• 首页关于本刊投稿须知期刊订阅编委会期刊合作查询检索English
引用本文:
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 26次   下载 22 本文二维码信息
码上扫一扫!
分享到: 微信 更多
集合经验模态分解(EEMD)在地下水位数据处理中的应用初探
余丹, 刘春国, 王晓, 韩雪君, 黄兴辉
中国地震台网中心, 北京 100045
摘要:
使用集合经验模态分解方法将水位观测数据分为高、中、低等3个频率分量。高频分量可以用来识别和研究包含同震响应在内的高频事件;中间频率分量包含固体潮的半日波、全日波信号;低频分量则反映观测数据的长期趋势性变化特征。在此基础上,将该方法应用于张道口-1井和新10井的水位观测分钟值数据,从处理后得到的高频分量中识别出31次7级以上地震的同震响应,定量地分析了其最大振幅随震中距和震级的变化特征。
关键词:  经验模态分解  井水位观测数据  同震响应
DOI:
分类号:P315
基金项目:中国地震局地震科技星火计划项目(XH17049Y)、中国地震台网中心青年科技基金项目(QNJJ201706)共同资助
Application of Ensemble Empirical Mode Decomposition(EEMD)in Underground Fluid Data Processing
Yu Dan, Liu Chunguo, Wang Xiao, Han Xuejun, Huang Xinghui
China Earthquake Networks Center, Beijing 100045, China
Abstract:
The Ensemble Empirical Mode Decomposition(EEMD)approach is applied to divide underground fluid observation data into high frequency component,intermediate frequency component and low frequency component. The high frequency component can be used to identify and study isolated events,such as the co-seismic response. The intermediate frequency component contains earth tide signals of diurnal tide and semidiurnal tide. The low frequency component represents trend variation characteristics of the observation in a long time. After the division,specific researches could be carried out according to frequency contents of signals,providing ideas and data foundation for noise suppression,abnormal event recognition and quantitative research of mechanisms. Based on the results,the EEMD is applied to the water level observations that were collected from 2016 to 2018 at the Xin No.10 well. The co-seismic response of 31 earthquakes with magnitude 7.0 and larger is identified from the high frequency component,and the characteristics of their maximum amplitude as a function of epicentral distance and magnitude are quantitatively analyzed.
Key words:  Ensemble Empirical Mode Decomposition  Water level observation  Co-seismic response