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研究生: 陳威榤
Wei-Jie Chen
論文名稱: 單站法即時地震波到時標記及強震預警預測模型開發
Development of Real-time Prediction Model for Single-station Earthquake Early Warning and Phase Picking
指導教授: 許丁友
Tim-Yu Hsu
口試委員: 金台鈴
Tai-Lin Chin
陳冠宇
Kuan-Yu Chen
陳達毅
Da-Yi Chen
學位類別: 碩士
Master
系所名稱: 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 102
中文關鍵詞: 現地型強震預警
外文關鍵詞: Recurrent Residual U-Net-Based Multitask Attention
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現地型強震預警系統主要目的在於提前發出精準的警報,讓近震央區域有時間做好預防措施,以減少強震波帶來的傷亡與財損。目前現地型強震預警系統需要先判斷P波到時,之後利用P波到時後之訊號預測PGA,據以對單一測站發出警報。目前判斷P波到時之模型以及預測PGA之模型係互相獨立運作。本研究為開發一模型可以同時處理這兩個問題,並設定在即時系統上運行。本研究以Liao等人所開發之即時標記地震波相模型架構為基礎,開發一可以同時標記地震波相並進行單站法強震預警之模型,並探討在不同輸入地表加速度歷時尺度組合之效果,所考慮的組合包括Z-score、Z-score&0.8gal、Z-score&2.5gal、Z-score&8gal、Z-score&25gal、Z-score&80gal、Z-score&250gal、Z-score&800gal和Z-score&原始加速度歷時。 評估模型效果有四種方式, (一)、評估九個模型對P波到時之準確率、召回率和F1-score,判斷訊號是否為地震訊號之準確率、召回率和F1-score,預測PGA是否大於25gal之準確率、召回率和F1-score,並觀察每個指標之九個模型在P波到時後0.1到3秒間百分比之變化。(二)、觀察九個模型在P波到時後0.1到3秒間對真實PGA在0-8gal和8-25gal之誤報率、真實PGA在25-80gal、80-250gal、250-800gal和高於800gal之漏報率。(三)、綜合考慮地震波形檢測和單站預警之結果是否能降低模型對noise誤報或是對地震訊號漏報。(四)、九個模型對P波到時後0.1到3秒間綜合發報之準確率、召回率和F1-score。最後,本研究選擇Z-score+正負2.5gal並用P波到時後2秒之訊號發報。Z-score&2.5gal預測池上地震觸發測站在無容許誤差下,P波到時後2秒到3秒之整體表現之準確率為89.3%,召回率為38.5%。容許誤差下,P波到時後2秒到3秒之整體表現之準確率為98.7%,召回率為93.7%。


The primary objective of an on-site early warning system for strong earthquakes is to issue accurate alerts in advance, allowing the regions near the epicenter to take precautionary measures to minimize casualties and property damage caused by strong seismic waves. Currently, the on-site early warning system involves two independent processes: determining the arrival time of P-waves and predicting Peak Ground Acceleration (PGA) based on the P-wave arrival time. This study aims to develop a model that can simultaneously address both of these issues and operate in real-time.Built upon the real-time seismic phase identification framework developed by Liao et al., this research establishes a model that can concurrently identify seismic wave phases and perform single-station early earthquake warning. It explores the effectiveness of various combinations of input ground acceleration time series scales, including Z-score, Z-score & 0.8 gal, Z-score & 2.5 gal, Z-score & 8 gal, Z-score & 25 gal, Z-score & 80 gal, Z-score & 250 gal, Z-score & 800 gal, and Z-score & raw acceleration time series.The model's effectiveness is evaluated using four approaches: (1) assessing the accuracy, recall, and F1-score of the nine models for P-wave arrival time, the accuracy, recall, and F1-score of identifying seismic signals, and the accuracy, recall, and F1-score of predicting PGA exceeding 25 gal, observing the percentage variation of the nine models for each indicator between 0.1 and 3 seconds after P-wave arrival time; (2) observing the false positive rates for real PGAs in the ranges of 0-8 gal and 8-25 gal and the false negative rates for real PGAs in the ranges of 25-80 gal, 80-250 gal, 250-800 gal, and above 800 gal between 0.1 and 3 seconds after P-wave arrival time; (3) comprehensively considering whether the combination of seismic waveform detection and single-station warning can reduce noise false alarms or seismic signal missed alarms; and (4) the combined accuracy, recall, and F1-score for issuing alerts between 0.1 and 3 seconds after P-wave arrival time.Ultimately, this study selects Z-score + ±2.5 gal and issues alerts using signals 2 seconds after the P-wave arrival time. Under the Z-score & 2.5 gal prediction, the overall accuracy of the seismic trigger station's performance between 2 and 3 seconds after P-wave arrival time is 89.3%, with a recall rate of 38.5%. With allowable error, the overall accuracy is 98.7%, with a recall rate of 93.7%.

摘要 I ABSTRACT II 誌謝 III 圖目錄 VII 表目錄 XII 第一章 緒論 1 1.1 研究動機與目的 1 1.2 文獻回顧 2 1.3 研究內容與架構 3 第二章 研究方法 4 2.1 深度學習簡介 4 2.2 類神經網路和參數介紹 5 2.2.1 類神經網路 5 2.2.2 梯度下降法 7 2.2.3 激勵函數(Activation Function) 12 2.2.4 損失函數 15 2.2.5 學習曲線與訓練週期 16 2.3 Recurrent Residual U-Net-Based Multitask Attention 架構 19 2.3.1 平衡不同任務間之損失 24 第三章 模型資料集與統計指標 26 3.1 資料來源 26 3.1.1 訓練、驗證資料集 27 3.1.2 測試資料集 30 3.2 模型不同輸入 34 3.3 模型輸出 43 3.4 說明三種目標之二元混淆矩陣 47 3.4.1 地震相位標記(Earthquake Phase Picking)之二元混淆矩陣 48 3.4.2 模型對非地震訊號標記之錯誤率 50 3.4.3 地震波形檢測(Earthquake Waveform Detection)之二元混淆矩陣 50 3.4.4 單站強震預警(On-site Earthquake Early Warning system)之二元混淆矩陣 51 第四章 模型測試結果 53 4.1 不同模型測試結果 53 4.1.1 各別評估三種目標之整體結果 53 4.1.2 不同PGA區間之結果討論 61 4.1.3 地震波形檢測和單站預警綜合評估 66 4.1.4 評估三個模型在P波到時後0.1到3秒之整體發報情形 75 4.2 實際案例 80 第五章 結論與未來展望 85 5.1 結論 85 5.2 未來展望 87

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