A Method of Real-Time Tsunami Detection Using Ensemble Empirical Mode Decomposition

Author:

Wang Yuchen1,Satake Kenji1,Maeda Takuto2,Shinohara Masanao1,Sakai Shin’ichi3

Affiliation:

1. Earthquake Research Institute, The University of Tokyo, Tokyo, Japan

2. Graduate School of Science and Technology, Hirosaki University, Aomori, Japan

3. Earthquake Research Institute and Interfaculty Initiative in Information Studies, The, University of Tokyo, Tokyo, Japan

Abstract

Abstract 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.

Publisher

Seismological Society of America (SSA)

Subject

Geophysics

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3