| dc.description.abstract | This study has referenced the Transformer Earthquake Alerting Model (TEAM), a deep learning earthquake early warning (EEW) framework. I optimized the model using seismic data from Taiwan to develop the Taiwan Transformer Shaking Alert Model (TT-SAM), and it could rapidly calculate the seismic intensity to provide longer warning time. The model utilized the Taiwan Strong Motion Instrumentation Program (TSMIP) database to obtain waveforms for events with a magnitude greater than 3.5 that occurred between 1999 and 2019. I split the dataset for model training and testing, the observations in 2016 were separated individually for the final evaluation. I cut the waveform initially triggered by the P-wave into a time window of 15 seconds, and other triggered stations′ waveforms in these 15 seconds will also be included. The model extracts waveform features through a convolution neural network (CNN), while the transformer encoder builds the relationship between features and station location. At the end of the model, a mixture density network was implemented to predict ground shaking by probability density functions. A warning threshold at 25 cm/s2 in PGA was set, corresponding to Central Weather Administration (CWA) intensity IV, to validate the model′s performance with 2016 data. The result shows that precision and recall are 75% and 81%, respectively. While utilizing the rolling warning method, it′s noteworthy that the average lead time for the 2016 Mw6.4 Meinong event and Mw6.1 Taitung offshore event stand at 16 and 7 seconds, respectively. In order to validate model’s reliability, I analyzed not only the residual of predicted PGA at different station also the correlation between the feature map from CNN and other waveform physical attributes, e.g., waveform envelope. The objective is to improve the efficiency and dependability of AI-based EEW in Taiwan. In addition, evaluating the performance of the TT-SAM model and the CWA EEW system provides comparative results of estimated seismic intensity and warning time. In summary, by optimizing the TT-SAM framework and retraining it with Taiwan′s earthquake data, the model can quickly and effectively predict areas with a seismic intensity of 4 and above. Additionally, it provides model explanations to help assess the model′s reliability. In the future, the goal is to continually fine-tune the model with updated high-intensity data to address the issue of high intensity underestimation. The TT-SAM framework could also extend to predict PGV, thereby offering earthquake warnings that more closely align with actual damage distribution. | en_US |