利用小波包变换识别地震和爆破
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摘要
在实际的地震监测和记录波形分析中,通常根据经验对波形特征进行直观分析和整体判断,由此定性地识别出天然地震和人工爆破,然而有些爆破有着类似于天然地震的波形记录,通过宏观特征很难予以排除或识别。本文将基于小波包变换的局部谱密度分析方法应用于宁夏北部及邻区的地震和爆破的分析与识别,通过比较不同记录信号在相同频带内的时频谱最大值之间的差异,研究能够识别天然地震和人工爆破的定量指标,以期提高识别的有效性。
在学习了小波基础知识、Matlab基本编程技巧,讨论了地震子波、小波包降噪方法、小波基函数的选取、时频谱分析方法等关键问题的基础上,本文利用小波包变换实现地震信号与爆破信号的时频谱分析。首先对地震信号和爆破信号进行尺度j=5的小波包变换分解,然后对分解信号进行时频谱分析,并进行归一化处理,绘制出归一化时频谱图,同时,计算出地震信号与爆破信号P波段在各个分解频带内的时频谱最大值,并研究二者在相同频带内的时频谱最大值差异,同理,研究地震信号和爆破信号S波段在各分解频带内的时频谱最大值之间的差异。由此给出地震和爆破之间的多项定量识别指标和识别阈值。最后把各个单项识别指标结合起来,原则是按照超过半数的识别指标给出的识别结果为事件类型的判别结果,以此提高识别指标的有效性。
研究结果表明:第一,银川台记录地震信号P(或S)波段时频谱值达到最大时的频率与爆破信号P(或S)波段时频谱值达到最大时的频率之间存在显著差异,识别率为84.85%(或87.88%)。第二,银川台记录地震信号P波低频带(0~6.25Hz)的两个分解频带(0.78125~1.5625Hz和1.5625~2.34375Hz)内的时频谱最大值与爆破信号P波相应频带内的时频谱最大值之间存在差异。第三,银川台记录地震信号S波低频带(0~6.25Hz)的六个分解频带(0~0.78125Hz、0.78125~1.5625Hz、1.5625~2.34375Hz、3.125~3.90625Hz、3.90625~4.6875Hz和4.6875~5.46875Hz)内的时频谱最大值与爆破信号S波相应频带内的时频谱最大值之间存在差异。第四,结合以上10项单项识别指标,对本文中的地震和爆破进行重新判别,结果均与事件原类型一致,尤其是7个落实为爆破的事件均判别为爆破,并且其中事件3的速报定性结果为地震,经落实证明为爆破,利用该综合识别判据较好地将事件3判断为爆破事件。
Usually, in the process of actual earthquake monitoring and recording waveform analysis, we apply visual analysis and whole judgment to the waveform characteristics based on experience. Then, we can qualitatively identify natural earthquakes from artificial explosions. However, there are some explosions, whose waveform records are similar to that of earthquakes. It is difficult to exclude or identify them through macro features. In the paper, we will apply the method of local spectral density analysis based on wavelet packet to analysis and identification of earthquakes and explosions in the northern part of Ningxia and adjacent areas. By comparing the maximum value of instantaneous spectrum in the same frequency bands of different recording signals, we make a study of quantitative index of identification of earthquakes from explosions, with a view to enhancing effectiveness of identification.
On the basis of having been studying the basic knowledge of wavelet and basic skills of Matlab programming, and discussing some key questions of seismic wavelet, noise reduction method of wavelet packet , selection of wavelet basis function and method of time-frequency spectral analysis, we use wavelet packet transform to realize time-frequency spectral analysis of seismic signals and blasting signals. First of all, we carry out wavelet packet decomposition with scale j=5 to seismic signals and blasting signals, then, time-frequency analysis of decomposition signals, and normalized treatment, finally, mapping out normalized time-frequency spectrum. At the same time, we can calculate maximum values of instantaneous spectrum in each decomposition frequency band of P-wave of seismic signals and blasting signals, and make a research on differences among the values in the same band. Similarly, we can make a research on differences among the values in the same band of S-wave of seismic signals and blasting signals. Then, we can obtain a number of quantitative indices and thresholds of identification of earthquakes from explosions. Finally, to enhance effectiveness of the indices, we unify all the individual indices with each other, in accordance with the principle that discrimination results from more than half of the indices are the types of events.
The results are as follows. Firstly, there are significant differences between frequency at the maximum value of time-frequency spectrum of P-wave (or S-wave) of seismic signals and that of blasting signals from Yinchuan seismic station. The recognition rate is 84.85% (or 87.88%). Secondly, there are differences between the maximum values of instantaneous spectrum in the two decomposition bands (0.78125 ~ 1.5625 Hz and 1.5625 ~ 2.34375 Hz) of P-wave band (0 ~ 6.25 Hz) of seismic signals and that of blasting signals from Yinchuan seismic station. Thirdly, there are differences between the maximum values of instantaneous spectrum in the six decomposition bands (0 ~ 0.78125Hz, 0.78125 ~ 1.5625Hz, 1.5625 ~ 2.34375Hz, 3.125 ~ 3.90625Hz, 3.90625 ~ 4.6875Hz and 4.6875 ~ 5.46875 Hz) of S-wave band (0 ~ 6.25 Hz) of seismic signals and that of blasting signals from Yinchuan seismic station. Fourthly, with the ten indices unified above, we re-discriminate the earthquakes from explosions in the paper. And discrimination types are all consistent with that of the events. Especially, seven explosions implemented are all distinguished from the events. And the third event, which was considered as earthquake from rapid report of earthquake and proved to be explosion after implementation, is identified as explosion with the synthesis identification criterion.
在学习了小波基础知识、Matlab基本编程技巧,讨论了地震子波、小波包降噪方法、小波基函数的选取、时频谱分析方法等关键问题的基础上,本文利用小波包变换实现地震信号与爆破信号的时频谱分析。首先对地震信号和爆破信号进行尺度j=5的小波包变换分解,然后对分解信号进行时频谱分析,并进行归一化处理,绘制出归一化时频谱图,同时,计算出地震信号与爆破信号P波段在各个分解频带内的时频谱最大值,并研究二者在相同频带内的时频谱最大值差异,同理,研究地震信号和爆破信号S波段在各分解频带内的时频谱最大值之间的差异。由此给出地震和爆破之间的多项定量识别指标和识别阈值。最后把各个单项识别指标结合起来,原则是按照超过半数的识别指标给出的识别结果为事件类型的判别结果,以此提高识别指标的有效性。
研究结果表明:第一,银川台记录地震信号P(或S)波段时频谱值达到最大时的频率与爆破信号P(或S)波段时频谱值达到最大时的频率之间存在显著差异,识别率为84.85%(或87.88%)。第二,银川台记录地震信号P波低频带(0~6.25Hz)的两个分解频带(0.78125~1.5625Hz和1.5625~2.34375Hz)内的时频谱最大值与爆破信号P波相应频带内的时频谱最大值之间存在差异。第三,银川台记录地震信号S波低频带(0~6.25Hz)的六个分解频带(0~0.78125Hz、0.78125~1.5625Hz、1.5625~2.34375Hz、3.125~3.90625Hz、3.90625~4.6875Hz和4.6875~5.46875Hz)内的时频谱最大值与爆破信号S波相应频带内的时频谱最大值之间存在差异。第四,结合以上10项单项识别指标,对本文中的地震和爆破进行重新判别,结果均与事件原类型一致,尤其是7个落实为爆破的事件均判别为爆破,并且其中事件3的速报定性结果为地震,经落实证明为爆破,利用该综合识别判据较好地将事件3判断为爆破事件。
Usually, in the process of actual earthquake monitoring and recording waveform analysis, we apply visual analysis and whole judgment to the waveform characteristics based on experience. Then, we can qualitatively identify natural earthquakes from artificial explosions. However, there are some explosions, whose waveform records are similar to that of earthquakes. It is difficult to exclude or identify them through macro features. In the paper, we will apply the method of local spectral density analysis based on wavelet packet to analysis and identification of earthquakes and explosions in the northern part of Ningxia and adjacent areas. By comparing the maximum value of instantaneous spectrum in the same frequency bands of different recording signals, we make a study of quantitative index of identification of earthquakes from explosions, with a view to enhancing effectiveness of identification.
On the basis of having been studying the basic knowledge of wavelet and basic skills of Matlab programming, and discussing some key questions of seismic wavelet, noise reduction method of wavelet packet , selection of wavelet basis function and method of time-frequency spectral analysis, we use wavelet packet transform to realize time-frequency spectral analysis of seismic signals and blasting signals. First of all, we carry out wavelet packet decomposition with scale j=5 to seismic signals and blasting signals, then, time-frequency analysis of decomposition signals, and normalized treatment, finally, mapping out normalized time-frequency spectrum. At the same time, we can calculate maximum values of instantaneous spectrum in each decomposition frequency band of P-wave of seismic signals and blasting signals, and make a research on differences among the values in the same band. Similarly, we can make a research on differences among the values in the same band of S-wave of seismic signals and blasting signals. Then, we can obtain a number of quantitative indices and thresholds of identification of earthquakes from explosions. Finally, to enhance effectiveness of the indices, we unify all the individual indices with each other, in accordance with the principle that discrimination results from more than half of the indices are the types of events.
The results are as follows. Firstly, there are significant differences between frequency at the maximum value of time-frequency spectrum of P-wave (or S-wave) of seismic signals and that of blasting signals from Yinchuan seismic station. The recognition rate is 84.85% (or 87.88%). Secondly, there are differences between the maximum values of instantaneous spectrum in the two decomposition bands (0.78125 ~ 1.5625 Hz and 1.5625 ~ 2.34375 Hz) of P-wave band (0 ~ 6.25 Hz) of seismic signals and that of blasting signals from Yinchuan seismic station. Thirdly, there are differences between the maximum values of instantaneous spectrum in the six decomposition bands (0 ~ 0.78125Hz, 0.78125 ~ 1.5625Hz, 1.5625 ~ 2.34375Hz, 3.125 ~ 3.90625Hz, 3.90625 ~ 4.6875Hz and 4.6875 ~ 5.46875 Hz) of S-wave band (0 ~ 6.25 Hz) of seismic signals and that of blasting signals from Yinchuan seismic station. Fourthly, with the ten indices unified above, we re-discriminate the earthquakes from explosions in the paper. And discrimination types are all consistent with that of the events. Especially, seven explosions implemented are all distinguished from the events. And the third event, which was considered as earthquake from rapid report of earthquake and proved to be explosion after implementation, is identified as explosion with the synthesis identification criterion.
引文
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