SeisBlue: a deep-learning data processing platform for seismology
- 1National Taiwan University, Department of Geoscience, Taipei, Taiwan (d11224001@ntu.edu.tw)
- 2Dept. of Earth Sciences, National Central University, Zhongli, Taiwan
- 3Dept. of Geosciences, National Taiwan University, Taipei, Taiwan, ORCID(s): 0000-0003-2282-9218
- 4Dept. of Computer Science and Information Engineering , National Central University, Zhongli, Taiwan
Deep learning has greatly improved the efficiency of earthquake detection and phase picking tasks, as demonstrated by neural network models such as PhaseNet and EQTransformer. However, the code released by these authors is not production-ready software that can be easily integrated into our lab's workflow. To solve this problem, we developed "SeisBlue," a platform that brings all the necessary steps together in one place. It includes these major components: database client, data inspector, data converter, model trainer, model evaluator, and pick associator, and is designed to be modular and interchangeable to allow for easy experimentation with different combinations.
SeisBlue has been used in several major earthquake events in Taiwan, including the 918 Taitung earthquake (magnitude 6.9 Mw). In this event, we were able to capture over 1,200 events near real-time in just two days - a task that would have taken over a month to complete manually. The quickly-released earthquake catalog provided insight into the complex behavior of the blind fault.
How to cite: 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.
