利用深度學習方法檢測震前電離層異常

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郭家暉(Chia-Hui Kuo) 查詢紙本館藏

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資訊工程學系

論文名稱

利用深度學習方法檢測震前電離層異常
(Using deep learning to detect pre-earthquake ionospheric anomalies)

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摘要(中)

本篇論文使用深度學習模型 ConvLSTM 來預測具有震前電離層異常之地震，使用的資料為 GIMTEC 公開資料。有別於地震預警系統只能在地震發生前幾秒或是幾分鐘才能收到通知，對於地震發生前的準備，明顯是時間不足的，如果能早在前一天甚至是數天前掌握地震即將到來的資訊，便可以及早預防、疏散，才能夠大幅度減少地震所帶來的災害。為了預測出那些具有明顯的地震電離層異常之規模6以上的地震，有別於使用傳統 LSTM 模型，本篇論文使用之ConvLSTM模型能夠獲得數值圖象二維空間的訊息，相較於LSTM，ConvLSTM 模型能夠更大的利用相鄰幾天的資訊來訓練模型，並得到更可靠的結果。

摘要(英)

In this paper, we use the deep learning model ConvLSTM to detect earthquakes with pre-seismic ionospheric anomalies, using the publicly available GIMTEC data. Unlike earthquake early warning systems that can only receive notifications a few seconds or minutes before an earthquake occurs, it is obviously that there is insufficient time for preparation before an earthquake. If we can obtain information about an impending earthquake a day or even several days in advance, we can take early preventive measures and evacuation, thus significantly reducing the disasters caused by earthquakes. In order to predict earthquakes of magnitude 6 or above with significant ionospheric anomalies, this paper utilizes the ConvLSTM model instead of the traditional LSTM model. The ConvLSTM model can capture spatial information in two-dimensional numerical images, enabling it to better utilize information from adjacent days for training and obtain more reliable results compared to LSTM.

關鍵字(中)

★ 全電子含量
★ 深度學習
★ 地震

關鍵字(英)

★ TEC
★ Deep learning
★ Earthquake

論文目次

摘要 ...................................................... I
ABSTRACT ................................................. II
目錄 .................................................... III
圖目錄 ................................................... IV
表目錄 .................................................... V
第一章緒論 ................................................ 1
1-1 研究動機 ................................................ 1
1-2 地震電離層前兆 .......................................... 2
1-3 相關文獻啟發 ............................................ 3
第二章模型設計與細節 ...................................... 6
2-1 CONVLSTM介紹............................................. 6
2-2 模型架構設計(MODEL ARCHITECTURE DESIGN) ....................... 8
第三章資料與地震判定 ..................................... 10
3-1 GIMTEC資料 ............................................. 10
3-2 電離層異常判定 ......................................... 11
3-3 有地震、無地震的界定.................................... 13
第四章實驗結果與討論 ..................................... 16
4-1 訓練資料、測試資料介紹 .................................. 16
4-2 實驗說明 ............................................... 18
4-3 異常判斷指標 ........................................... 20
4-4 實驗結果 ............................................... 22
第五章結論 ............................................... 28
參考文獻 ................................................. 29

參考文獻

[1]Tsai, T. C., Jhuang, H. K., Ho, Y. Y., Lee, L. C., Su, W. C., Hung, S. L., ... & Kuo, C. L. (2022). Deep learning of detecting ionospheric precursors associated with M≥ 6.0 earthquakes in Taiwan. Earth and Space Science, 9(9), e2022EA002289. [2] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. [3] Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems, 28. [4] Abdu, M. A., Ramkumar, T. K., Batista, I. S., Brum, C. G. M., Takahashi, H., Reinisch, B. W., & Sobral, J. H. A. (2006). Planetary wave signatures in the equatorial atmosphere–ionosphere system, and mesosphere-E-and F-region coupling. Journal of Atmospheric and Solar-Terrestrial Physics, 68(3-5), 509-522. [5] Wu, T. Y., Liu, J. Y., Lin, C. Y., & Chang, L. C. (2020). Response of ionospheric equatorial ionization crests to lunar phase. Geophysical Research Letters, 47(7), e2019GL086862.
30
[6] Liu, J. Y., Chen, Y. I., Pulinets, S. A., Tsai, Y. B., & Chuo, Y. J. (2000). Seismo‐ionospheric signatures prior to M≥ 6.0 Taiwan earthquakes. Geophysical research letters, 27(19), 3113-3116. [7] Liu, J. Y., Chen, Y. I., Chuo, Y. J., & Tsai, H. F. (2001). Variations of ionospheric total electron content during the Chi‐Chi earthquake. Geophysical Research Letters, 28(7), 1383-1386. [8] Liu, J. Y., Chen, Y. I., Chuo, Y. J., & Chen, C. S. (2006). A statistical investigation of preearthquake ionospheric anomaly. Journal of Geophysical Research: Space Physics, 111(A5). [9] Jhuang, H. K., Ho, Y. Y., Kakinami, Y., Liu, J. Y., Oyama, K. I., Parrot, M., ... & Zhang, D. (2010). Seismo-ionospheric anomalies of the GPS-TEC appear before the 12 May 2008 magnitude 8.0 Wenchuan Earthquake. International Journal of Remote Sensing, 31(13), 3579-3587.
[10] GIMTEC Dataset: 取自
https://scidm.nchc.org.tw/dataset/earthquake-and-tsunami
[11] 地震資訊: 取自
https://www.usgs.gov/programs/earthquake-hazards
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[12] Aji, B. A. S., Liong, T. H., & Muslim, B. (2017, October). Detection precursor of sumatra earthquake based on ionospheric total electron content anomalies using N-Model Articial Neural Network. In 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 269-276). IEEE.

指導教授

陳映濃(Ying-Nong Chen)

審核日期

2023-7-27

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