Denoising of acoustic emission signals from rock failure processes through ICEEMDAN combined with multiple criteria and wavelet transform

To improve the accuracy of rock failure monitoring, this article addresses the optimization of denoising acoustic emission (AE) signals. Combining laboratory experiments on rock AE and theoretical research on signal denoising, a denoising method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed for rock fracture AE signals. The ICEEMDAN algorithm is used to decompose the original noisy signal into multiple intrinsic mode functions (IMFs) and employs cluster analysis to determine data thresholds based on their characteristics. Subsequently, using multiple criteria such as permutation entropy, correlation coefficient, and variance contribution rate, the IMFs are categorized into two groups. The low-correlation IMFs are partially removed based on the combination of indicators, while the high-correlation portion is denoised using wavelet thresholding (WT). Finally, a wavelet analysis is performed to reconstruct the signal, effectively achieving an optimized denoising of the original signal. Quantitative analysis of denoising effects on typical rock uniaxial compression fracture AE signals reveals that the optimized method has a positive impact on high-frequency noise reduction. The peak frequency range is unaffected before and after optimization, while the main amplitude reduction is concentrated in the high-frequency range. Compared to traditional wavelet denoising methods, the proposed method exhibits higher signal-to-noise ratio (SNR) improvement, as well as varying degrees of reduction in mean squared error (MSE) and total harmonic distortion (THD). This research introduces a novel approach for optimizing the application of rock AE signals.