Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition

Abstract

Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data.

Using geometric mode decomposition for the background noise suppression on microseismic data

“…The EEMD method reduces the mode-mixing and aliasing artefacts of EMD by adding white Gaussian noise to the signal. Another example is the VMD method that has superior anti-noise performance as compared to EMD and EEMD and can further solve the mode-mixing problem (Chen et al, 2021). Liu et al (2016) used the VMD method to highlight geologic and stratigraphic information on seismic datasets.…”

“…Banjade et al (2019Banjade et al ( , 2021 applied different hybrid methods using VMD, to earthquake time series data for random noise suppression. Chen et al (2021) used a re-constrained VMD method to denoise three-component microseismic data. Another recent inspiration from the VMD framework is the geometric mode decomposition (GMD; Yu et al, 2018) that can optimally estimate the geometric features such as lines and parabolas on two-dimensional (2D) datasets.…”

“…Chen et al. (2021) used a re‐constrained VMD method to denoise three‐component microseismic data. Another recent inspiration from the VMD framework is the geometric mode decomposition (GMD; Yu et al., 2018) that can optimally estimate the geometric features such as lines and parabolas on two‐dimensional (2D) datasets.…”

Adaptive noise suppression for low-S/N microseismic data based on ambient-noise-assisted multivariate empirical mode decomposition

“…These mode-decomposition algorithms have been widely employed in seismic/microseismic data denoising and arrival picking [19,[39][40][41][42][43][44]. The separation or reconstruction of signals is often accomplished in the mode decomposition based methods and their improvement approaches by the selection of IMF components.…”