基于EMD的IMF时域统计特征提取及其应用于震动事件源类型识别研究

文章对地震波形进行经验模态分解(EMD),对分解后的内模函数(IMF)进行时域特征提取,由所提取的特征对天然地震和人工爆炸2类事件源类型进行分类识别,结果表明,由IMF所提取的时域特征具有良好的区分识别能力.采用经验模态分解将原波形信号分解为7个内模函数和残差函数,对原波形、每个内模函数和残差函数分别提取26个时域统计...

Extraction of IMF time-domain features based on EMD andits application to recognition of vibration event source type

In this paper,the seismic waveform was decomposed by the empirical mode decomposition(EMD)method,and the time-domain features of the decomposed intrinsic mode function(IMF)were extracted.The two types of event sources(natural earthquake and artificial explosion)were classified and identified by the extracted features.The results showed that the time-domain features extracted from IMF have good discrimination and recognition ability.The original waveform signal was decomposed into 7 IMFs and residual functions by the EMD,and 26 time-domain statistical features were extracted from the original waveform,each IMF,and each residual function,respectively,to form 9 feature groups(named Q₀,Q₁,…,Q₈).Then 7 energy ratio features were calculated from the amplitude-energy ratio of 7 IMFs,and 32 features were selected from the time-domain features of the first 4 IMFs,which formed a feature group with 39 features(named Q₉).A series of identification experiments were conducted on single group or various combinations of the 10 feature groups by using the symmetrical Kullback-Leibler(KL)divergence.In each experiment,the training and testing samples were the same,but were randomly selected from the corresponding feature groups of all waveform of some events(30%,60%,70,or 90%).The experiments were repeated many times,and the results showed that the time domain features extracted by the second IMF have the best recognition effect,and the correct recognition rate is above 90%.It suggests that the IMF,which has better ability to distinguish event types than the original waveform,can provide more effective features for the recognition of event source type.