Downhole microseismic signal denoising via empirical wavelet transform and adaptive thresholding

Downhole microseismic data have the characteristics of low signal-to-noise ratio and high frequency, which pose a major challenge to noise attenuation. In this paper, we propose a novel downhole microseismic denoising approach based on the empirical wavelet transform combined with adaptive thresholding. According to the frequency characteristics of the signal, a spectrum segmentation strategy is designed. It can adaptively decompose the signal and noise into different modes. Through analyzing the spectrum and energy of the modes, different threshold functions are applied. The mode that contains more valid signals is processed by hard thresholding to reserve the amplitude; the other modes, which contain less useful signals, are processed by modified threshold functions to maintain the continuity of the restructured signal. We have determined this approach's potential for microseismic denoising by comparing its performance on synthetic and field data using complete ensemble empirical mode decomposition, wavelet transform, and synchrosqueezed wavelet transform.