Applied Sciences (Nov 2021)

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

  • Zhili Chen,
  • Peng Wang,
  • Zhixian Gui,
  • Qinghui Mao

DOI
https://doi.org/10.3390/app112210943
Journal volume & issue
Vol. 11, no. 22
p. 10943

Abstract

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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.

Keywords