研究生: |
黃郁洺 Yu-Ming Huang |
---|---|
論文名稱: |
用於多測站預警的地震神經網路框架 SENSE:A Seismic Neural Network Framework for Multiple Stations Early Warning |
指導教授: |
陳冠宇
Kuan-Yu Chen |
口試委員: |
金台齡
Tai-Lin Chin 陳達毅 Da-Yi Chen 吳逸民 Yih-Min Wu |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 資訊工程系 Department of Computer Science and Information Engineering |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 147 |
中文關鍵詞: | 地面震動預測 、地震預警系統 、卷積神經網路 、多測站預警 |
外文關鍵詞: | Ground Motion Prediction, Convolutional Neural Network, Earthquake Early Earning, Multiple Stations Early Warning |
相關次數: | 點閱:262 下載:11 |
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地震預警在降低地震災害風險方面具有重要作用。然而,目前主要的地震預警方法 多半依賴於單一測站的預警,這限制了預警模型對資料的覆蓋範圍和準確性。因此,如 何整合多個測站的資訊進行預警成為一項具有挑戰性的任務。
我們提出了一種全新的用於多測站預警的地震神經網路框架,名為 SENSE。 SENSE 模型採用全測站的輸入資料方法和輔助訓練模組,以提升預警效能。首先,我們 會同時將所有測站的地理及三軸波型資訊輸入模型,進行空間訊息的編碼,並利用前/後 期特定地點向量(Early/Late Locality-Specific Embeddings)來捕捉不同測站之間的特 徵差異,這麼做有助於減少額外訊息的丟失,增加模型的穩定性。最後,我們引入了閘 門機制模組(Gate Mechanism),用以調節波形資訊和地理資訊對預警系統的貢獻程度, 進一步提升模型的效能。
本論文在台灣和日本數據集上進行一系列詳盡的實驗,實驗結果說明我們所提出的 架構與其他最先進的方法相比皆展現了有競爭力或更好的效果,此外,我們還將 SENSE 模型部署上台灣中央氣象局實際運作的系統上進行測試分析,更進一步證明了模型的可 用性。
Earthquake early warning systems play a crucial role in reducing seismic disaster risks. However, the current predominant methods for earthquake early warning rely heav- ily on single station-based alerts, limiting the warning models’ coverage and accuracy. Therefore, integrating information from multiple stations to improve early warning capa- bility is challenging.
We propose a novel framework for a fully station-based earthquake early warning model called a seismic neural network framework for multiple stations early warning (SENSE). The SENSE model improves early warning performance by introducing in- novative data input approaches and auxiliary training modules. First,We will input the geographical and three-axis waveform information of all stations into the model simultane- ously, encoding spatial information. Second, we constructed Early/Late Locality-Specific Embeddings, which capture additional feature variations among stations. Finally, we de- signed a Gate Mechanism module to adjust the contributions of waveform and geograph- ical information to the early warning system.
Extensive experiments are conducted on datasets from Taiwan and Japan in this study. The results demonstrate that our proposed framework exhibits competitive or even supe- rior performance compared to other state-of-the-art methods. Additionally, we deploy the SENSE model on the operational system of the Taiwan Central Weather Bureau for further testing and analysis, providing additional evidence for the model’s usability.
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