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Yews is an open-source project dedicated to providing a deep learning framework for processing seismological data. It contains abstract classes for deep learning tasks as well as automation tools for preparing seismic dataset.
[April 25 2019] CPIC model on Marina dataset was trained up to 99.5% accuracy.
- Training used the same setup as
[April 17 2019] Add tests
yews.transformsis covered 100%
yews.datasetsis covered 100%
[April 16 2019] Redesigne datasets classes
[April 15 2019] Documentation online
- Documentation is now available on the custom domain at https://www.yews.info
[April 14 2019] Unit Test added
yews.transformsmodule is under unit test
[April 12 2019] Improvement for the GitHub repo
- A logo added on the top of README.md
- Travis-CI and Codecov have been set up for Linux platform
- AppVeyor have been set up for Windows platform
[April 10 2019] Workshop @ GT ML for Seismic
We held our first internal workshop to introduce the Yews package and open for the internal alpha test.
- Processing seismic waveform data by deep learning
- Peripheral tools to facilitate research in seismic processing
- Release an alpha test version (0.0.1) in April 2019
- Additional alpha test version (0.0.2 - 0.0.3)
- Release beta test version (tentatively v0.0.5) in August 2019
- Release the first stable version (v0.1.0) in December 2019
yews.datasetand add unittest
yews.train(tentatively renamed to
yews.utils) and add unittest
- Get a list of feature request from EAS scholars
Please support the project by acknowledging the use of it. The citations help us keep it alive. If you use Yews for work resulting in an academic publication, we would be grateful if you cite one of the following papers:
Zhu, L., Peng, Z., McClellan, J., Li, C., Yao, D., Li, Z., & Fang, L. (2019).
Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7. 9 Wenchuan.
Zhu, L., Peng, Z., & McClellan, J. (2018, October).
Deep learning for seismic event detection of earthquake aftershocks.
In 2018 52nd Asilomar Conference on Signals, Systems, and Computers (pp. 1121-1125). IEEE.
Zhu, L., Peng, Z., & McClellan, J. (2018, June).
Event Detection and Phase Picking Based on Deep Convolutional Neural Networks.
In 80th EAGE Conference and Exhibition 2018.
Zhu, L., Li, Z., Li, C., Wang, B., Chen, Z., McClellan, J. H., & Peng, Z. (2017, December).
Machine-Learning Inspired Seismic Phase Detection for Aftershocks of the 2008 MW7. 9 Wenchuan Earthquake.
In AGU Fall Meeting Abstracts, 2017.