User guide#

Rationale#

In the recent years, we worked on a scattering network for seismic time series data, mainly for data exploration task involving dimensionality reduction and clustering. We have seen an increasing interest in the seismological community for this kind of approach, and decided to make the code available to the community. This package delivers the network instance and in the tutorials we introduce the scattering network together with some data exploration applications. Please note that the task and data at hand drive the choice of the exact design of the scattering network and the tools used for data exploration. The following papers show some possible applications and we hope that they can inspire you for your specific use case. If you use this package for your own scientific output, please cite one or more of the following papers.

Tutorials#

The tutorials are available in the Tutorials section from the navigation bar. We will also make sure that most of the notebooks that we use for the papaers are made avaiable. If you have specific questions you would like to address, please open an issue on the github repository or contact us direclty.

Contribution guidelines#

Thank you for your interest in contributing to this project! Here are some guidelines to help ensure a smooth and successful contribution process. Please read them carefully before contributing. We are happy to answer any questions you may have, and to welcome you as a contributor.

  1. Fork the project to your own GitHub account by clicking the “Fork” button in the top right corner of the repository page. This will allow you to make changes to the project without affecting the main project.

  2. Create a new branch for your contribution. This will keep your changes separate from the main branch and make it easier to review and merge your changes. The name of your branch should be concise and descriptive. For example, if you are adding a new feature, you might call your branch “add-feature”.

  3. Write concise commit messages that describe the changes you made. Use the present tense and avoid redundant information. We try to follow the Conventional Commits specification.

  4. Make sure your changes work as intended and do not introduce new bugs or problems. Write tests if applicable.

  5. Document your changes with following the numpydoc format. This step is important to ensure that the package documentation is up to date and complete. If you are not sure about this step, we can help you.

  6. When you are ready to submit your changes, create a pull request from your branch to the main branch of the original repository. Provide a clear description of your changes and why they are necessary, and we will review your contribution.

Thank you again for your interest in contributing to this project!

References#

  1. Seydoux, L., Balestriero, R., Poli, P. et al. Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. Nat Commun 11, 3972 (2020). https://doi.org/10.1038/s41467-020-17841-x

  2. Barkaoui, S., Lognonné, P., Kawamura, T., Stutzmann, É., Seydoux, L., de Hoop, M. V., … & Banerdt, W. B. (2021). Anatomy of continuous Mars SEIS and pressure data from unsupervised learning. Bulletin of the Seismological Society of America, 111(6), 2964-2981. https://doi.org/10.1785/0120210095

  3. Steinmann, R., Seydoux, L., Beaucé, E., & Campillo, M. (2022). Hierarchical exploration of continuous seismograms with unsupervised learning. Journal of Geophysical Research: Solid Earth, 127(1), e2021JB022455. https://doi.org/10.1029/2021JB022455

  4. Steinmann, R., Seydoux, L., & Campillo, M. (2022). AI-Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns Geophysical Research Letters, 49(15), e2022GL098854. https://doi.org/10.1029/2022GL098854