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1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 | 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;
}
|