A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan
A timely, high-resolution earthquake catalog is crucial for estimating seismic evolution and assessing hazards. This study aims to introduce a deep-learning-based real-time microearthquake monitoring system (RT-MEMS) for Taiwan, designed to provide rapid and reliable earthquake catalogs. The system...
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3353 |
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| author | Wei-Fang Sun Sheng-Yan Pan Yao-Hung Liu Hao Kuo-Chen Chin-Shang Ku Che-Min Lin Ching-Chou Fu |
| author_facet | Wei-Fang Sun Sheng-Yan Pan Yao-Hung Liu Hao Kuo-Chen Chin-Shang Ku Che-Min Lin Ching-Chou Fu |
| author_sort | Wei-Fang Sun |
| collection | DOAJ |
| description | A timely, high-resolution earthquake catalog is crucial for estimating seismic evolution and assessing hazards. This study aims to introduce a deep-learning-based real-time microearthquake monitoring system (RT-MEMS) for Taiwan, designed to provide rapid and reliable earthquake catalogs. The system integrates continuous data from high-quality seismic networks via SeedLink with deep learning models and automated processing workflows. This approach enables the generation of an earthquake catalog with higher resolution and efficiency than the standard catalog announced by the Central Weather Administration, Taiwan. The RT-MEMS is designed to capture both background seismicity and earthquake sequences. The system employs the SeisBlue deep learning model, trained with a local dataset, to process continuous waveform data and pick P- and S-wave arrivals. Earthquake events are then associated and located using a modified version of PhasePAPY. Three stable RT-MEMS have been established in Taiwan: one for monitoring background seismicity along a creeping fault segment and two for monitoring mainshock–aftershock sequences. The system can provide timely information on changes in seismic activity following major earthquakes and generate long-term catalogs. The refined catalogs from RT-MEMS contribute to a more detailed understanding of seismotectonic structures and serve as valuable datasets for subsequent research. |
| format | Article |
| id | doaj-art-21b0e89712d24a2e89afd9000f8ed336 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-21b0e89712d24a2e89afd9000f8ed3362025-08-20T03:46:49ZengMDPI AGSensors1424-82202025-05-012511335310.3390/s25113353A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for TaiwanWei-Fang Sun0Sheng-Yan Pan1Yao-Hung Liu2Hao Kuo-Chen3Chin-Shang Ku4Che-Min Lin5Ching-Chou Fu6Department of Geosciences, National Taiwan University, Taipei City 10617, TaiwanDepartment of Geosciences, National Taiwan University, Taipei City 10617, TaiwanDepartment of Geosciences, National Taiwan University, Taipei City 10617, TaiwanDepartment of Geosciences, National Taiwan University, Taipei City 10617, TaiwanInstitute of Earth Sciences, Academia Sinica, Taipei City 115201, TaiwanNational Center for Research on Earthquake Engineering, National Institutes of Applied Research, Taipei City 106219, TaiwanInstitute of Earth Sciences, Academia Sinica, Taipei City 115201, TaiwanA timely, high-resolution earthquake catalog is crucial for estimating seismic evolution and assessing hazards. This study aims to introduce a deep-learning-based real-time microearthquake monitoring system (RT-MEMS) for Taiwan, designed to provide rapid and reliable earthquake catalogs. The system integrates continuous data from high-quality seismic networks via SeedLink with deep learning models and automated processing workflows. This approach enables the generation of an earthquake catalog with higher resolution and efficiency than the standard catalog announced by the Central Weather Administration, Taiwan. The RT-MEMS is designed to capture both background seismicity and earthquake sequences. The system employs the SeisBlue deep learning model, trained with a local dataset, to process continuous waveform data and pick P- and S-wave arrivals. Earthquake events are then associated and located using a modified version of PhasePAPY. Three stable RT-MEMS have been established in Taiwan: one for monitoring background seismicity along a creeping fault segment and two for monitoring mainshock–aftershock sequences. The system can provide timely information on changes in seismic activity following major earthquakes and generate long-term catalogs. The refined catalogs from RT-MEMS contribute to a more detailed understanding of seismotectonic structures and serve as valuable datasets for subsequent research.https://www.mdpi.com/1424-8220/25/11/3353real-time microearthquake monitoring systemdeep learningSeedLinkautomated workflowearthquake catalog |
| spellingShingle | Wei-Fang Sun Sheng-Yan Pan Yao-Hung Liu Hao Kuo-Chen Chin-Shang Ku Che-Min Lin Ching-Chou Fu A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan Sensors real-time microearthquake monitoring system deep learning SeedLink automated workflow earthquake catalog |
| title | A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan |
| title_full | A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan |
| title_fullStr | A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan |
| title_full_unstemmed | A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan |
| title_short | A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan |
| title_sort | deep learning based real time microearthquake monitoring system rt mems for taiwan |
| topic | real-time microearthquake monitoring system deep learning SeedLink automated workflow earthquake catalog |
| url | https://www.mdpi.com/1424-8220/25/11/3353 |
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