【摘要】 核爆炸地震监测技术研究中,数据质量检测是地震数据自动处理的基本内容,毛刺是影响数据质量的主要问题数据。基于平稳小波变换和非线性能量检测算法,给出一种毛刺自动检测算法。平稳小波变换弥补了正交小波变换存在的不足,可以使尺度分解结果的长度和原始数据保持一致,具备时移不变性。非线性能量检测算法可以增强记录中的高频信号,对平稳小波变换的结果应用非线性能量检测算法,提高了记录中毛刺检测的准确性,非常适合连续地震监测数据自动处理的需要。实验结果表明,给出的这种算法特别有利于记录中小毛刺的检测,从而能够减小信号检测的误检率。更多还原

【Abstract】 In the research of seismology for monitoring nuclear explosion, data quality checking is a basic step for seismogram automatic processing, spike is the main problem data. A novel algorithm for spikes detection based on stationary wavelet transform and nonlinear energy operator is proposed. Time invariance and analysis results keep the same length with that of original signal are some good properties of stationary wavelet transform. Nonlinear energy operator can enhance signal with high frequency. Spikes can be detected accurately and easily while applied nonlinear energy operator to the output of stationary wavelet transform, the modified method is particularly suitable for the automatic detecting of small spikes in the seismogram, which is helpful to reduce the false detection ratio of seismic signal.更多还原