Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method.

ABSTRACT: There are high- and low-frequency noise signals in a microseismic signal that can lead to the distortion and submersion of an effective waveform. At present, effectively removing high- and low-frequency noise without losing the effective signal of local waveform spikes remains a challenge. This work addresses this issue with an improved wavelet adaptive thresholding method. Because a denoised signal conceptually approximates the minimum error, a dynamic selection model is established for the optimal threshold. On this basis, an adaptive correction factor a_j is proposed to reflect the noise intensity, which uses the 1/2 power of the ratio of the median absolute value to the amplitude of the monitoring data to reflect the noise intensity of the wavelet detail signal and corrects t

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC9789042 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature