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;
}