| const slidesData = [ |
| { |
| "id": "ppt1", |
| "title": "Real-Time Seismic Data Processing and Monitoring at CWA: Current Status and Future Directions", |
| "slidesCount": 26, |
| "slides": [ |
| { |
| "slideNumber": 1, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Real-Time Seismic Data Processing and Monitoring at CWA Current Status and Future Directions", |
| "Seismological Center, Central Weather Administration, Taiwan", |
| "2025.05.29 @ JPGU", |
| "Da-Yi Chen, Guan-Yi Song, Yu-Hsuan Chang", |
| "PPT file is available here." |
| ], |
| "image": "slides/ppt1/slide_1.png" |
| }, |
| { |
| "slideNumber": 2, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Comprehensive Seismic Monitoring Infrastructure", |
| "Open-source software : Earthworm and Seiscomp", |
| "Dynamic Real-Time Monitoring via Grafana", |
| "Summary", |
| "Contents" |
| ], |
| "image": "slides/ppt1/slide_2.png" |
| }, |
| { |
| "slideNumber": 3, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Seismological Center", |
| "About 60 people working in the seismological center" |
| ], |
| "image": "slides/ppt1/slide_3.png" |
| }, |
| { |
| "slideNumber": 4, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Earthquake Monitoring in CWA", |
| "Our team of approximately 10 staff provides 24/7 coverage, with three individuals on each shift." |
| ], |
| "image": "slides/ppt1/slide_4.png" |
| }, |
| { |
| "slideNumber": 5, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Earthquake Monitoring in CWA", |
| "news, seismic waveforms, observed intensities, and disseminations of the earthquake early warnings" |
| ], |
| "image": "slides/ppt1/slide_5.png" |
| }, |
| { |
| "slideNumber": 6, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "CWA Seismic Network" |
| ], |
| "image": "slides/ppt1/slide_6.png" |
| }, |
| { |
| "slideNumber": 7, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Seismic Monitoring infrastructure", |
| "First Layer", |
| "Second Layer", |
| "Third Layer", |
| "Field Stations", |
| "or", |
| "Institutes" |
| ], |
| "image": "slides/ppt1/slide_7.png" |
| }, |
| { |
| "slideNumber": 8, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "For Earthquake Early Warning System", |
| "Total 632 real-time stations" |
| ], |
| "image": "slides/ppt1/slide_8.png" |
| }, |
| { |
| "slideNumber": 9, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Open-source Earthquake Monitoring software", |
| "Earthworm", |
| "Open-source system for real-time seismic data.", |
| "Modular design, highly customizable for networks.", |
| "Global standard for earthquake monitoring.", |
| "USGS developed, widely used globally.", |
| "Robust, real-time processing and alerts.", |
| "Modern, open-source seismic data platform.", |
| "Integrated workflow: acquisition to alerts.", |
| "Advanced algorithms for precise earthquake processing.", |
| "Intuitive GUIs streamline analyst review.", |
| "Global standard for real-time monitoring." |
| ], |
| "image": "slides/ppt1/slide_9.png" |
| }, |
| { |
| "slideNumber": 10, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Open-source Earthquake Monitoring software", |
| "Earthworm" |
| ], |
| "image": "slides/ppt1/slide_10.png" |
| }, |
| { |
| "slideNumber": 11, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Dive into the First Layer", |
| "Field", |
| "Stations", |
| "RING", |
| "MSEED RING", |
| "Export", |
| "Second", |
| "Layer", |
| "NAS", |
| "tbuf2mseed", |
| "mseedarchiver", |
| "shared memory", |
| "shared memory", |
| "Using Earthworm software to integrate and archive data" |
| ], |
| "image": "slides/ppt1/slide_11.png" |
| }, |
| { |
| "slideNumber": 12, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Dive into the Earthquake Early Warning System", |
| "Earthworm Based Earthquake Alert Reporting (eBEAR) system", |
| "CWA EEW System is available in Docker Hub :", |
| "docker pull cwadayi/earthworm_ubuntu22.04_eew:v1" |
| ], |
| "image": "slides/ppt1/slide_12.png" |
| }, |
| { |
| "slideNumber": 13, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "A case of the M6.4 Dapu Earthquake", |
| "2025, January 21st" |
| ], |
| "image": "slides/ppt1/slide_13.png" |
| }, |
| { |
| "slideNumber": 14, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Real-time observed intensities for alert issuance", |
| "2024, April 3rd", |
| "First EEW alert", |
| "Second EEW alert", |
| "A case of the M7.2 Hualien Earthquake" |
| ], |
| "image": "slides/ppt1/slide_14.png" |
| }, |
| { |
| "slideNumber": 15, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Real-time Monitoring in Seiscomp", |
| "Map view of seismicity", |
| "Manual Picking", |
| "Data Access" |
| ], |
| "image": "slides/ppt1/slide_15.png" |
| }, |
| { |
| "slideNumber": 16, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Real-time Monitoring in Seiscomp", |
| "Check travel time curves and residuals of stations", |
| "CWA Seiscomp is available in Docker Hub :", |
| "docker pull cwadayi/seiscomp_ubuntu22.04" |
| ], |
| "image": "slides/ppt1/slide_16.png" |
| }, |
| { |
| "slideNumber": 17, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Real-time Monitoring via Grafana", |
| "Open-source platform for data visualization.", |
| "Create dynamic dashboards from many sources.", |
| "Supports diverse databases and data types.", |
| "Real-time monitoring of metrics and logs.", |
| "Customizable panels and query builders.", |
| "Alerting capabilities for threshold breaches.", |
| "Interactive graphs and data exploration.", |
| "Community-driven, highly extensible.", |
| "Visualize time-series and operational data.", |
| "Enhances observability across systems." |
| ], |
| "image": "slides/ppt1/slide_17.png" |
| }, |
| { |
| "slideNumber": 18, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Real-time Monitoring via Grafana", |
| "Using pressure gauge to monitoring wave height", |
| "CWA OBS network" |
| ], |
| "image": "slides/ppt1/slide_18.png" |
| }, |
| { |
| "slideNumber": 19, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Real-time Monitoring via Grafana", |
| "Monitoring data latency" |
| ], |
| "image": "slides/ppt1/slide_19.png" |
| }, |
| { |
| "slideNumber": 20, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Real-time Monitoring via Grafana", |
| "Monitoring data availability" |
| ], |
| "image": "slides/ppt1/slide_20.png" |
| }, |
| { |
| "slideNumber": 21, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Real-time Monitoring via Grafana", |
| "Monitoring disseminations of earthquake early warnings" |
| ], |
| "image": "slides/ppt1/slide_21.png" |
| }, |
| { |
| "slideNumber": 22, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Real-time Monitoring via Grafana", |
| "Monitoring seismic waveforms for a specific station" |
| ], |
| "image": "slides/ppt1/slide_22.png" |
| }, |
| { |
| "slideNumber": 23, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Large Language Model for EEW", |
| "Training large language model by using historical data" |
| ], |
| "image": "slides/ppt1/slide_23.png" |
| }, |
| { |
| "slideNumber": 24, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Global Collaboration and Data Access", |
| "Current continuous seismic waveforms will be available after 15 minutes" |
| ], |
| "image": "slides/ppt1/slide_24.png" |
| }, |
| { |
| "slideNumber": 25, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Summary", |
| "Robust Monitoring Infrastructure: CWA operates a comprehensive, 24/7 seismic network across Taiwan", |
| "Real-Time Processing Excellence: Leveraging Earthworm and SeisComP software", |
| "Advanced Observability with Grafana: Grafana dashboards provide dynamic, real-time visualization of system status, data latency, and earthquake warning dissemination, enhancing operational awareness" |
| ], |
| "image": "slides/ppt1/slide_25.png" |
| }, |
| { |
| "slideNumber": 26, |
| "title": "", |
| "text": [ |
| "‹#›", |
| "Thank you for your listening", |
| "PPT file is available here.", |
| "‹#›" |
| ], |
| "image": "slides/ppt1/slide_26.png" |
| } |
| ] |
| }, |
| { |
| "id": "ppt2", |
| "title": "2026年5月 地震預警系統報告", |
| "slidesCount": 17, |
| "slides": [ |
| { |
| "slideNumber": 1, |
| "title": "", |
| "text": [ |
| "2026年5月 地震預警系統報告", |
| "報告人: 林育謙" |
| ], |
| "image": "slides/ppt2/slide_1.png" |
| }, |
| { |
| "slideNumber": 2, |
| "title": "", |
| "text": [ |
| "5月中央氣象署強震即時警報系統(EEW)效能統計表" |
| ], |
| "image": "slides/ppt2/slide_2.png" |
| }, |
| { |
| "slideNumber": 3, |
| "title": "", |
| "text": [ |
| "PWS發布情形", |
| "115年5月1日 規模6.1臺灣東部海域地震", |
| "第1報 發布範圍", |
| "第2報 發布範圍" |
| ], |
| "image": "slides/ppt2/slide_3.png" |
| }, |
| { |
| "slideNumber": 4, |
| "title": "", |
| "text": [ |
| "案例探討", |
| "第一報預估震度", |
| "觀測震度", |
| "第一報: 18.5秒/幾何中心法/預估規模5.0", |
| "115年5月1日 規模6.1臺灣東部海域地震", |
| "預估減觀測震度" |
| ], |
| "image": "slides/ppt2/slide_4.png" |
| }, |
| { |
| "slideNumber": 5, |
| "title": "", |
| "text": [ |
| "震度正負1級準確度 80%", |
| "案例探討", |
| "5/1 規模6.1臺灣東部海域地震", |
| "第一報: 18.5秒/幾何中心法/預估規模5.0" |
| ], |
| "image": "slides/ppt2/slide_5.png" |
| }, |
| { |
| "slideNumber": 6, |
| "title": "", |
| "text": [ |
| "PWS發布情形", |
| "115年5月12日 規模5.6臺灣東部海域地震", |
| "第1報 發布範圍" |
| ], |
| "image": "slides/ppt2/slide_6.png" |
| }, |
| { |
| "slideNumber": 7, |
| "title": "", |
| "text": [ |
| "案例探討", |
| "第一報預估震度", |
| "觀測震度", |
| "第一報: 9.7秒/幾何中心法/預估規模5.3", |
| "115年5月12日 規模5.6臺灣東部海域地震", |
| "預估減觀測震度" |
| ], |
| "image": "slides/ppt2/slide_7.png" |
| }, |
| { |
| "slideNumber": 8, |
| "title": "", |
| "text": [ |
| "震度正負1級準確度 95%", |
| "案例探討", |
| "5/12 規模5.6臺灣東部海域地震", |
| "第一報: 9.7秒/幾何中心法/預估規模5.3" |
| ], |
| "image": "slides/ppt2/slide_8.png" |
| }, |
| { |
| "slideNumber": 9, |
| "title": "", |
| "text": [ |
| "PWS發布情形", |
| "115年5月17日 規模5.1臺灣東部海域地震", |
| "第1報 發布範圍", |
| "第2報 發布範圍", |
| "第3報 發布範圍" |
| ], |
| "image": "slides/ppt2/slide_9.png" |
| }, |
| { |
| "slideNumber": 10, |
| "title": "", |
| "text": [ |
| "案例探討", |
| "第一報預估震度", |
| "觀測震度", |
| "第一報: 11.9秒/幾何中心法/預估規模5.0", |
| "115年5月17日 規模5.1臺灣南投埔里地震", |
| "預估減觀測震度" |
| ], |
| "image": "slides/ppt2/slide_10.png" |
| }, |
| { |
| "slideNumber": 11, |
| "title": "", |
| "text": [ |
| "震度正負1級準確度 85%", |
| "案例探討", |
| "5/17 規模5.1臺灣東部海域地震", |
| "第一報: 11.9秒/幾何中心法/預估規模5.0" |
| ], |
| "image": "slides/ppt2/slide_11.png" |
| }, |
| { |
| "slideNumber": 12, |
| "title": "", |
| "text": [ |
| "網頁版面簡介", |
| "地震基本資訊展示", |
| "地震預警解統整表格", |
| "應用圖資：", |
| "預警震央地圖 / 算解誤差 / 測站觸發率等", |
| "選擇地震 - 表格資料同步", |
| "地震報告" |
| ], |
| "image": "slides/ppt2/slide_12.png" |
| }, |
| { |
| "slideNumber": 13, |
| "title": "", |
| "text": [ |
| "前月範例：0501東北外海地震M6.1" |
| ], |
| "image": "slides/ppt2/slide_13.png" |
| }, |
| { |
| "slideNumber": 14, |
| "title": "", |
| "text": [ |
| "Part2. 分析：預警結果 / 地震報告(P file)比對", |
| "Part3. 應用：應變地圖 (最快解解算完後S波到達時間)", |
| "應變盲區：最快解解算花費時間 * S波平均波速(3.5km/s)" |
| ], |
| "image": "slides/ppt2/slide_14.png" |
| }, |
| { |
| "slideNumber": 15, |
| "title": "", |
| "text": [ |
| "Part4. 應用：測站觸發率", |
| "(測站列表重複計算共站，故觸發比率偏低)", |
| "以該解內 最遠測站 與 速報震央 為半徑做圓，", |
| "尋找該圓內所有測站的測站觸發率" |
| ], |
| "image": "slides/ppt2/slide_15.png" |
| }, |
| { |
| "slideNumber": 16, |
| "title": "", |
| "text": [ |
| "16", |
| "Part5. 應用：預估震度", |
| "第1報 發布範圍", |
| "第2報 發布範圍" |
| ], |
| "image": "slides/ppt2/slide_16.png" |
| }, |
| { |
| "slideNumber": 17, |
| "title": "", |
| "text": [ |
| "5月地震預警時效最快約9.7秒。", |
| "5月預警產品對外發布次數，PWS 6次、TV 6次，APP及中小學軟體11次。", |
| "總結" |
| ], |
| "image": "slides/ppt2/slide_17.png" |
| } |
| ] |
| }, |
| { |
| "id": "ppt3", |
| "title": "AI 代理群與大型語言模型在次世代地震測報之整合與應用", |
| "slidesCount": 71, |
| "slides": [ |
| { |
| "slideNumber": 1, |
| "title": "AI 代理群與大型語言模型在次世代地震測報之整合與應用", |
| "text": [ |
| "陳達毅 科長", |
| "中央氣象署 地震測報中心", |
| "2026年臺北市立大學資訊科學系演講" |
| ], |
| "image": "slides/ppt3/slide_1.png" |
| }, |
| { |
| "slideNumber": 2, |
| "title": "在地震中心工作面臨的挑戰", |
| "text": [ |
| "2" |
| ], |
| "image": "slides/ppt3/slide_2.png" |
| }, |
| { |
| "slideNumber": 3, |
| "title": "競速開始", |
| "text": [ |
| "3", |
| "1999.9.21集集地震", |
| "2024.4.3花蓮地震" |
| ], |
| "image": "slides/ppt3/slide_3.png" |
| }, |
| { |
| "slideNumber": 4, |
| "title": "臺灣的地震環境與風險 – 過去10年災害地震", |
| "text": [ |
| "4", |
| "臺南維冠大樓 (自由時報)", |
| "2016/2/6 M6.6美濃地震(7級)", |
| "花蓮統帥飯店 (中央通訊社)", |
| "2018/2/6 M6.2花蓮地震(7級)", |
| "2024/4/3 M7.1花蓮地震(6強)", |
| "2022/9/18 M6.8池上地震(6強)", |
| "東里車站鐵道 (經濟部地礦中心)", |
| "臺南市楠西國小 (公視新聞網)", |
| "臺北市大樓 (端傳媒)", |
| "花蓮天王星大樓 (自由時報)", |
| "621人員傷亡、749戶房屋損失", |
| "308人員傷亡、 195戶房屋損失", |
| "108人員傷亡、 34戶房屋損失", |
| "1069人員傷亡、207戶房屋損失", |
| "2025/1/21 M6.4大埔地震(6弱)", |
| "2019/4/18 M6.3花蓮地震(7級)", |
| "29人員傷亡", |
| "1810戶房屋損失" |
| ], |
| "image": "slides/ppt3/slide_4.png" |
| }, |
| { |
| "slideNumber": 5, |
| "title": "臺灣的地震環境與風險", |
| "text": [ |
| "5", |
| "菲律賓海板塊與歐亞大陸板塊每年約以7至8公分的速度聚合，兩板塊持續擠壓碰撞導致臺灣地震頻繁發生。", |
| "臺灣東部海域菲律賓海板塊向北隱沒到歐亞大陸板塊下，南部海域歐亞大陸板塊向東隱沒到菲律賓海板塊下。" |
| ], |
| "image": "slides/ppt3/slide_5.png" |
| }, |
| { |
| "slideNumber": 6, |
| "title": "臺灣的地震環境與風險", |
| "text": [ |
| "6", |
| "1900年以來共發生7次大規模災害地震", |
| "(罹難百人以上) (規模以ML表示)", |
| "罹難人數", |
| "❶", |
| "❷", |
| "❸", |
| "❹", |
| "❺", |
| "❻", |
| "❼", |
| "每天 100個地震", |
| "每年 100個顯著有感地震", |
| "每30~40年 1個大規模災害性地震" |
| ], |
| "image": "slides/ppt3/slide_6.png" |
| }, |
| { |
| "slideNumber": 7, |
| "title": "近年來嘉南地區災害性地震", |
| "text": [ |
| "7", |
| "2010, March, 4th M6.3 JiaXian Earthquake", |
| "(Wu et al., 2011)" |
| ], |
| "image": "slides/ppt3/slide_7.png" |
| }, |
| { |
| "slideNumber": 8, |
| "title": "近年來嘉南地區災害性地震", |
| "text": [ |
| "8", |
| "2016, February, 6th M6.6 Meinong Earthquake", |
| "(Kanamori et al., 2017)" |
| ], |
| "image": "slides/ppt3/slide_8.png" |
| }, |
| { |
| "slideNumber": 9, |
| "title": "近年來嘉南地區災害性地震", |
| "text": [ |
| "9", |
| "2025, January, 21st M6.4 Dapu Earthquake", |
| "(Su et al., 2025)" |
| ], |
| "image": "slides/ppt3/slide_9.png" |
| }, |
| { |
| "slideNumber": 10, |
| "title": "地震測報中心工作環境", |
| "text": [ |
| "10", |
| "員工人數約60人，24小時作業，全年無休" |
| ], |
| "image": "slides/ppt3/slide_10.png" |
| }, |
| { |
| "slideNumber": 11, |
| "title": "地震測報中心工作環境", |
| "text": [ |
| "11", |
| "員工人數約60人，24小時作業，全年無休" |
| ], |
| "image": "slides/ppt3/slide_11.png" |
| }, |
| { |
| "slideNumber": 12, |
| "title": "地震監測畫面", |
| "text": [ |
| "12", |
| "地震發生時值班同仁可以立刻掌握相關資訊" |
| ], |
| "image": "slides/ppt3/slide_12.png" |
| }, |
| { |
| "slideNumber": 13, |
| "title": "即時地震觀測網-巨量資料", |
| "text": [ |
| "13", |
| "約有3000個觀測頻道，每個頻道接收100H取樣率資料" |
| ], |
| "image": "slides/ppt3/slide_13.png" |
| }, |
| { |
| "slideNumber": 14, |
| "title": "地震監測系統架構", |
| "text": [ |
| "14" |
| ], |
| "image": "slides/ppt3/slide_14.png" |
| }, |
| { |
| "slideNumber": 15, |
| "title": "地震預警系統架構", |
| "text": [ |
| "15", |
| "CWA EEW System is available in Docker Hub :", |
| "docker pull cwadayi/earthworm_ubuntu22.04_eew:v1", |
| "透過獨立運作的模組與共享記憶體，建構地震資料處理系統" |
| ], |
| "image": "slides/ppt3/slide_15.png" |
| }, |
| { |
| "slideNumber": 16, |
| "title": "地震預警系統演算邏輯", |
| "text": [ |
| "16", |
| "偵測地震Ｐ波 >> 分群 >> 地震定位 >> 計算規模 >> 預估各地震度", |
| "(Chen et al., 2019)" |
| ], |
| "image": "slides/ppt3/slide_16.png" |
| }, |
| { |
| "slideNumber": 17, |
| "title": "", |
| "text": [ |
| "17", |
| "科技極限與複合型災害 – 誤報", |
| "115年2月23日 馬來西亞婆羅洲外海地震導致誤發中小學EEW" |
| ], |
| "image": "slides/ppt3/slide_17.png" |
| }, |
| { |
| "slideNumber": 18, |
| "title": "地震預警系統演算邏輯", |
| "text": [ |
| "18", |
| "採用機器學習模型預估震度" |
| ], |
| "image": "slides/ppt3/slide_18.png" |
| }, |
| { |
| "slideNumber": 19, |
| "title": "地震預警系統演算邏輯", |
| "text": [ |
| "19", |
| "採用機器學習模型預估震度", |
| "Epicenter", |
| "Use 3 sec P wave", |
| "Have 15 sec leading time", |
| "Use an AI Model", |
| "Provide Warnings !!!" |
| ], |
| "image": "slides/ppt3/slide_19.png" |
| }, |
| { |
| "slideNumber": 20, |
| "title": "地震預警系統演算邏輯", |
| "text": [ |
| "20", |
| "機器學習模型預估震度—實際案例" |
| ], |
| "image": "slides/ppt3/slide_20.png" |
| }, |
| { |
| "slideNumber": 21, |
| "title": "利用Grafana建構監控系統", |
| "text": [ |
| "21", |
| "Open-source platform for data visualization.", |
| "Create dynamic dashboards from many sources.", |
| "Supports diverse databases and data types.", |
| "Real-time monitoring of metrics and logs.", |
| "Customizable panels and query builders.", |
| "Alerting capabilities for threshold breaches.", |
| "Interactive graphs and data exploration.", |
| "Community-driven, highly extensible.", |
| "Visualize time-series and operational data.", |
| "Enhances observability across systems." |
| ], |
| "image": "slides/ppt3/slide_21.png" |
| }, |
| { |
| "slideNumber": 22, |
| "title": "利用Grafana建構監控系統", |
| "text": [ |
| "22", |
| "CWA OBS network", |
| "即時監控海底地震觀測網水壓計資料" |
| ], |
| "image": "slides/ppt3/slide_22.png" |
| }, |
| { |
| "slideNumber": 23, |
| "title": "利用Grafana建構監控系統", |
| "text": [ |
| "23", |
| "即時監控地震觀測站資料延遲狀況" |
| ], |
| "image": "slides/ppt3/slide_23.png" |
| }, |
| { |
| "slideNumber": 24, |
| "title": "利用Grafana建構監控系統", |
| "text": [ |
| "24", |
| "即時監控地震預警系統發布情形" |
| ], |
| "image": "slides/ppt3/slide_24.png" |
| }, |
| { |
| "slideNumber": 25, |
| "title": "利用Grafana建構監控系統", |
| "text": [ |
| "25", |
| "即時展示特定地震站地震波形" |
| ], |
| "image": "slides/ppt3/slide_25.png" |
| }, |
| { |
| "slideNumber": 26, |
| "title": "利用即時逐秒觀測震度輔助地震預警系統", |
| "text": [ |
| "26", |
| "地震災害必須同時考慮強度與持續時間", |
| "First EEW alert", |
| "Second EEW alert", |
| "2024, April 3rd" |
| ], |
| "image": "slides/ppt3/slide_26.png" |
| }, |
| { |
| "slideNumber": 27, |
| "title": "利用即時逐秒觀測震度輔助地震預警系統", |
| "text": [ |
| "27", |
| "利用地震發生初期的觀測震度分佈評估是否該發布警報" |
| ], |
| "image": "slides/ppt3/slide_27.png" |
| }, |
| { |
| "slideNumber": 28, |
| "title": "利用即時逐秒觀測震度輔助地震預警系統", |
| "text": [ |
| "28", |
| "利用地震發生初期的觀測震度分佈評估是否該發布警報" |
| ], |
| "image": "slides/ppt3/slide_28.png" |
| }, |
| { |
| "slideNumber": 29, |
| "title": "利用即時逐秒觀測震度輔助地震預警系統", |
| "text": [ |
| "29", |
| "利用地震發生初期的觀測震度分佈評估是否該發布警報" |
| ], |
| "image": "slides/ppt3/slide_29.png" |
| }, |
| { |
| "slideNumber": 30, |
| "title": "利用即時逐秒觀測震度輔助地震預警系統", |
| "text": [ |
| "30" |
| ], |
| "image": "slides/ppt3/slide_30.png" |
| }, |
| { |
| "slideNumber": 31, |
| "title": "利用即時逐秒觀測震度輔助地震預警系統", |
| "text": [ |
| "31" |
| ], |
| "image": "slides/ppt3/slide_31.png" |
| }, |
| { |
| "slideNumber": 32, |
| "title": "", |
| "text": [ |
| "基礎地震學研究發表", |
| "探討自監督學習在構建地震預訓練模型之有效性", |
| "大型地震模型 (LEM)：\u000b 地震監測的新紀元" |
| ], |
| "image": "slides/ppt3/slide_32.png" |
| }, |
| { |
| "slideNumber": 33, |
| "title": "", |
| "text": [ |
| "傳統演算法於自動辨識地震波相上有大量參數需調整，且準確性難以提升", |
| "背景與緣起" |
| ], |
| "image": "slides/ppt3/slide_33.png" |
| }, |
| { |
| "slideNumber": 34, |
| "title": "", |
| "text": [ |
| "背景與緣起", |
| "近年來機器學習技術用於波相辨識有極大的突破" |
| ], |
| "image": "slides/ppt3/slide_34.png" |
| }, |
| { |
| "slideNumber": 35, |
| "title": "", |
| "text": [ |
| "背景與緣起", |
| "近年來機器學習技術用於波相辨識有極大的突破" |
| ], |
| "image": "slides/ppt3/slide_35.png" |
| }, |
| { |
| "slideNumber": 36, |
| "title": "", |
| "text": [ |
| "背景與緣起", |
| "近年來機器學習技術用於波相辨識有極大的突破" |
| ], |
| "image": "slides/ppt3/slide_36.png" |
| }, |
| { |
| "slideNumber": 37, |
| "title": "", |
| "text": [ |
| "資料來源", |
| "中央氣象署地震觀測網", |
| "CWBSN+ real-time TSMIP", |
| "TSMIP", |
| "(550)" |
| ], |
| "image": "slides/ppt3/slide_37.png" |
| }, |
| { |
| "slideNumber": 38, |
| "title": "", |
| "text": [ |
| "資料來源", |
| "中央氣象署地震觀測網", |
| "CWBSN+ real-time TSMIP", |
| "TSMIP", |
| "(550)" |
| ], |
| "image": "slides/ppt3/slide_38.png" |
| }, |
| { |
| "slideNumber": 39, |
| "title": "", |
| "text": [ |
| "資料來源與資料前處理", |
| "人工標記P波與S波到時" |
| ], |
| "image": "slides/ppt3/slide_39.png" |
| }, |
| { |
| "slideNumber": 40, |
| "title": "", |
| "text": [ |
| "資料來源與資料前處理", |
| "資料擴增，增加資料集的多樣性" |
| ], |
| "image": "slides/ppt3/slide_40.png" |
| }, |
| { |
| "slideNumber": 41, |
| "title": "", |
| "text": [ |
| "地震預警是災害減災的核心組件。隨著深度學習 (Deep Learning) 的發展，許多研究已成功應用神經網路於監測任務中。", |
| "傳統瓶頸： 深度學習需要海量標註數據。", |
| "資源浪費： 地震台站產生大量數據，但多數未經標註。", |
| "新技術： 自監督學習能從未標註資料中提取特徵。", |
| "地震監測的挑戰與機遇" |
| ], |
| "image": "slides/ppt3/slide_41.png" |
| }, |
| { |
| "slideNumber": 42, |
| "title": "", |
| "text": [ |
| "無標註學習", |
| "無需人工標記正確答案，直接利用數據本身的結構進行訓練。", |
| "通用表示", |
| "學習具有信息量且通用的特徵表示 (Representations)。", |
| "高效微調", |
| "只需少量任務導向數據即可達到 State-of-the-art 性能。", |
| "自監督學習 (SSL) 的核心優勢" |
| ], |
| "image": "slides/ppt3/slide_42.png" |
| }, |
| { |
| "slideNumber": 43, |
| "title": "", |
| "text": [ |
| "LEM 延續了 Wav2Vec 2.0 在語音處理上的成功，將其核心思想遷移至地震波形分析。", |
| "SeisWav2Vec 2.0 是一個基於對比學習 (Contrastive Learning) 的自監督模型，旨在區分正確樣本與干擾項。", |
| "從語音到地震：SeisWav2Vec 2.0" |
| ], |
| "image": "slides/ppt3/slide_43.png" |
| }, |
| { |
| "slideNumber": 44, |
| "title": "", |
| "text": [ |
| "特徵編碼器", |
| "將原始波形轉換為連續 Embedding，捕捉信號的局部結構。", |
| "上下文編碼器", |
| "內含 12 層 Transformer，引導向量學習全局上下文資訊。", |
| "遮罩與量化", |
| "離散化特徵並進行遮罩預測任務，模仿自然語言處理的學習方式。", |
| "核心組件：模型內部的運作邏輯" |
| ], |
| "image": "slides/ppt3/slide_44.png" |
| }, |
| { |
| "slideNumber": 45, |
| "title": "", |
| "text": [ |
| "工作流程：從預訓練到任務微調", |
| "在預訓練之後接上一個任務導向的 CNN 解碼器(Decoder)，並採用三種策略 進行微調" |
| ], |
| "image": "slides/ppt3/slide_45.png" |
| }, |
| { |
| "slideNumber": 46, |
| "title": "", |
| "text": [ |
| "在預訓練之後接上一個任務導向的 CNN 解碼器(Decoder)，並採用三種策略進行微調" |
| ], |
| "image": "slides/ppt3/slide_46.png" |
| }, |
| { |
| "slideNumber": 47, |
| "title": "", |
| "text": [ |
| "性能指標與效率提升預測" |
| ], |
| "image": "slides/ppt3/slide_47.png" |
| }, |
| { |
| "slideNumber": 48, |
| "title": "", |
| "text": [ |
| "MEETING ARTIFACTS", |
| "EEW 多代理協作\u000b 技術戰略簡報", |
| "整合地震監控、自動化派遣與科研分析的閉環系統" |
| ], |
| "image": "slides/ppt3/slide_48.png" |
| }, |
| { |
| "slideNumber": 49, |
| "title": "", |
| "text": [ |
| "Agent Matrix", |
| "核心代理角色與職責" |
| ], |
| "image": "slides/ppt3/slide_49.png" |
| }, |
| { |
| "slideNumber": 50, |
| "title": "", |
| "text": [ |
| "my_agent", |
| "預警總管\u000b 維護監控 Cron、異常推播與日報總結。", |
| "secondary", |
| "整合工程師\u000b 負責 Hermes 鏈路轉發與跨代理任務派遣。", |
| "seismo_agent", |
| "地震學助教\u000b 解析 .rep、效能延遲量測與自動繪圖。", |
| "seismo", |
| "科學研究員\u000b 專注地震數據建模、實驗設計與趨勢預測。", |
| "四位 Agent 的技術定位" |
| ], |
| "image": "slides/ppt3/slide_50.png" |
| }, |
| { |
| "slideNumber": 51, |
| "title": "", |
| "text": [ |
| "監控面 Cron: 每 30 分鐘執行健康檢查。", |
| "Log 判讀: 異常時推送最後 50 行日誌至 Telegram。", |
| "即時警報: 確保 EEW .rep 及時更新觸發分析鏈。", |
| "摘要統整: 負責所有 Agent 回報後的彙編。", |
| "my_agent: 監控與警報核心" |
| ], |
| "image": "slides/ppt3/slide_51.png" |
| }, |
| { |
| "slideNumber": 52, |
| "title": "", |
| "text": [ |
| "標準化架構", |
| "完成四個 Agent 的啟動腳本、模型設定與環境隔離。利用 delegate_task 實現跨 Agent 互叫機制。", |
| "路由分發", |
| "開發 seismic-conductor 模組，將結構化數據導向 seismo_agent 進行後續分析。", |
| "secondary_agent: 自動化調度中心" |
| ], |
| "image": "slides/ppt3/slide_52.png" |
| }, |
| { |
| "slideNumber": 53, |
| "title": "", |
| "text": [ |
| "seismo_agent:\u000b 數據科學流水線", |
| ".rep 轉換: 原始檔轉為 CSV/JSON。", |
| "圖表產出: 自動生成散佈與序列圖。", |
| "雙重存檔: 同步保存二進位原檔與數據。" |
| ], |
| "image": "slides/ppt3/slide_53.png" |
| }, |
| { |
| "slideNumber": 54, |
| "title": "", |
| "text": [ |
| "事件觸發", |
| "偵測 .rep 更新", |
| "數據派遣", |
| "指派跨節點任務", |
| "科學繪圖", |
| "生成事件散佈圖表", |
| "研究建模", |
| "進行長期趨勢預測", |
| "彙報發送", |
| "產出日報並推播", |
| "EEW 自動化閉環工作流" |
| ], |
| "image": "slides/ppt3/slide_54.png" |
| }, |
| { |
| "slideNumber": 55, |
| "title": "", |
| "text": [ |
| "資料來源：Seismo-Agent 延遲統計模組", |
| "處理階段耗時分析 (ms)" |
| ], |
| "image": "slides/ppt3/slide_55.png" |
| }, |
| { |
| "slideNumber": 56, |
| "title": "", |
| "text": [ |
| "科學研究驅動開發", |
| "針對「密集小震群」與「深部無感地震」建立統計模型。透過長期趨勢分析，為預警閾值提供動態調整建議。", |
| "定期產出科學驗證報告", |
| "學術論文數據鏈路追蹤", |
| "seismo: 科學研究與數據建模" |
| ], |
| "image": "slides/ppt3/slide_56.png" |
| }, |
| { |
| "slideNumber": 57, |
| "title": "", |
| "text": [ |
| "eew_report_daily 整合規劃" |
| ], |
| "image": "slides/ppt3/slide_57.png" |
| }, |
| { |
| "slideNumber": 58, |
| "title": "", |
| "text": [], |
| "image": "slides/ppt3/slide_58.png" |
| }, |
| { |
| "slideNumber": 59, |
| "title": "", |
| "text": [ |
| "Hermes Agent 團隊會議", |
| "EEW 日常交辦會議記錄", |
| "日期：2026-06-03", |
| "主持：地震總管（主助理）", |
| "出席：my_agent、seismo、seismo_agent、secondary_agent" |
| ], |
| "image": "slides/ppt3/slide_59.png" |
| }, |
| { |
| "slideNumber": 60, |
| "title": "", |
| "text": [ |
| "會議議程", |
| "1️⃣ 確認各 agent 職責與角色", |
| "2️⃣ 每日 EEW 工作項目交辦", |
| "3️⃣ 回報機制與時程", |
| "4️⃣ 後續優化建議與行動方案", |
| "5️⃣ Q&A / 討論事項" |
| ], |
| "image": "slides/ppt3/slide_60.png" |
| }, |
| { |
| "slideNumber": 61, |
| "title": "", |
| "text": [ |
| "## Agenda 01｜my_agent", |
| "my_agent — 地震預警總管", |
| "角色定位：監控 Earthworm EEW 流程、分析 .rep 事件、執行延遲量測並主動推播異常與摘要。", |
| "監控 Telegram 指令與異常告警，接收 eew_health 與 eew_report_daily 輸出。", |
| "判斷 .rep 事件是否需進一步處置（延遲異常、遺漏事件、格式錯誤）。", |
| "協調 seismo、seismo_agent、secondary_agent 的任務分派，並彙整每日摘要。", |
| "Agenda 第 1 項 / 共 4 項" |
| ], |
| "image": "slides/ppt3/slide_61.png" |
| }, |
| { |
| "slideNumber": 62, |
| "title": "", |
| "text": [ |
| "## Agenda 02｜seismo", |
| "seismo — 地震資料處理工程師 (heartbeat / forwarder)", |
| "角色定位：負責 .rep 事件資料結構化轉出，維持 heartbeat loop 穩定運作。", |
| "從 /home/ubuntu/EEW/sysop/params/*.rep 擷取最新地震事件資料。", |
| "持續將 .rep 事件結構化轉出，供 my_agent 與 secondary_agent 後續處理。", |
| "維持 heartbeat loop 穩定運作，異常時回報 my_agent。", |
| "Agenda 第 2 項 / 共 4 項" |
| ], |
| "image": "slides/ppt3/slide_62.png" |
| }, |
| { |
| "slideNumber": 63, |
| "title": "", |
| "text": [ |
| "## Agenda 03｜seismo_agent", |
| "seismo_agent — seismology assistant（原始設計）", |
| "角色定位：管理 EEW Docker 容器健康、恢復流程與 .rep 事件圖表。", |
| "管理 Earthworm EEW 容器健康檢查（eew_health cron）。", |
| "異常時執行 eew_fault_recovery.sh 自動處置", |
| "產出 .rep 事件圖表與日報摘要供參考。", |
| "Agenda 第 3 項 / 共 4 項" |
| ], |
| "image": "slides/ppt3/slide_63.png" |
| }, |
| { |
| "slideNumber": 64, |
| "title": "", |
| "text": [ |
| "## Agenda 04｜secondary_agent", |
| "secondary_agent — 系統整合工程師", |
| "角色定位：在本地環境整合、測試並自動化 Hermes 流程，協調多代理間的訊息轉發與任務派遣。", |
| "彙整各 agent 回報，產生團隊每日工作記錄（artifacts/）。", |
| "負責 host-side 腳本維護（eew_fault_recovery.sh 等）。", |
| "統籌 cron 配置與 Telegram 推播流程，回報異常給 my_agent。", |
| "Agenda 第 4 項 / 共 4 項" |
| ], |
| "image": "slides/ppt3/slide_64.png" |
| }, |
| { |
| "slideNumber": 65, |
| "title": "", |
| "text": [ |
| "每日回報機制時程表" |
| ], |
| "image": "slides/ppt3/slide_65.png" |
| }, |
| { |
| "slideNumber": 66, |
| "title": "", |
| "text": [ |
| "後續行動方案" |
| ], |
| "image": "slides/ppt3/slide_66.png" |
| }, |
| { |
| "slideNumber": 67, |
| "title": "", |
| "text": [ |
| "67" |
| ], |
| "image": "slides/ppt3/slide_67.png" |
| }, |
| { |
| "slideNumber": 68, |
| "title": "", |
| "text": [ |
| "68" |
| ], |
| "image": "slides/ppt3/slide_68.png" |
| }, |
| { |
| "slideNumber": 69, |
| "title": "", |
| "text": [ |
| "AI × WORKFLOW · 2022 → 2026", |
| "用 2026 年人類思維的方式", |
| "去思考每一項工作", |
| "2022 – 2024", |
| "對話時代", |
| "問它、貼上、執行", |
| "ChatGPT 登場，寫程式、查指令、翻譯、寫英文信都用「問」的。", |
| "產出仍需自己手動複製、貼上、在本機執行。", |
| "AI 是助理，人類是手腳。", |
| "2024 – 2025", |
| "整合介面", |
| "Agent 進入 IDE", |
| "Cursor、Windsurf、GitHub Copilot Agent Mode 等 AI 原生編輯器登場。", |
| "能直接改多檔程式、執行終端機指令、跑測試、部署到雲端。", |
| "AI 開始接管「執行」那一段工作。", |
| "2025 – 2026", |
| "自主 Agent", |
| "任務交給它跑完", |
| "模型能力持續增強，事情愈做愈好。", |
| "專門化 agent 出現：OpenClaw（訊息介面）、Hermes Agent（自學技能）、Google Antigravity（agent-first IDE）。", |
| "Claude Code 等 CLI agent 功能愈來愈強大，能獨立完成多步驟任務。", |
| "BASE", |
| "VS Code ── 貫穿三個階段的基座平台:Copilot Chat 住在這、Cursor / Windsurf / Antigravity 都是它的 fork。", |
| "思維轉換:別再問「我要怎麼做這件事」,改問「要交給哪個 agent、怎麼驗收?」" |
| ], |
| "image": "slides/ppt3/slide_69.png" |
| }, |
| { |
| "slideNumber": 70, |
| "title": "地生系同學作業分享-hugging face space", |
| "text": [ |
| "70", |
| "https://huggingface.co/spaces/Sapphirejimmy/seismology_HW10" |
| ], |
| "image": "slides/ppt3/slide_70.png" |
| }, |
| { |
| "slideNumber": 71, |
| "title": "", |
| "text": [ |
| "71", |
| "謝謝聆聽，敬請指教", |
| "26" |
| ], |
| "image": "slides/ppt3/slide_71.png" |
| } |
| ] |
| }, |
| { |
| "id": "ppt4", |
| "title": "臺灣地震預警系統的演進與發展 (嘉義災防宣導)", |
| "slidesCount": 50, |
| "slides": [ |
| { |
| "slideNumber": 1, |
| "title": "臺灣地震預警系統的演進與發展", |
| "text": [ |
| "陳達毅 科長", |
| "中央氣象署 地震測報中心", |
| "嘉義縣政府115度", |
| "天然災害停止上班及上課通報作業講習" |
| ], |
| "image": "slides/ppt4/slide_1.png" |
| }, |
| { |
| "slideNumber": 2, |
| "title": "在地震中心工作面臨的挑戰", |
| "text": [ |
| "2" |
| ], |
| "image": "slides/ppt4/slide_2.png" |
| }, |
| { |
| "slideNumber": 3, |
| "title": "競速開始", |
| "text": [ |
| "3", |
| "1999.9.21集集地震", |
| "2024.4.3花蓮地震" |
| ], |
| "image": "slides/ppt4/slide_3.png" |
| }, |
| { |
| "slideNumber": 4, |
| "title": "競速開始", |
| "text": [ |
| "4", |
| "2024.4.3花蓮地震" |
| ], |
| "image": "slides/ppt4/slide_4.png" |
| }, |
| { |
| "slideNumber": 5, |
| "title": "利用即時逐秒觀測震度輔助地震預警系統", |
| "text": [ |
| "5", |
| "地震災害必須同時考慮強度與持續時間", |
| "First EEW alert", |
| "Second EEW alert", |
| "2024.4.3花蓮地震" |
| ], |
| "image": "slides/ppt4/slide_5.png" |
| }, |
| { |
| "slideNumber": 6, |
| "title": "臺灣的地震環境與風險", |
| "text": [ |
| "6", |
| "菲律賓海板塊與歐亞大陸板塊每年約以7至8公分的速度聚合，兩板塊持續擠壓碰撞導致臺灣地震頻繁發生。", |
| "臺灣東部海域菲律賓海板塊向北隱沒到歐亞大陸板塊下，南部海域歐亞大陸板塊向東隱沒到菲律賓海板塊下。" |
| ], |
| "image": "slides/ppt4/slide_6.png" |
| }, |
| { |
| "slideNumber": 7, |
| "title": "臺灣的地震環境與風險-GNSS絕對水平速度場", |
| "text": [ |
| "7", |
| "菲律賓海板塊與歐亞大陸板塊每年約以7至8公分的速度聚合，兩板塊持續擠壓碰撞導致臺灣地震頻繁發生。" |
| ], |
| "image": "slides/ppt4/slide_7.png" |
| }, |
| { |
| "slideNumber": 8, |
| "title": "臺灣的地震環境與風險", |
| "text": [ |
| "8", |
| "1900年以來共發生7次大規模災害地震", |
| "(罹難百人以上) (規模以ML表示)", |
| "罹難人數", |
| "❶", |
| "❷", |
| "❸", |
| "❹", |
| "❺", |
| "❻", |
| "❼", |
| "每天 100個地震", |
| "每年 100個顯著有感地震", |
| "每30~40年 1個大規模災害性地震" |
| ], |
| "image": "slides/ppt4/slide_8.png" |
| }, |
| { |
| "slideNumber": 9, |
| "title": "臺灣的地震環境與風險", |
| "text": [ |
| "9" |
| ], |
| "image": "slides/ppt4/slide_9.png" |
| }, |
| { |
| "slideNumber": 10, |
| "title": "臺灣的地震環境與風險 – 過去10年災害地震", |
| "text": [ |
| "10", |
| "臺南維冠大樓 (自由時報)", |
| "2016/2/6 M6.6美濃地震(7級)", |
| "花蓮統帥飯店 (中央通訊社)", |
| "2018/2/6 M6.2花蓮地震(7級)", |
| "2024/4/3 M7.1花蓮地震(6強)", |
| "2022/9/18 M6.8池上地震(6強)", |
| "東里車站鐵道 (經濟部地礦中心)", |
| "臺南市楠西國小 (公視新聞網)", |
| "臺北市大樓 (端傳媒)", |
| "花蓮天王星大樓 (自由時報)", |
| "621人員傷亡、749戶房屋損失", |
| "308人員傷亡、 195戶房屋損失", |
| "108人員傷亡、 34戶房屋損失", |
| "1069人員傷亡、207戶房屋損失", |
| "2025/1/21 M6.4大埔地震(6弱)", |
| "2019/4/18 M6.3花蓮地震(7級)", |
| "29人員傷亡", |
| "1810戶房屋損失" |
| ], |
| "image": "slides/ppt4/slide_10.png" |
| }, |
| { |
| "slideNumber": 11, |
| "title": "近年來嘉南地區災害性地震", |
| "text": [ |
| "11", |
| "2010, March, 4th M6.3 JiaXian Earthquake", |
| "(Wu et al., 2011)" |
| ], |
| "image": "slides/ppt4/slide_11.png" |
| }, |
| { |
| "slideNumber": 12, |
| "title": "近年來嘉南地區災害性地震", |
| "text": [ |
| "12", |
| "2016, February, 6th M6.6 Meinong Earthquake", |
| "(Kanamori et al., 2017)" |
| ], |
| "image": "slides/ppt4/slide_12.png" |
| }, |
| { |
| "slideNumber": 13, |
| "title": "近年來嘉南地區災害性地震", |
| "text": [ |
| "13", |
| "2025, January, 21st M6.4 Dapu Earthquake", |
| "(Su et al., 2025)" |
| ], |
| "image": "slides/ppt4/slide_13.png" |
| }, |
| { |
| "slideNumber": 14, |
| "title": "地震測報中心工作環境", |
| "text": [ |
| "14", |
| "員工人數約60人，24小時作業，全年無休" |
| ], |
| "image": "slides/ppt4/slide_14.png" |
| }, |
| { |
| "slideNumber": 15, |
| "title": "地震測報中心工作環境", |
| "text": [ |
| "15", |
| "員工人數約60人，24小時作業，全年無休" |
| ], |
| "image": "slides/ppt4/slide_15.png" |
| }, |
| { |
| "slideNumber": 16, |
| "title": "地震監測畫面", |
| "text": [ |
| "16", |
| "地震發生時值班同仁可以立刻掌握相關資訊" |
| ], |
| "image": "slides/ppt4/slide_16.png" |
| }, |
| { |
| "slideNumber": 17, |
| "title": "即時地震觀測網-巨量資料", |
| "text": [ |
| "17", |
| "約有3000個觀測頻道，每個頻道接收100H取樣率資料" |
| ], |
| "image": "slides/ppt4/slide_17.png" |
| }, |
| { |
| "slideNumber": 18, |
| "title": "強震即時警報原理", |
| "text": [ |
| "地震發生時會同時產生P波(縱波)與S波(橫波)，其中P波傳播速度快震幅小，S波則相反，因此若能藉由P波所提供的資訊推估地震大小並且快速地發送訊息，就有機會於災害性震波抵達前收到地震警報，採取應變。" |
| ], |
| "image": "slides/ppt4/slide_18.png" |
| }, |
| { |
| "slideNumber": 19, |
| "title": "強震即時警報發布條件與管道", |
| "text": [ |
| "113年8月16日 07時35分55秒", |
| "規模6.3 地震位於東部海域", |
| "或規模6震度3級", |
| "地震對高樓層建築物的影響" |
| ], |
| "image": "slides/ppt4/slide_19.png" |
| }, |
| { |
| "slideNumber": 20, |
| "title": "強震即時警報", |
| "text": [ |
| "2024.4.3花蓮地震" |
| ], |
| "image": "slides/ppt4/slide_20.png" |
| }, |
| { |
| "slideNumber": 21, |
| "title": "強震即時警報", |
| "text": [ |
| "2024.4.3花蓮地震", |
| "國家級警報" |
| ], |
| "image": "slides/ppt4/slide_21.png" |
| }, |
| { |
| "slideNumber": 22, |
| "title": "強震即時警報", |
| "text": [ |
| "2024.4.3花蓮地震", |
| "網路推播", |
| "電視臺推播" |
| ], |
| "image": "slides/ppt4/slide_22.png" |
| }, |
| { |
| "slideNumber": 23, |
| "title": "", |
| "text": [ |
| "與地震波賽跑–警報發布時效從 102秒縮短至7秒", |
| "搶在地震波抵達之前，全力爭取每一秒寶貴的應變時間", |
| "臺灣歷經三十年的測站布建、演算法革新與加密觀測，警報發布時效已從 102 秒大幅壓縮至 7 秒，下一階段正邁向 AI 驅動的預警技術與現地型預警系統。", |
| "預警盲區大幅縮小", |
| "發布時效從10秒縮短至7秒，盲區半徑由 35 公里縮減至 25 公里，面積減少逾 50%。當都會區發生地震時，數百萬人口可以提早收到警報。" |
| ], |
| "image": "slides/ppt4/slide_23.png" |
| }, |
| { |
| "slideNumber": 24, |
| "title": "以114年12月27日規模7.0地震為例", |
| "text": [ |
| "24", |
| "國家級警報 第2報", |
| "國家級警報 第1報", |
| "0秒", |
| "20秒", |
| "40秒", |
| "60秒", |
| "80秒", |
| "0秒", |
| "20秒", |
| "40秒", |
| "60秒", |
| "80秒", |
| "科技極限與複合型災害 – 深震" |
| ], |
| "image": "slides/ppt4/slide_24.png" |
| }, |
| { |
| "slideNumber": 25, |
| "title": "", |
| "text": [ |
| "25", |
| "科技極限與複合型災害 – 誤報", |
| "111年9月18日 雲嘉南地區誤發國家級警報" |
| ], |
| "image": "slides/ppt4/slide_25.png" |
| }, |
| { |
| "slideNumber": 26, |
| "title": "", |
| "text": [ |
| "26", |
| "科技極限與複合型災害 – 誤報", |
| "111年9月18日 雲嘉南地區誤發國家級警報" |
| ], |
| "image": "slides/ppt4/slide_26.png" |
| }, |
| { |
| "slideNumber": 27, |
| "title": "", |
| "text": [ |
| "27", |
| "科技極限與複合型災害 – 誤報", |
| "短時間內發生兩個地震造成誤報" |
| ], |
| "image": "slides/ppt4/slide_27.png" |
| }, |
| { |
| "slideNumber": 28, |
| "title": "", |
| "text": [ |
| "28", |
| "科技極限與複合型災害 – 漏報", |
| "短時間內發生兩個地震造成漏報", |
| "兩個地震相差 4 秒，地震預警系統僅處理第一個地震，因此後面大的地震就漏掉了" |
| ], |
| "image": "slides/ppt4/slide_28.png" |
| }, |
| { |
| "slideNumber": 29, |
| "title": "", |
| "text": [ |
| "29", |
| "科技極限與複合型災害 – 漏報", |
| "短時間內發生兩個地震造成漏報", |
| "兩個地震相差 4 秒，地震預警系統僅處理第一個地震，因此後面大的地震就漏掉了" |
| ], |
| "image": "slides/ppt4/slide_29.png" |
| }, |
| { |
| "slideNumber": 30, |
| "title": "科技極限與複合型災害 – 海嘯", |
| "text": [ |
| "30", |
| "2004年南亞海嘯", |
| "(圖片來源：網路照片)", |
| "2011年日本海嘯", |
| "地震規模夠大", |
| "震源深度夠淺", |
| "海床垂直錯動", |
| "(圖片來源：網路照片)" |
| ], |
| "image": "slides/ppt4/slide_30.png" |
| }, |
| { |
| "slideNumber": 31, |
| "title": "科技極限與複合型災害 – 臺灣海嘯威脅", |
| "text": [ |
| "31", |
| "國家科學及技術委員會(2012)研究報告", |
| "臺灣東部 ：琉球海溝", |
| "臺灣東南部：亞普海溝", |
| "臺灣南部 ：馬尼拉海溝" |
| ], |
| "image": "slides/ppt4/slide_31.png" |
| }, |
| { |
| "slideNumber": 32, |
| "title": "臺灣海嘯警戒分區劃分與預估波高分級", |
| "text": [ |
| "海嘯警戒分區：", |
| "根據海嘯威脅可能性與周圍海底地形等因素，並輔以行政區域考量，劃分6個海嘯警戒分區", |
| "32", |
| "預估波高等級：", |
| "參考國際海嘯預警作業，以及臺灣海嘯觀測經驗，制訂為4級", |
| "(114年2月1日修正)", |
| "科技極限與複合型災害 – 海嘯警報作業" |
| ], |
| "image": "slides/ppt4/slide_32.png" |
| }, |
| { |
| "slideNumber": 33, |
| "title": "", |
| "text": [ |
| "33", |
| "年度+序號", |
| "警報報序", |
| "發布警報時間", |
| "說明海嘯地震資訊與提醒沿岸地區提高警戒", |
| "臺灣6個海嘯警戒分區", |
| "預估海嘯波到達時間", |
| "預估海嘯波波高和分級", |
| "海嘯地震資訊", |
| "發震時間", |
| "震央位置", |
| "震源深度", |
| "地震規模", |
| "資料來源", |
| "範例", |
| "科技極限與複合型災害 – 海嘯警報範例" |
| ], |
| "image": "slides/ppt4/slide_33.png" |
| }, |
| { |
| "slideNumber": 34, |
| "title": "", |
| "text": [ |
| "34", |
| "地震", |
| "2024/4/3 07:58:09", |
| "M7.2花蓮地震發生", |
| "海嘯警報第1報 (08:11)", |
| "海嘯警報第2報 (10:00)", |
| "海嘯警報解除報 (11:10)", |
| "地震後時間", |
| "波高與到時", |
| "模擬資料", |
| "氣象署潮位站", |
| "觀測數據", |
| "科技極限與複合型災害 – 海嘯警報實例" |
| ], |
| "image": "slides/ppt4/slide_34.png" |
| }, |
| { |
| "slideNumber": 35, |
| "title": "", |
| "text": [ |
| "35", |
| "科技極限與複合型災害 – 海嘯警報實例", |
| "M8.8", |
| "海嘯消息 (07:38)", |
| "海嘯警報解除(17:10)", |
| "地震後時間", |
| "海嘯消息(08:23)", |
| "海嘯警訊(09:12)", |
| "海嘯警報(11:40)", |
| "波高與到時", |
| "模擬資料", |
| "114年7月30日7時25分 堪察加半島東部外海 規模8.8地震", |
| "2025/7/30 07:25" |
| ], |
| "image": "slides/ppt4/slide_35.png" |
| }, |
| { |
| "slideNumber": 36, |
| "title": "", |
| "text": [ |
| "36", |
| "地震報告", |
| "網路推播", |
| "手機細胞廣播", |
| "電視台插播", |
| "App", |
| "臉書粉絲團", |
| "強震警報", |
| "氣象署官網", |
| "手機簡訊", |
| "LINE Notify", |
| "受限於傳輸能量限制，紅色標註管道僅提供特定使用者", |
| "震度速報", |
| "網路推播", |
| "掌握關鍵應變與整備 – 最後一哩路" |
| ], |
| "image": "slides/ppt4/slide_36.png" |
| }, |
| { |
| "slideNumber": 37, |
| "title": "掌握關鍵應變與整備 – 直送防救災需求單位", |
| "text": [ |
| "37", |
| "師生緊急庇護", |
| "地震 資訊", |
| "~4100所", |
| "高中小學", |
| "公路局、高公局", |
| "~40個", |
| "防救災單位", |
| "臺鐵、高鐵捷運", |
| "地震速報資訊直接發送單位超過 4500個", |
| "(教育部)", |
| "(公路局)", |
| "(臺鐵)", |
| "(NCDR)", |
| "國家防災日演練" |
| ], |
| "image": "slides/ppt4/slide_37.png" |
| }, |
| { |
| "slideNumber": 38, |
| "title": "", |
| "text": [ |
| "38", |
| "掌握關鍵應變與整備" |
| ], |
| "image": "slides/ppt4/slide_38.png" |
| }, |
| { |
| "slideNumber": 39, |
| "title": "掌握關鍵應變與整備 - 防災教育宣導", |
| "text": [ |
| "39" |
| ], |
| "image": "slides/ppt4/slide_39.png" |
| }, |
| { |
| "slideNumber": 40, |
| "title": "掌握關鍵應變與整備 - 防災教育宣導", |
| "text": [ |
| "40" |
| ], |
| "image": "slides/ppt4/slide_40.png" |
| }, |
| { |
| "slideNumber": 41, |
| "title": "掌握關鍵應變與整備 - 防災教育宣導", |
| "text": [ |
| "41" |
| ], |
| "image": "slides/ppt4/slide_41.png" |
| }, |
| { |
| "slideNumber": 42, |
| "title": "– 以114年12月27日規模7.0地震為例，屏東縣竹田鄉履豐村 鄉下長輩沒手機，屏東青年自架「村里地震廣播預警系統」", |
| "text": [ |
| "42", |
| "常見Q&A – 民間業者APP比較快收到警報？" |
| ], |
| "image": "slides/ppt4/slide_42.png" |
| }, |
| { |
| "slideNumber": 43, |
| "title": "地震預警系統演算邏輯", |
| "text": [ |
| "43", |
| "採用機器學習模型預估震度" |
| ], |
| "image": "slides/ppt4/slide_43.png" |
| }, |
| { |
| "slideNumber": 44, |
| "title": "地震預警系統演算邏輯", |
| "text": [ |
| "44", |
| "採用機器學習模型預估震度", |
| "Epicenter", |
| "Use 3 sec P wave", |
| "Have 15 sec leading time", |
| "Use an AI Model", |
| "Provide Warnings !!!" |
| ], |
| "image": "slides/ppt4/slide_44.png" |
| }, |
| { |
| "slideNumber": 45, |
| "title": "地震預警系統演算邏輯", |
| "text": [ |
| "45", |
| "機器學習模型預估震度—實際案例" |
| ], |
| "image": "slides/ppt4/slide_45.png" |
| }, |
| { |
| "slideNumber": 46, |
| "title": "利用即時逐秒觀測震度輔助地震預警系統", |
| "text": [ |
| "46", |
| "地震災害必須同時考慮強度與持續時間", |
| "First EEW alert", |
| "Second EEW alert", |
| "2024, April 3rd" |
| ], |
| "image": "slides/ppt4/slide_46.png" |
| }, |
| { |
| "slideNumber": 47, |
| "title": "利用即時逐秒觀測震度輔助地震預警系統", |
| "text": [ |
| "47", |
| "利用地震發生初期的觀測震度分佈評估是否該發布警報" |
| ], |
| "image": "slides/ppt4/slide_47.png" |
| }, |
| { |
| "slideNumber": 48, |
| "title": "結語 - 持續精進地震速警報效能", |
| "text": [ |
| "48" |
| ], |
| "image": "slides/ppt4/slide_48.png" |
| }, |
| { |
| "slideNumber": 49, |
| "title": "結語 - 持續加強公私協力合作", |
| "text": [ |
| "49" |
| ], |
| "image": "slides/ppt4/slide_49.png" |
| }, |
| { |
| "slideNumber": 50, |
| "title": "", |
| "text": [ |
| "50", |
| "謝謝聆聽，敬請指教", |
| "26" |
| ], |
| "image": "slides/ppt4/slide_50.png" |
| } |
| ] |
| } |
| ]; |
|
|
| if (typeof module !== 'undefined' && module.exports) { |
| module.exports = slidesData; |
| } |
|
|
