Deep learning-based earthquake catalogs extracted from threebroadband/nodal seismic arrays with different apertures in Taiwan bySeisBlue
- Department of Geosciences, National Taiwan University, Taipei, Taiwan (johnson606100@gmail.com)
SeisBlue, a deep-learning-based earthquake monitoring system, is one of the solutions to deal with massive continuous waveform data and create earthquake catalogs. The SeisBlue workflow contains waveform data preprocessing, phase arrival detection by AI modules, phase associator, earthquake locating, earthquake catalog generation, and data visualization. The whole process can be done automatically and efficiently reduces the labor and time costs. In this study, SeisBlue is applied to three different regional seismic networks: the Formosa Array for the observation of magma chamber beneath the Tatun volcanic area, Taiwan (aperture ~80 km with 148 broadband stations and station spacing 5 km), the Chihshang seismic network (CSN) for monitoring micro-seismicity of Chihshang, Taiwan (aperture ~150 km with 14 broadband stations and station spacing 20 km), and the temporary dense nodal array for capturing the aftershock sequence of the 18 th Sep. 2022 Mw6.9 Chihshang earthquake, Taiwan (aperture ~70 km with 46 nodal stations and station spacing 3 km). The 2020 annual SeisBlue catalog of the Formosa Array contains 2,201 earthquakes, as background seismicity, compare to the 1,467 earthquakes listed in the standard catalog of the Central Weather Administration (CWA), Taiwan. The two-month SeisBlue catalog of the 2022 Mw6.9 Chihshang earthquake sequence, September to October, contains 14,276 earthquakes using the CSN dataset; however, the CWA standard catalog only lists 1,247 earthquakes during the same time period. By using waveform data of 18 th Sep. to 25 th Oct. 2022, SeisBlue detects 34,630 and 12,458 earthquakes extracted from the datasets of the dense nodal array and CSN, respectively. SeisBlue can effectively detects both background and aftershock seismicity and extracts small earthquakes via dense arrays.
Keywords: AI earthquake monitoring system, deep learning, AI earthquake catalog, SeisBlue, automatic waveform picking
How to cite: Pan, S.-Y., Sun, W.-F., Huang, C.-M., and Kuo-Chen, H.: Deep learning-based earthquake catalogs extracted from threebroadband/nodal seismic arrays with different apertures in Taiwan bySeisBlue, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7309, https://doi.org/10.5194/egusphere-egu24-7309, 2024.