探討臺灣地震活動特徵與環境變數相關性分析;Investigating the Correlation between the Characteristics of Seismic Activity and Environmental Variables in Taiwan

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題名:	探討臺灣地震活動特徵與環境變數相關性分析;Investigating the Correlation between the Characteristics of Seismic Activity and Environmental Variables in Taiwan
作者:	陳宜和;YI-HO, CHEN
貢獻者:	土木工程學系
關鍵詞:	地震活動;海陸溫差;顯著性差異統計;機器學習;Seismic Activity;LST;SST;Statistical Analysis;Machine Learning
日期:	2023-06-30
上傳時間:	2024-09-19 14:12:33 (UTC+8)
出版者:	國立中央大學
摘要:	由於台灣位於環太平洋火山地震帶上，幾乎每天都有規模不一的地震活動發生。其中，部分地震活動事件進而造成了嚴重的災害，導致個人財產損失、人員傷亡以及損壞重要公共設施。因此，研究地震活動的長期時空機制是了解地震活動成因以及未來預測地震活動從而提早進行相關預防災害措施的關鍵任務。然以往的地震相關研究，主要集中在探討小時空尺度上單一地震事件的誘發成因及背後機制。而在此研究中，使用從1987年至2020年台灣地區及附近海域的有效記錄數據，包括美國地質調查局(USGS)的地震長期記錄事件、中央氣象局(CWB)的每日大氣溫度等環境因素以及美國國家海洋暨大氣總署(NOAA)的每日海洋表面溫度資料，觀察並探討台灣從1987年至2020年間地震事件與大氣溫度及海洋表面溫度間溫差的異常發生頻率以及地震事件的發生與大氣溫度及海洋表面溫度間溫差異常發生的相關性。以便探討因為陸地與海洋比熱的改變所產生的溫差將可能對地表的海陸交界處產生應力，是否會進而誘發地震的發生。結果表明，許多地震事件從地震發生前21日至後7日，常有相較於以往同段時期下的溫差有異常發生，並且在不同的芮氏規模下有其特定的溫差異常趨勢。在芮氏規模2.5到4以及芮氏規模大於6的地震事件中，幾乎所有地震事件與無地震事件有大氣溫度及海洋表面溫度間溫差的顯著異常差異。本研究發現了地震事件與大氣溫度及海洋表面溫度間溫差的異常發生頻率特徵，以及透過顯著性差異統計分析，比較地震事件與無地震事件之間溫差的顯著差異。最後再利用機器學習模型的邏輯迴歸(Logistic regression)、隨機森林(Random forest) 和多層感知機(Multilayer Perceptron, MLP)來識別於不同芮氏規模區間下，是否會有地震事件發生。其中表現最好與最穩定的為MLP分類模型，並且在判斷芮氏規模大於6的區間中，有高達93%的準確率。希冀本研究能為未來地震活動預測提供相關資訊，從而更準確地預防地震活動可能引發的災害。;Since Taiwan is located at the Pacific Ring of Fire, seismic activity of varying magnitudes occurs almost every day. Among them, some of these seismic activities have in turn caused severe disasters, resulting in loss of personal property, casualties and damage to important public facilities. Therefore, investigating the long-term spatiotemporal pattern of seismic activities is a crucial task for understanding the causes of seismic activity and to predict future seismic activity, in order to carry out disaster prevention measures in advance. Previous studies mostly focused on the causes of single seismic events on the small spatiotemporal scale. In this study, the data from 1987 to 2020 are used, including seismic events from the United States Geological Survey (USGS), the ambient environmental factors such as daily air temperature from Taiwan Central Weather Bureau (CWB) and daily sea surface temperature data from National Oceanic and Atmospheric Administration (NOAA). Then the temperature difference between the land surface temperature (LST) and the sea surface temperature (SST) to the correlation between the occurrence of seismic activities and the abnormal occurrence of temperature difference are compared. In order to explore whether the temperature difference caused by the change of the specific heat of the land and the ocean may cause stress on the land-sea junction on the surface, and whether it will trigger seismic activity . The results show that lots of seismic activities often have positive and negative anomalies of temperature difference from twenty-one days before to seven days after the seismic event. Moreover, there is a specific trend of temperature difference anomalies under different magnitude intervals. In the magnitude range of 2.5 to 4 and greater than 6, almost all of the seismic events have significant anomalous differences in the temperature difference between LST and SST compared with no seismic events. This study uncovers anomalous frequency signatures of seismic activities and temperature differences between LST and SST. The significant difference in temperature difference between seismic events and non-seismic events was compared by using statistical analysis. Additionally, the multilayer perceptron, logistic regression and random forest of machine learning model was used to identify whether there will be a seismic event under different magnitude intervals. It is hoped that it can provide relevant information for the prediction of future seismic activity, to more accurately prevent disasters that may be caused by seismic activity.
顯示於類別:	[土木工程研究所] 博碩士論文

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