大規模崩塌地動訊號自動辨識技術開發與應用

Improvement and Application of Automatic Landslide-quake Identification Technology

林冠瑋(Guan-Wei Lin) ； 李憲昆(Shian-Kuen Lee) ； 張簡意逢(Yi-Feng Chang Chien)

《中華水土保持學報》 51卷3期 (2020/09) Pp. 85-94

https://doi.org/10.29417/JCSWC.202009_51(3).0001

摘要

大規模崩塌產生的地表振動可能會被地震儀記錄下來，開發崩塌訊號的自動分類器將有助於快速的獲得崩塌發生時間。本研究分析了台灣寬頻地震網的連續紀錄，從中獲得214個崩塌訊號。此外，還採用了相等數量的地震和環境噪訊，建立崩塌地動訊號的分類器。透過混淆矩陣進行驗證，顯示出該分類器的正確度可以達到91.3%。進一步將分類器運用於2009年莫拉克颱風和2015年颱風蘇迪勒颱風期間的地動紀錄，最終分類器對崩塌訊號的敏感度達到98%。

關鍵字

大規模崩塌 ； 地動訊號 ； 機器學習 ； 訊號特徵值

並列摘要

Landslide-generated seismic waves (landslide-quakes), exhibiting distinctive waveforms and frequency characteristics, can be recorded by nearby seismometers. Implementing an automatic classifier for landslide-quakes could help provide objective and accurate initiation times of landslides with efficiency. This study collected and analyzed 214 large scale landslide seismic records from the Broadband Array in Taiwan for Seismology (BATS). In addition, equal numbers of earthquake and noise signals were also incorporated. The 642 seismic signals and time information were carefully examined to create an automatic landslide-quake classifier. By validating the signal attributes of the landslide, earthquake, and noise events, specifically in the time and frequency domains, it was shown that the proposed classifier can reach an accuracy of 91.3 %. To further evaluate the applicability of the automatic classifier, landslide-quakes generated during the devastating Typhoon Morakot (2009) and Typhoon Soudelor (2015) were also verified, showing that the sensitivity of the classifier is higher than 98 %.

並列關鍵字

Large scale landslide ； Seismic signal ； Machine learning ； Signal attributers