GEOthermal SEISmic AI Platform (GEOSEIS-AI): A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan

Establishing a real-time and high-resolution earthquake catalog is crucial for understanding the development process of earthquake sequences and conducting disaster risk assessment. This study developed a real-time microearthquake monitoring system (RT-MEMS) that integrates deep learning technology (Sun et al., 2025). After testing and verification, it was confirmed that the system can quickly and reliably provide earthquake activity information through a fully automated process. The main data processing process of the system includes: (1) using SeedLink to receive continuous waveform data from four broadband seismic networks, maintained by the Institute of Earth Sciences of Academia Sinica, the National Center for Research on Earthquake Engineering, National Chung Cheng University, and National Taiwan University, and store and build a continuous waveform database; (2) using a deep learning model trained with Taiwan earthquake arrival data to identify and select P- and S-wave arrival times and store them in an arrival database; (3) selecting appropriate seismic station combinations according to the monitoring area, extracting corresponding P- and S-wave arrival times to associate and locate earthquake events, and generating a preliminary deep learning earthquake catalog; (4) preparing daily earthquake reports and sending them to relevant personnel via email, LINE, Discord etc. Compared with the existing seismic observation network, this system has shown advantages in microseismic detection and analysis capabilities and processing efficiency. It is particularly suitable for specific areas or fields that require intensive monitoring. Currently, three real-time microseismic monitoring systems have been established: 1. Chihshang real-time microearthquake monitoring system (2022CSN-RT-MEMS), which observes the background microseismic activity of the creeping segment of the Chihshang fault, including the 2022 M6.9 Chihshang earthquake sequence (Sun et al., 2024); 2. Hualien earthquake real-time microseismic monitoring system (2024HL-RT-MEMS), which continuously observes the changes in the aftershock sequence of the 2024 M7.2 Hualien earthquake; 3. the Chia-Nan real-time microseismic monitoring system (2025CN-RT-MEMS), that this system was established in early 2025 to observe the main aftershock sequence of medium and large earthquakes in the area including the 2025 M6.4 Dapu earthquake sequence (Kuo-Chen et al., 2025). RE-MEMS can quickly provide changes in seismic activity and establish a long-term earthquake catalog. After further data processing (such as absolute or relative relocation), the earthquake catalog will help the subsequent interpretation of earthquake tectonic structures and other earthquake parameter studies, such as focal mechanism, earthquake magnitude, and three-dimensional velocity model inversion. In summary, RT-MEMS serves as an effective reinforcement for the current earthquake observation network, significantly improving the timeliness and resolution of earthquake observation.

Keywords: real-time microearthquake monitoring system; deep learning; SeedLink; automated workflow; earthquake catalog

References

Kuo-Chen H., et al. (2025). Real-time earthquake monitoring with deep learning: A case study of the 2025 M6.4 Dapu earthquake and its fault system in southwestern Taiwan. The Seismic Record, 5(3), 320-329, https://doi.org/10.1785/0320250023.

Sun, W. F., et al. (2024). Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 M_W 6.9 Chihshang earthquake sequence. Terr. Atmos. Ocean. Sci., 35, 5, https://doi.org/10.1007/s44195-024-00063-9.

Sun, W. F., et al. (2025). A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan. Sensors, 25(11), 3353. https://doi.org/10.3390/s25113353.