Abstract
Due to complicated noise interference, seismic signals of high arch dam are of nonstationarity and a low signal‐to‐noise ratio (SNR) during acquisition process. The traditional denoising method may have filtered effective seismic signals of high arch dams. A self‐adaptive denoising method based on ensemble empirical mode decomposition (EEMD) combining wavelet threshold with singular spectrum analysis (SSA) is proposed in this paper. Based on the EEMD result for seismic signals of high arch dams, a continuous mean square error criterion is used to distinguish high‐frequency and low‐frequency components of the intrinsic mode functions (IMFs). Denoised high‐frequency IMF using wavelet threshold is reconstructed with low‐frequency components, and SSA is implemented for the reconstructed signal. Simulation signal denoising analysis indicates that the proposed method can significantly reduce mean square error under low SNR condition, and the overall denoising effect is superior to EEMD and EEMD‐Wavelet threshold denoising algorithms. Denoising analysis of measured seismic signals of high arch dams shows that the performance of denoised seismic signals using EEMD‐Wavelet‐SSA is obviously improved, and natural frequencies of the high arch dams can be effectively identified.
Highlights
Affected by the testing system and environmental factors, seismic signals of high arch dams are unavoidably interfered by complicated noises during the acquisition process, which greatly influences identification and analysis of real signal information
Wavelet threshold denoising is carried out for high-frequency intrinsic mode functions (IMFs) with many noises discarded by ensemble empirical mode decomposition (EEMD) in order to reserve effective information in these components. e singular spectrum analysis (SSA) is implemented for the reconstructed signal
Ensemble Empirical Mode Decomposition Method. e EEMD is a new empirical mode decomposition (EMD)-based signal processing method to solve easy mode mixing effect of EMD. is method makes the signal be of continuity at different scales by virtue of uniform distribution feature of the Gaussian white noise frequency. e noises are offset by multiple averaging processing so as to inhibit and even completely eliminate noise influence [24]. e procedures for implementing EEMD are as follows: (1) e Gaussian white noise is added to the original signal, whose mean value is 0 and amplitude and standard deviation are constants. e noised signal is shown as xi(t) x(t) + ni(t), (1)
Summary
Affected by the testing system and environmental factors, seismic signals of high arch dams are unavoidably interfered by complicated noises during the acquisition process, which greatly influences identification and analysis of real signal information. Chaotic and complicated dynamic behaviors of seismic signals of high arch dams always result in full or partial overlapping of frequency bands of real signals and superimposed noise. It is difficult for the traditional linear method to realize effective denoising. Some scholars have put forward using the wavelet threshold method to remove high-frequency noises in EEMD and reconstructing residual components [19, 20]. Wavelet threshold denoising is carried out for high-frequency IMFs with many noises discarded by EEMD in order to reserve effective information in these components.
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