【摘要】 本文对地下核爆炸和天然地震所产生的地震波信号进行了处理和分析,发现时域地震波信号具有统计自仿射分形特征,但用对数功率谱求得的分维数D不适合作模式识别的特征参数.对时域地震波信号进行小波分解后,各分解尺度的信号"能量"与尺度有关,同时发现,精细结构信号能量均出现—峰值,据此求得的两类参数可以作为核爆地震模式识别的特征量,且识别结果较好.更多还原

【Abstract】 In this paper, based on the processing and analysis of seismic signals originated from underground nuclear explosion and natural earthquake, it is illustrated that the seismic signals in time domain possess the characteristics of statistical self-affine fractal, whilst the fractal dimension D yielded from logarithmic power spectrum does not serve as effective feature for seismic pattern recognition. Moreover, it is found that the signal " energy " at each scale of wavelet decomposition relates closely to the scale, and that an apex appeared on the " energy spectrum " of detail signal, hence, two kinds of features advocated are very likely to be utilized in seismic pattern recognition applications. Procvided recognition results show the great improvement and excellent performance achieved by the proposed methodologies of feature extraction and selection.更多还原