Research on WT-SVD Bilayer Filter Denoising for Downhole Signal

Abstract

Aiming at the problem of singularity and mutation points after wavelet denoising with improved layered threshold when denoising the downlink signal received in the downhole in rotary steering drilling, a bilayer filtering denoising method based on singular value decomposition is proposed. This method combines wavelet denoising and singular value decomposition denoising, and adopts the WT-SVD bilayer filtering denoising method to construct a matrix of the wavelet improved layered threshold denoising signal, and then performs singular value decomposition and recovery on the matrix signal. The number of selected singular values and the structure of the reconstruction matrix were determined through experiments, and the signal waveforms, signal-to-noise ratio, root mean square error and correlation coefficients of three different denoising methods including singular value decomposition, wavelet improved layered threshold and WT-SVD bilayer filtering are compared and studied. The final experimental results show that the WT-SVD bilayer filtering denoising method is effective.

Integration of Extreme-point Symmetric Mode Decomposition and Singular Value Decomposition for Dynamic Deflection Denoising of Urban Bridges Obtained by Ground-based Microwave Interferometry

“…Yang et al proposed a WT-SVD bilayer filtering denoising method based on WT and SVD denoising processes, and experimentally proved its effectiveness. (28) However, this method did not reduce the effect of the signal trend term on SVD. To solve the mode mixing problem in EMD, Wen and Zhou proposed an SVD-EEMD method combining SVD and EEMD that can extract the fault information of a rolling bearing effectively and realize a precise fault diagnosis.…”

“…(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) Nonlinear and nonstationary signals measured with a low signal-to-noise ratio (SNR) often have strong random and impulse noises, and the useful signals are almost submerged by noise. (24)(25)(26)(27)(28)(29) SVD is an effective signal processing technique for analyzing nonlinear and nonstationary signals, which has been widely used in speech recognition, image processing, fault diagnosis, signal denoising, and various other fields. (24)(25)(26)(27)(28)(29) Because signal information and noise information have different effects on the singular values of the matrix, SVD extracts signal features and separates noise by selecting appropriate singular values of the matrix.…”

“…(24)(25)(26)(27)(28)(29) SVD is an effective signal processing technique for analyzing nonlinear and nonstationary signals, which has been widely used in speech recognition, image processing, fault diagnosis, signal denoising, and various other fields. (24)(25)(26)(27)(28)(29) Because signal information and noise information have different effects on the singular values of the matrix, SVD extracts signal features and separates noise by selecting appropriate singular values of the matrix. (24)(25)(26)(27)(28)(29) The phase shift of the denoised signal obtained by SVD is small and there is no time delay.…”

“…(24)(25)(26)(27)(28)(29) Because signal information and noise information have different effects on the singular values of the matrix, SVD extracts signal features and separates noise by selecting appropriate singular values of the matrix. (24)(25)(26)(27)(28)(29) The phase shift of the denoised signal obtained by SVD is small and there is no time delay. (24)(25)(26)(27)(28)(29) However, the denoised results of SVD will be affected by the signal trend term.…”

“…(24)(25)(26)(27)(28)(29) The phase shift of the denoised signal obtained by SVD is small and there is no time delay. (24)(25)(26)(27)(28)(29) However, the denoised results of SVD will be affected by the signal trend term. (29) Extreme-point symmetric mode decomposition (ESMD) is a new analysis method in the time-frequency domain.…”