An application of waveform denoising for microseismic data using polarization–linearity and time–frequency thresholding
Noise suppression or signal-to-noise ratio enhancement is often desired for better processing results from a microseismic dataset. In this paper, a polarization–linearity and time–frequency-thresholding-based approach is used for denoising waveforms. A polarization–linearity filter is initially applied to preserve the signal intervals and suppress the noise amplitudes. This is followed by time–frequency thresholding for further signal-to-noise ratio enhancement in the S transform domain. The parameterisation for both polarization filter and time–frequency thresholding is also discussed. Finally, real microseismic data examples are shown to demonstrate the improvements in processing results when denoised waveforms are considered in the workflow. The results indicate that current denoising approach effectively suppresses the background noise and preserves the vector fidelity of signal waveform. Consequently, the quality of event detection, arrival-time picking, and hypocenter location improves.