A Method of Real-Time Tsunami Detection Using Ensemble Empirical Mode Decomposition
A Method of Real-Time Tsunami Detection Using Ensemble Empirical Mode Decomposition
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DOI：
10.1785/0220200115
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发表时间：
2020-09
影响因子：
3.3
通讯作者：
Yuchen Wang;K. Satake;T. Maeda;M. Shinohara;S. Sakai
中科院分区：
文献类型：
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作者：
Yuchen Wang;K. Satake;T. Maeda;M. Shinohara;S. Sakai
提出了一种基于集合经验模态分解（EEMD）的海啸实时探测方法。EEMD自适应地将时间序列分解为一组固有模态函数。海底压力计（obpg）的海啸信号与潮汐信号、地震信号以及背景噪声自动分离。与传统的海啸探测方法不同，我们的算法不需要对潮汐进行预测。应用于Tokohu海岸电缆obpg的实际数据表明，它成功地检测到2016年福岛地震（7.4级）的海啸。该方法还应用于2011年东北地震（9.0级）的超强海啸和1998年三陆地震（6.4级）的超强海啸。该算法检测到造成毁灭性破坏的前一种大海啸，而未检测到海岸未注意到的后一种微海啸。该算法还对长达一个月的OBPG数据进行了测试，没有产生假警报。因此，该算法对于海啸预警系统非常有用，因为它不需要任何地震信息来检测海啸。它能以较短的时间延迟探测海啸，并能准确表征海啸的振幅。
We propose a method of real-time tsunami detection using ensemble empirical mode decomposition (EEMD). EEMD decomposes the time series into a set of intrinsic mode functions adaptively. The tsunami signals of ocean-bottom pressure gauges (OBPGs) are automatically separated from the tidal signals, seismic signals, as well as background noise. Unlike the traditional tsunami detection methods, our algorithm does not need to make a prediction of tides. The application to the actual data of cabled OBPGs off the Tokohu coast shows that it successfully detects the tsunami from the 2016 Fukushima earthquake (M 7.4). The method was also applied to the extremely large tsunami from the 2011 Tohoku earthquake (M 9.0) and extremely small tsunami from the 1998 Sanriku earthquake (M 6.4). The algorithm detected the former huge tsunami that caused devastating damage, whereas it did not detect the latter microtsunami, which was not noticed on the coast. The algorithm was also tested for month-long OBPG data and caused no false alarm. Therefore, the algorithm is very useful for a tsunami early warning system, as it does not require any earthquake information to detect the tsunamis. It detects the tsunami with a short-time delay and characterizes the tsunami amplitudes accurately.