Time-frequency analysis of the Earth's natural pulse electromagnetic field before earthquake based on BSWT-DDTFA method
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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MS7.0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.
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 MS7.0 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.
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图 2
2013年4月20日芦山MS7.0地震所处地震带及周边地区活动断层图分布图(王杰等, 2013)
Figure 2.
The seismic zone and distribution of surrounding active faults of Lushan MS7.0 earthquake on April 20th
图 3
(a) ENPEMF原始数据的包络图(通道CN2)；(b) 4月20日数据的傅里叶变换(FFT)频谱图
Figure 3.
(a) Envelope of original ENPEMF data (channel 2); (b) Fourier transform (FFT) spectrum of the data on April 20th
图 6
不同方法分解得到的信号x1(t)和x2(t)的分量
Figure 6.
IMFs of x1(t) and x2(t) decomposed by different methods
图 7
仿真信号x1(t)和x2(t)的真实瞬时频率(虚线)和(a)EMD-DDTFA瞬时频率(实线); (b) BSWT-DDTFA瞬时频率(实线)
Figure 7.
The actual instantaneous frequency (dashed) and (a) EMD-DDTFA instantaneous frequency (solid); (b) BSWT-DDTFA instantaneous frequency (solid) of signal x1(t) and x2(t)
图 8
不同方法得到的仿真信号x1(t)和x2(t)的时频分布图
Figure 8.
The time-frequency distribution of signal x1(t) and x2(t)
图 9
不同信噪比下sig(t)由BSWT-DDTFA分解的分量
Figure 9.
The components of sig(t) decomposed by BSWT-DDTFA with different SNR
图 10
EMD-DDTFA和BSWT-DDTFA方法得到的IMF分量的绝对误差平均值
Figure 10.
The average absolute error of the IMFs decomposed by EMD-DDTFA and BSWT-DDTFA
图 11
不同信噪比下sig(t)由SWT和BSWT-DDTFA绘制的时频分布图
Figure 11.
The time-frequency distribution of sig(t) plotted by SWT and BSWT-DDTFA with different SNR
图 12
2013年芦山地震前后ENPEMF信号的时频分布的变化
Figure 12.
The variation in time-frequency distribution of ENPEMF data before and after the Lushan earthquake in 2013
表 1
通过不同方法得到的IMF分量的绝对误差平均值
Table 1.
The absolute error of the IMFs obtained by the BSWT-DDTFA method
算法 20 dB 15 dB 10 dB 5 dB 0 dB -5 dB -10 dB EMD-DDTFA 0.0302 0.0317 0.3676 0.3675 0.3710 0.3823 0.4255 BSWT-DDTFA 0.0115 0.0134 0.0171 0.0240 0.0368 0.0610 0.1050 
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表 2
通道CN2的观测数据
Table 2.
Observation data of the S-N Channel (CN2)
17th April(cn2) 18th April(cn2) 19th April(cn2) 20th April(cn2) 21st April(cn2) Time AH Time AH Time AH Time AH Time AH - - - - - - - - - - 14：57：50 0 14：57：50 376 07：54：04 0 22：23：43 0 11：10：42 0 14：57：51 0 14：57：51 179 07：54：05 220 22：23：44 0 11：10：42 244 14：57：52 256 14：57：52 0 07：54：06 229 22：23：45 234 11：10：42 0 14：57：53 468 14：57：53 0 07：54：07 217 22：23：46 0 11：10：42 476 14：57：54 0 14：57：54 167 07：54：08 0 22：23：47 181 11：10：42 200 14：57：55 0 14：57：55 176 07：54：09 0 22：23：48 0 11：10：42 0 - - - - - - - - - - 23：59：59 282 23：59：59 231 23：59：59 212 23：59：59 4 23：59：57 11
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