The seismological machine learning benchmark collection
Project description
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 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 seismic data.
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.
Alternatively, you can clone the repository and run the same examples locally.
To get detailed information on Seisbench check out the SeisBench documentation.
Installation
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
Alternatively, you can install the latest version from source. For this approach, clone the repository, switch to the repository root and run:
pip install .
Contributing
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.
Citation
A reference publication for SeisBench is under publication. Please check back later.
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