郝国成^1,2,4,,
白雨晓^1,4,
吴敏^3,4,
王巍¹,
刘辉¹
1. 中国地质大学(武汉)机械与电子信息学院, 武汉 430074
2. Department of Mathematics, Duke University, Durham, NC, 27708, USA
3. 中国地质大学(武汉)自动化学院, 武汉 430074
4. 复杂系统先进控制与智能自动化湖北省重点实验室, 武汉 430074

基金项目: 国家自然科学基金（61333002），教育部博士后基金（2015M582293），武汉市科技局攻关计划项目（2016060101010073），111项目（B17040）资助


详细信息

作者简介: 郝国成, 男, 1975年生, 副教授, 博士, 研究方向为地球天然脉冲电磁场方法及非平稳信号时频分析.E-mail:haogch@cug.edu.cn

中图分类号: P318

收稿日期:2017-07-13
修回日期:2017-12-18
上线日期:2018-10-05

Time-frequency analysis of the Earth's natural pulse electromagnetic field before earthquake based on BSWT-DDTFA method

HAO GuoCheng^1,2,4,,
BAI YuXiao^1,4,
WU Min^3,4,
WANG Wei¹,
LIU Hui¹
1. Faculty of Mechanical & Electronic Information, China University of Geosciences, Wuhan 430074, China
2. Department of Mathematics, Duke University, Durham, NC, 27708, USA
3. School of Automation, China University of Geosciences, Wuhan 430074, China
4. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China


MSC: P318

--> Received Date: 13 July 2017
Revised Date: 18 December 2017
Available Online: 05 October 2018


摘要
摘要:地球天然脉冲电磁场（ENPEMF）信号，可理解为地球天然变化磁场的瞬间扰动，携带了大量有用的地质构造及其动力学信息.研究ENPEMF信号所蕴含的时间-频率联合分布特点，有利于深入了解目标对象的地球物理现象及其地质动力学原理.本文针对ENPEMF信号的非平稳特点，在数据驱动时频分析方法（DDTFA）的基础上提出了基于二值化同步压缩小波变换的改进算法（BSWT-DDTFA）.该算法可以实现数据驱动初始相位自动赋值的功能，具有自适应性.实验仿真和实际数据均证明了该改进算法不仅能够得到较为精确的频率曲线和更加清晰的时频分布，而且具有较强的抗噪声能力.以2013年芦山M_S7.0地震为例，利用BSWT-DDTFA方法提取ENPEMF信号的时频特性，结果表明ENPEMF信号的时间-频率-幅度分布在震前有明显的异常特征.
关键词: 地球天然脉冲电磁场/
时频分析/
数据驱动/
同步压缩变换/
震前异常

Abstract:The Earth's natural pulse electromagnetic field (ENPEMF) signal can be interpreted as the instantaneous perturbation caused by the Earth's natural varying magnetic field, carrying a great deal of useful information about geological structure and dynamics. It is beneficial for understanding the geophysical phenomena of the target object and its geodynamic principles to study the time-frequency joint distribution of the ENPEMF signal. In this paper, for the "non-stationary" characteristics of ENPEMF signals, an improved data-driven time-frequency analysis method based on binarized synchrosqueezed wavelet transform (BSWT-DDTFA) is proposed. This improved algorithm is able to assign initial values automatically with adaptability. Both experimental simulation and real data demonstrate that the improved algorithm not only can provide more accurate frequency curve and clearer time-frequency distribution, but also has stronger anti-noise ability. For the case of the M_S7.0 Lushan earthquake in 2013, the time-frequency characteristics of the ENPEMF signal are extracted by using the BSWT-DDTFA method. The results show that there exists obvious anomalous characteristics in time-frequency-amplitude distribution of ENPEMF signal before the earthquake.
Key words:The Earth's natural pulsed electromagnetic field/
Time-frequency analysis/
Data-driven/
Synchrosqueezed transform/
Abnormal characteristics before the earthquake



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