【作者】 毕明霞；

【导师】 黄汉明；

【作者基本信息】 广西师范大学 ， 计算机软件与理论， 2011， 硕士

【摘要】 地震波形特征包括天然地震和人工地震(即人工爆破)波形数据的特征,抽取地震波形特征主要是根据信号处理方法和地震波形数据的自身特点从时域、频域等抽取能够用于识别天然地震和人工爆破波形的有用特征,其关键问题是信号的特征选择与提取。1998年华裔美国工程院院士N.E Huang等人提出了希尔伯特-黄变换方法,它是一种新的信号处理和分析的方法,不同于以往信号分析方法的全局分析特点,在研究信号的局部特征方面具有独特的优越性,是一种比傅里叶变换及小波变换等更具适应性的时频局部化分析方法。这种方法能够更真实的反应出信号能量在时间和频率各尺度上的分布规律,将希尔伯特-黄变换方法应用于地震资料处理中,能够有效解决使用其他方法时存在的各种困扰问题,使地震资料处理在信号分解、时频分析、瞬时参数求解等各方面得到不同程度的突破。鉴于此,本文实验选用希尔伯特-黄变换方法实现了对地震波形特征的抽取,首先分析了天然地震和人工爆破波形信号在时域和频域上的特点,进行一些必要的预处理,然后对天然地震和人工爆破波形信号进行经验模态分解,提取了最大幅值对应的周期(TAmax)、倒谱平均值(Cave)、自相关函数的最大值(M xc)三个特征,最后按照严格的样本划分方法,利用支持向量机方法对天然地震和人工爆破的波形信号进行识别。在实验中,基于C方法时,识别率较为乐观,对经验模态分解后的前三个内模函数提取的这三个特征的识别率达到99.8%。基于U方法时,采用严格的样本划分方法,经反复多次实验,对经验模态分解后的前三个内模函数提取的这三个特征的平均识别率稳定在97%以上。实验结果表明,基于希尔伯特-黄变换方法提取出的波形特征参数具有较好的识别率,基于该方法提取出的最大幅值对应的周期(TAmax)、倒谱平均值(Cave)、自相关函数的最大值(Mxc)这些特征参数可以作为一种新的特征参数应用于天然地震和人工爆破的波形信号识别。更多还原

【Abstract】 The characteristics of the seismic waveform include the waveform data characteristics of natural earthquake and artificial explosion. Extracting the seismic waveform is mainly to extract useful characteristics according to signal processing method and seismic waveform data’s own characteristics for identifying the waveform of natural earthquake and artificial explosion from time domain, frequency domain and so on. The key problem of this is the selection and extracting of the signal characteristics.The Hilbert-Huang Transform method put forward by Chinese American academician of National Academy of Engineering in 1998. It is a new signal processing and analysis method which differs from the global analysis characteristics of former signal analysis methods and has a unique superiority in the localized characteristic area of the signal research. It is a localized analysis method which is a time frequency localization analysis method and is more suitable than Fourier Transform and Wavelet Transform. This method can reflect the signal energy distribution regularity on every scale of the time and frequency more really. If the HHT method is used in processing the seismic resources, the existing problem when using other methods can be effectively solved and we can get breakthroughs on different level in different aspects of seismic resources, such as signal decomposition, time frequency analysis, and instant parameter solution and so on.According to this, this paper using realized the extracting of the seismic waveform with HHT method. First, this paper analyzed the characteristics of natural earthquake and artificial explosion in time domain and frequency domain and did some necessary pre-process. Then carry out the empirical mode decomposition to the waveform signal of natural earthquake and artificial explosion and extract the three characteristics of corresponding maximum amplitude:the period (TAmax), cepstrum average (Cave) and the maximum of autocorrelation function (Mxc). At last, identify the waveform of natural earthquake and artificial explosion with support vector machine strictly according to the sample classify method. In the experiment, based on the C method, recognition rate is more optimistic, on the empirical mode decomposition of the intrinsic mode function to extract the first three of these three features the recognition rate of 99.8%.Based on the U method, the sample divided by the strict method, after repeated and a large number of experiments, on the empirical mode decomposition of the intrinsic mode function to extract the first three of these three features the average recognition rate over 97%.The results indicate that the waveform characteristic parameters extracted based on HHT method has a good identification rate. The period (TAmax), cepstrum average (Cave) and the maximum of autocorrelation function (Mxc) of corresponding maximum amplitude extracted according to this method can be used as a new characteristic parameter in the identifying the waveform signal of natural earthquake and artificial explosion. It has a good identification effect.更多还原