Efficient seismic noise suppression for microseismic data using an adaptive TMSST approach

Hydraulic fracturing is an effective reservoir stimulation technique. Microseismic monitoring technology can effectively obtain information from within the reservoir. In this process, the effective extraction of microseismic data is crucial, but monitoring data is often interfered with by various noises, thus necessitating noise suppression processing. Currently, commonly used noise suppression methods mainly target random noise and often overlook the possibility of impulse noise in microseismic data. To address this issue, this paper proposes a method that combines periodic noise suppression with time-reassigned multisynchrosqueezing transform (TMSST). The method first highlights impulse noise by suppressing periodic noise and then adaptively determines the optimal parameters of the TMSST algorithm through stability judgment and peak value searching. In simulation and experimental tests, the proposed method was compared with the traditional ensemble empirical mode decomposition (EEMD) method. The results show that in an environment with strong background noise, the proposed algorithm performs excellently in suppressing strong impulse noise in hydraulic fracturing microseismic data.