Metadata-Version: 2.4
Name: seisbench
Version: 0.12.0a0
Summary: The seismological machine learning benchmark collection
Author-email: Jack Woolam <jack.woollam@kit.edu>, Jannes Münchmeyer <munchmej@gfz-potsdam.de>
Maintainer-email: Jack Woolam <jack.woollam@kit.edu>, Jannes Münchmeyer <munchmej@gfz-potsdam.de>
License: GPLv3
Project-URL: GitHub, https://github.com/seisbench/seisbench
Project-URL: Documentation, https://seisbench.readthedocs.io/en/latest/
Project-URL: Issues, https://github.com/seisbench/seisbench/issues
Keywords: seismology,machine learning,signal processing,earthquake
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.21.6
Requires-Dist: pandas>=1.1
Requires-Dist: h5py>=3.1
Requires-Dist: obspy>=1.3.1
Requires-Dist: tqdm>=4.52
Requires-Dist: torch>=1.10.0
Requires-Dist: scipy>=1.9
Requires-Dist: nest_asyncio>=1.5.3
Requires-Dist: bottleneck>=1.3
Requires-Dist: typing_extensions>=4.0
Provides-Extra: das
Requires-Dist: xdas>=0.2.3; extra == "das"
Requires-Dist: pyarrow; extra == "das"
Requires-Dist: torchvision; extra == "das"
Provides-Extra: dev
Requires-Dist: ruff; extra == "dev"
Requires-Dist: pre-commit; extra == "dev"
Provides-Extra: tests
Requires-Dist: pytest; extra == "tests"
Requires-Dist: pytest-asyncio>=0.26.0; extra == "tests"
Requires-Dist: pytest-benchmark; extra == "tests"
Dynamic: license-file

<p align="center">
  <img src="https://raw.githubusercontent.com/seisbench/seisbench/main/docs/_static/seisbench_logo_subtitle_outlined.svg" />
</p>

---

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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.

## Getting started

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.
Just click on the "Open in Colab" link to run them in your browser.
Alternatively, you can clone the repository and run the same [examples](https://github.com/seisbench/seisbench/tree/main/examples) locally.

*Note:* If an example notebooks has been added very recently, it might rely on functionality that is not yet part of a numbered version.
To run it nonetheless, you can replace the installation line with `pip install "seisbench[das] @ git+https://github.com/seisbench/seisbench"` to use the latest development branch.

### Basic examples

These examples introduce you to the key modules of SeisBench.

| Examples              |                                                                                                                                                                                                         |
|-----------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Dataset basics        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/01a_dataset_basics.ipynb)                  |
| Model API             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/01b_model_api.ipynb)                       |
| Generator Pipelines   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/01c_generator_pipelines.ipynb)             |

### Advanced topics

This section covers topics like training models, creating datasets or building earthquake catalogs.

| Examples                              |                                                                                                                                                                                                         |
|---------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Applied picking                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/02a_deploy_model_on_streams_example.ipynb) |
| Training PhaseNet                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/03a_training_phasenet.ipynb)               |
| Creating a dataset                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/03b_creating_a_dataset.ipynb)              |
| Building an event catalog with GaMMA  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/03c_catalog_seisbench_gamma.ipynb)         |
| Building an event catalog with PyOcto | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/03d_catalog_seisbench_pyocto.ipynb)        |

### Using SeisBench for DAS data

These tutorials introduce how to use SeisBench to train and apply models for distributed acoustic sensing (DAS) data.

| Examples                 |                                                                                                                                                                                                         |
|--------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Applying DAS models      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/04a_das_models.ipynb)                       |
| Training DeepSubDAS      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/04b_training_deepsubdas.ipynb)               |

### Miscellaneous

A collection of notebooks for miscellaneous topics, such as denoising or depth estimation.

| Examples                          |                                                                                                                                                                                                      |
|-----------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Training DKPN                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/03f_training_dkpn.ipynb)                |
| Using DeepDenoiser                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/02b_deep_denoiser.ipynb)                |
| Training Denoiser                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/03e_training_denoiser.ipynb)            |
| Depth phases and earthquake depth | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/02c_depth_phases.ipynb)                 |


For more detailed information on Seisbench check out the [SeisBench documentation](https://seisbench.readthedocs.io/).

## Installation

SeisBench can be installed in two ways.
In both cases, you might consider installing SeisBench in a virtual environment, for example using [conda](https://docs.conda.io/en/latest/).

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.

If you want to run SeisBench on DAS data, make sure to pass the `[das]` argument, i.e., `pip install seisbench[das]`.

### CPU only installation

SeisBench is built on pytorch, which in turn runs on CUDA for GPU acceleration.
Sometimes, it might be preferable to install pytorch without CUDA, for example, because CUDA will not be used and the CUDA binaries are rather large.
To install such a pure CPU version, the easiest way is to follow a two-step installation.
First, install pytorch in a pure CPU version [as explained here](https://pytorch.org/).
Second, install SeisBench the regular way through pip.
Example instructions would be:
```
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install seisbench
```

## Contributing
There are many ways to contribute to SeisBench and we are always looking forward to your contributions.
Check out the [contribution guidelines](https://github.com/seisbench/seisbench/blob/main/CONTRIBUTING.md) for details on how to contribute.

## Known issues

- We've experienced occasional issues with access to our repository.
  To verify the issue, try accessing [https://hifis-storage.desy.de](https://hifis-storage.desy.de/) directly from the same machine.
  As a mitigation, you can use our backup repository. Just run `seisbench.use_backup_repository()`.
  Please note that the backup repository will usually show lower download speeds.
- We've recently changed the URL of the SeisBench repository. To use the new URL update to SeisBench 0.11.5.
  It this is not possible, you can use the following commands within your runtime to update the URL manually:
  ```python
  import seisbench
  from urllib.parse import urljoin

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

## References
Reference publications for SeisBench:

---

* [SeisBench - A Toolbox for Machine Learning in Seismology](https://doi.org/10.1785/0220210324)

  _Reference publication for software._

---

* [Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers](https://doi.org/10.1029/2021JB023499)

  _Example of in-depth benchmarking study of deep learning-based picking routines using the SeisBench framework._

---

## Acknowledgements

The initial version of SeisBench has been developed at [GFZ Potsdam](https://www.gfz-potsdam.de/) and [KIT](https://www.gpi.kit.edu/) with funding from [Helmholtz AI](https://www.helmholtz.ai/).
The SeisBench repository is hosted by [HIFIS - Helmholtz Federated IT Services](https://www.hifis.net/).

This development of the LFE detection model has been supported by MIAI@Grenoble Alpes (ANR-19-P3IA-0003) and
the European Union through the Marie Skłodowska-Curie Actions (n°101104996 - DECODE).

The initial DAS model and data interface was supported by the European Commission
under the Horizon Europe programme, through the project SUBMERSE (https://submerse.eu/)
(Grant Agreement No. 101095055), funded within the HORIZON-INFRA-2022-TECH-01 call.
