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  • Seisbench - an open-source python toolbox for machine learning in seismology,
  • ObsPy - an open-source project dedicated to provide a Python framework for processing seismological data,
  • Jupyter Notebook and JupyterLab - a web services for interactive computing,
  • IPython - a powerful interactive shell,
  • PyTorch - an optimized tensor library for deep learning using GPUs and CPUs

Tools for picking P and S waves

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titleTODO

On HPC cluster - SLURM Job

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titleTODO

Training AI models

Our Platform supports the user with training their own weights for predefined models and even to create their own models. In the first case this could help to improve the quality of the model by training in on data coming from a particular seismic networks (see e.g. Johnson et al. 2021).

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  1. Active account on PLGrid Portal. To create an account see the official guide available in English and Polish.
  2. Computing grant on PLGrid Infrastructure. The resources needed for training are highly depended on the size of a training dataset. For a dataset of about 104 samples a single training session lasts about 10 minutes on a single GPU for models like GPD or PhaseNet. Please keep in mind that fine tuning of the model hyper-parameters usually requires tu run training sessions multiple times. Therefore applying for a grant, please use this numbers only as a starting point.
    We recommend to apply for resources on a cluster Athena or Ares, but it is possible to run the training on other clusters as well. Please apply for the CPU and GPU computing time, as the GPU significantly accelerates the training process.
  3. Mambaforge installation. Please follow the above instruction.
  4. Environment for training AI models. Please install the epos-ai-train environment, by downloading epos-ai-train.yml and running:

    Code Block
    mamba env create -f epos-ai-train.yml


Acknowledgements

The development of the EPOS AI platform and its integration with the EPISODES Platform was partially funded by the EPOS-PL+ project (No POIR.04.02.00-00-C005/19-00), co-financed by the European Union from the funds of the European Regional Development Fund (ERDF).

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