Denoising of seismic signals based on empirical mode decomposition-wavelet thresholding

Unattended object detection systems have seen full applications in military surveillance, object recognition, and intrusion prevention. When applied to actual work scenarios, these systems have problems such as low recognition accuracy, low positioning accuracy, and weak detection effect of distant objects. Obtainment of enough feature information concerning the effective signals is critical to target recognition. This work focuses on interference in seismic signals and the way to store the feature information of effective signals. First, the authors analyzed the frequency and attenuation characteristics of seismic waves of typical target sites, in which the Rayleigh wave is suitable for the detection of the energy of seismic signals produced by human targets and vehicles. As seismic signals are low-frequency waves, the authors researched the performance of the empirical mode decomposition method and the wavelet thresholding method in denoising seismic signals, and an improved empirical mode decomposition-wavelet threshold denoising method is proposed. The test result shows that the improved denoising method can effectively remove noise in seismic signals and preserve the effective signals of the target.