A deep-learning data processing platform for seismology
This project is turned into internal development. The code is not maintained and updated.
Please contact the author or SGYLAB for more information.
- Huang, C.-M., Chang, L.-H., Kuo-Chen, H., and Zhuang, Y.: SeisBlue: a deep-learning data processing platform for seismology, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13927, https://doi.org/10.5194/egusphere-egu23-13927, 2023.
- Sun, WF., Pan, SY., Huang, CM. et al. Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence. Terr Atmos Ocean Sci 35, 5 (2024). https://doi.org/10.1007/s44195-024-00063-9
- Kuo-Chen, H., Sun, W., Huang, C., Pan, S., 2022, Near real-time seismic data processing helps scientist understand aftershocks, Temblor, http://doi.org/10.32858/temblor.276
Reference:
Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature communications, 11(1), 1-12.
Zhu, W., & Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. arXiv preprint arXiv:1803.03211.
Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 3-11). Springer, Cham.
Chen, C., & Holland, A. A. (2016). PhasePApy: A robust pure Python package for automatic identification of seismic phases. Seismological Research Letters, 87(6), 1384-1396.
Chang, Y. H., Hung, S. H., & Chen, Y. L. (2019). A fast algorithm for automatic phase picker and event location: Application to the 2018 Hualien earthquake sequences. Terr. Atmos. Ocean. Sci, 30, 435-448.
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