models or build workflows around their own datasets. It supports conventional seismic waveforms and distributed acoustic sensing (DAS), but a model’s suitability still depends on the task and how closely its training data matches the data you want to analyze.
What SeisBench does
SeisBench is software, not a seismic sensor or a model by itself. Its purpose is to make seismology datasets and machine-learning models easier to access, apply, train, and compare through a shared Python toolkit. The project documentation describes a common model interface for loading pretrained weights, running models on new data, and training with custom datasets. Read the SeisBench documentation.
The repository organizes its main functionality into three modules:
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data: access to benchmark datasets and tools for loading data.models: available models, including facilities to load and train them.generate: data-generation pipelines that connect datasets to models.
Examples and notebooks cover introductory use as well as phase picking, training PhaseNet, creating datasets, and event-catalog workflows. See the SeisBench repository.
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What data and tasks can it support?
The documented model tasks include earthquake detection, phase identification and picking, denoising, and depth-phase identification. These describe capabilities available across the project; they do not mean every model supports every task or every compute backend. Models document support for GPU and Apple MPS acceleration, but check the requirements of the particular model and your installation.
For data, the documentation distinguishes conventional seismic records—such as those from seismometers, strong-motion instruments, and geophones—from DAS recordings. Model interfaces can work directly with ObsPy streams for conventional waveform data or xdas arrays for DAS. If your goal is only to apply a model, you may not need the dataset-training and evaluation features.
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Start by matching the input format and target task to a model. Then check whether the model’s training data is relevant to your region and recording domain; a model that accepts your waveform format is not necessarily a good scientific fit for it.
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The documented pip installation is:
pip install seisbenchFor distributed acoustic sensing support, install the optional extra:
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pip install "seisbench[das]"Installation details and dependencies can change, so consult the SeisBench package page and repository instructions for the version you plan to use. The project also documents installation from source. CPU-only PyTorch is an option if you do not need CUDA acceleration; GPU use depends on installing a compatible PyTorch configuration and on the model and hardware involved.
Apply a pretrained model
For inference, load a model and its pretrained weights, then pass data in the input form the model supports. The project’s 2022 paper describes applying models to seismic streams; the model documentation explains the shared interfaces and supported input types. See the SeisBench paper and model documentation for examples and current details. The paper describes weights being downloaded and cached when needed; exact behavior can depend on the model and software version.
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Train or adapt a workflow
For training, use the dataset functionality to load data, then connect it to a model through data-generation transforms. The paper illustrates a PhaseNet training workflow, while repository examples include dataset creation and more advanced workflows. Your own training results will depend on dataset quality, labels, preprocessing, and evaluation design; the example is a documented workflow, not a performance guarantee.
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The benchmark paper “Which picker fits my data?” compared six previously published deep-learning models on eight datasets spanning local to teleseismic distances and three tasks: event detection, phase identification, and onset-time picking. The authors reported that EQTransformer, GPD, and PhaseNet performed best overall in that study, with a small EQTransformer advantage on teleseismic data. They also found mild performance degradation when transferring models between regions, but failure to transfer between regional and teleseismic data in either direction. See the benchmark preprint.
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Those findings describe the models, datasets, and task definitions used in that benchmark—not a universal ranking for all SeisBench versions or all seismic data. Use them as context, then evaluate a candidate on data representative of your intended use.
- Define the task: detection, phase identification, and onset-time picking are distinct problems.
- Match the domain: consider whether your data are local, regional, or teleseismic, and whether they are conventional waveforms or DAS.
- Check training-data fit: look for alignment in geography, recording conditions, and event characteristics.
- Validate locally: compare candidates on a held-out dataset relevant to your deployment or analysis. Do not assume cross-region or cross-domain transfer.
How to cite SeisBench
The project asks users to cite its 2022 paper:
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.” Seismological Research Letters. DOI: 10.1785/0220210324. The project documentation links an arXiv preprint for readers who cannot access the journal version.
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