Denoising Method for Ultrasonic Echo Signal of Mining Well Logging Instrument Based on NRBO-ICEEMDAN Wavelet Thresholding

To effectively deal with the problem of high noise and low signal-to-noise ratio (SNR) in the ultrasonic echo signal caused by mud pairs when the ultrasonic well logging instrument operates in the underground complex environment, this article proposes a wavelet threshold denoising method based on Newton-Raphson-based optimizer (NRBO)-improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). This method employs the NRBO algorithm to optimize the parameters of ICEEMDAN, targeting the optimal combination of white noise amplitude weights (Nstd) and the number of noise additions (NE). Then with the help of correlation coefficient method to filter out the effective components from the intrinsic mode functions (IMFs) obtained from the decomposition, the improved wavelet threshold function is used to suppress the noise of the signal components, and finally, the denoised components are reconstructed to constitute the denoised signal. The results indicate that the proposed method demonstrates superior performance over conventional signal denoising techniques. Compared with the ICEEMDAN algorithm, it achieves a 23.45% improvement in SNR and a 38.27% reduction in root-mean-square error (RMSE). This approach effectively enhances signal clarity, thereby substantially improving the reliability and measurement accuracy in ultrasonic logging applications.