SeisBench is an open-source Python toolbox for machine learning in seismology. It brings together the whole machine learning model lifecycle: datasets and benchmarks, models and training pipelines, and efficient implementations for deploying the models in production.
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 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
The easiest way of getting started is through our colab notebooks.
|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.
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/")
pip install seisbench. As a workaround, follow the instructions at (https://pytorch.org/) to install pytorch and then install SeisBench as usual through pip.
from_pretrained("original", update=True). The weights will not differ in their predictions.
Reference publications for SeisBench:
Reference publication for software.
Example of in-depth bencharking study of deep learning-based picking routines using the SeisBench framework.