地震是危害人類的重大天然災害之一，臺灣位於歐亞大陸板塊與菲律賓海板塊的交界，地震活動頻繁。倘若一個規模大且淺層的地震發生於人口活動密集的區域，則可能會伴隨無數的人員傷亡以及嚴重的經濟損失。
地震預警是一種降低地震所帶來的損害的手段，其中定位結果影響著規模與震度的評估，對於地震預警定位的演算公式而言主要講求計算簡易且結果精確。本研究所使用的定位演算法基於 Equal Differential Time (EDT)公式，並將得出的到時殘差機率密度化，且僅使用 P 波到時資料進行定位計算。首先根據地震測站網密度將格
點可分成密度較高的本島區域與密度較低的外海區域並做出基礎網格點，再近一步將兩格點之中生成一格點並確立其半徑 10 公里內有發生過芮氏規模大於等於 3 的歷史地震後保留該點，將已建立好的臺灣地區格點到距離它前 20 個測站的走時資料表並儲存。在地震觸發測站時，用事件分類機制將觸發測站分到所屬的事件字典中，並迅速交集已觸發的測站群，將兩倆測站觀測到時差與理論走時差的差值機率化，機率會隨著測站觸發而疊代，最終得出機率最大的格點為定位震源。本研究對於歷史地震資料進行離線定位測試，以及分類同時發生三個地震與同地點連續地震的離線事件分類及定位測試。
在歷史地震資料(於 1990~2022 年間挑選出規模大於 6 的 32 地震座標)的離線定位測試結果中，島內事件的定位誤差平均為 2.83 公里、每次的計算時間平均為 0.08秒、最佳解(最相近真實震源的解)的花費時間平均為 1.3 秒; 外海事件的定位誤差平均為 4.91 公里、每次的計算時間平均為 0.13 秒、最佳解的花費時間平均為 3.32
秒 ; 全部地震事件的平均定位時長為 2.69 秒、定位誤差為 4.26 公里。在多重地震事件的離線測試上，不論是相異位置同時觸發的事件，或是相同位置的連續事件，本演算法皆能辨別出觸發測站所屬的各別事件，並且能快速定位。未來將與機器學習的挑波模型結合，使用臺灣地震即時資料進行預警地震定位的離線測試。也期盼能將此預警定位法能進一步提升臺灣即時地震定位的速度與精確度。

Earthquake early warning is a means of mitigating the damages caused by earthquakes, and the accuracy of the location results is crucial for assessing the magnitude and intensity. The algorithm used in this study for regional earthquake early warning location focuses on simplicity and accuracy. It is based on the Equal Differential Time formula and utilizes a three-dimensional velocity model to establish travel time data for Taiwan. Finally, the residual arrival times are converted into probability density and iterated upon.
In this study, historical earthquake data was used for offline location testing, as well as offline event classification and location testing for simultaneous triple earthquakes and consecutive earthquakes at the same location.
The results of the offline testing indeed demonstrate the ability to quickly and accurately locate the earthquake sources. The algorithm can identify individual events triggered simultaneously or consecutively at different or the same locations, respectively, and provide rapid localization. In the future, we plan to integrate the algorithm with machine learning waveform picking models and conduct offline testing using real-time seismic data from Taiwan for earthquake early warning location. We hope to further enhance the speed and accuracy of real-time earthquake localization in Taiwan with this warning and location method.

Allen, R. M., Gasparini, P., Kamigaichi, O., & Bose, M. (2009). The status of earthquake early warning around the world: An introductory overview. Seismological Research Letters, 80(5), 682-693.

Chen, D. Y., Hsiao, N. C., & Wu, Y. M. (2015). The Earthworm based earthquake alarm reporting system in Taiwan. Bulletin of the Seismological Society of America, 105(2A), 568-579.

Chen, D. Y., Lin, T. L., Hsu, H. C., Hsu, Y. C., & Hsiao, N. C. (2019). An approach to improve the performance of the earthquake early warning system for the 2018 Hualien earthquake in Taiwan. Terr. Atmos. Ocean. Sci, 30, 423-433.

Font, Y., Kao, H., Lallemand, S., Liu, C. S., & Chiao, L. Y. (2004). Hypocentre determination offshore of eastern Taiwan using the Maximum Intersection method. Geophysical Journal International, 158(2), 655-675.

Ge, M. (2003). Analysis of source location algorithms: Part II. Iterative methods. Journal of Acoustic Emission, 21(1), 29-51.

Hsu, H. C., Chen, D. Y., Tseng, T. L., Wu, Y. M., Lin, T. L., & Pu, H. C. (2018). Improving location of offshore earthquakes in earthquake early warning system. Seismological Research Letters, 89(3), 1101-1107.

Kanamori, H. (2007). Real-time earthquake damage mitigation measures. Earthquake early warning systems, 1-8.

Koulakov, I. (2009). LOTOS Code for Local Earthquake Tomographic Inversion: Benchmarks for Testing Tomographic AlgorithmsLOTOS Code for Local Earthquake Tomographic Inversion: Benchmarks for Testing Tomographic Algorithms, Bulletin of the Seismological Society of America 99, no. 1, 194–214, doi: 10.1785/0120080013.

Kodera, Y., Saitou, J., Hayashimoto, N., Adachi, S., Morimoto, M., Nishimae, Y., & Hoshiba, M. (2016). Earthquake early warning for the 2016 Kumamoto earthquake: Performance evaluation of the current system and the next-generation methods of the Japan Meteorological Agency. Earth, Planets and Space, 68(1), 1-14.

Kodera, Y., Yamada, Y., Hirano, K., Tamaribuchi, K., Adachi, S., Hayashimoto, N., ... & Hoshiba, M. (2018). The Propagation of Local Undamped Motion (PLUM) Method: A Simple and Robust Seismic Wavefield Estimation Approach for Earthquake Early WarningThe Propagation of Local Undamped Motion (PLUM) Method. Bulletin of the Seismological Society of America, 108(2), 983-1003.

Liao, W.-Y., E.-J. Lee, D.-Y. Chen, P. Chen, D. Mu, and Y.-M. Wu (2022). RED-PAN: Real-Time Earthquake Detection and Phase-Picking With Multitask Attention Network, IEEE Transactions on Geoscience and Remote Sensing 60, 1–11, doi: 10.1109/TGRS.2022.3205558.

Liao, W.-Y., E.-J. Lee, D. Mu, P. Chen, and R.-J. Rau (2021). ARRU Phase Picker: Attention Recurrent-Residual U-Net for Picking Seismic P - and S -Phase Arrivals, Seismological Research Letters 92, no. 4, 2410–2428, doi: 10.1785/0220200382.

Murillo, J. M. M. (1995). The 1985 méxico earthquake. Earth Sciences Research Journal, (3), 5-19.

Mori, N., Takahashi, T., & 2011 Tohoku Earthquake Tsunami Joint Survey Group. (2012). Nationwide post event survey and analysis of the 2011 Tohoku earthquake tsunami. Coastal Engineering Journal, 54(01), 1250001.

Nakamura, Y., & Saita, J. (2007). UrEDAS, the earthquake warning system: Today and tomorrow. Earthquake early warning systems, 249-281.

Sambridge, M. (1999). Geophysical inversion with a neighbourhood algorithm—I. Searching a parameter space. Geophysical journal international, 138(2), 479-494.

Shin, T. C., & Teng, T. L. (2001). An overview of the 1999 Chi-Chi, Taiwan, earthquake. Bulletin of the seismological society of America, 91(5), 895-913.

Satriano, C., Lomax, A., & Zollo, A. (2007). Optimal, real-time earthquake location for early warning. Earthquake early warning systems, 85-96.

Satriano, C., Lomax, A., & Zollo, A. (2008). Real-time evolutionary earthquake location for seismic early warning. Bulletin of the Seismological Society of America, 98(3), 1482-1494.

Strauss, J. A., & Allen, R. M. (2016). Benefits and costs of earthquake early warning. Seismological Research Letters, 87(3), 765-772.

Wu, Y. M., Mittal, H., Chen, D. Y., Hsu, T. Y., & Lin, P. Y. (2021). Earthquake early warning systems in Taiwan: Current status. Journal of the Geological Society of India, 97, 1525-1532.

Yamada, M., Tamaribuchi, K., & Wu, S. (2021). The extended integrated particle filter method (IPFx) as a high‐performance earthquake early warning system. Bulletin of the Seismological Society of America, 111(3), 1263-1272.

Zhou, H. W. (1994). Rapid three‐dimensional hypocentral determination using a master station method. Journal of Geophysical Research: Solid Earth, 99(B8), 15439-15455.

黃瑞樺、樂鍇 祿璞崚岸，2016：走時序列定位法及波形疊加法應用於臺灣線上即時 區域地震預警陣列，共 194 頁。