Abstract:Applying adaptive noise-complete ensemble empirical modal decomposition(CEEMDAN) and wavelet denoising, we propose a random noise suppression method for DSQ water pipe tiltmeter signals. Firstly, the signals are decomposed by CEEMDAN to obtain several eigen mode functions(IMFs), and the number of decompositions varies dynamically with different signal noises. Then we calculate the correlation coefficient values of each IMF and the original signal, and calculate the original signal. By wavelet transform we process the IMFs within the threshold of the coefficient value. Finally, we perform the linear reconstruction to obtain the denoised signal. The simulation and actual denoising experimental results show that the random noise suppression effect of this method is obvious, and the retention ratio of the effective components of the signal is higher and better than other similar methods.