Microseismic signal denoising using simple bandpass filtering based on normal time–frequency transform

In active underground mining environments, monitoring mine vibrations has important implications for both safety and productivity. Microseismic data processing is crucial for subsurface real-time monitoring during mineral mining processes. Microseismic events are difficult to detect due to their small magnitudes and low signal-to-noise ratios (SNRs). Useful microseismic signals are usually obscured by long-period microseisms, random noise and artificial strong noise. We propose a useful microseismic denoising algorithm based on the normal time–frequency transform (NTFT) to determine the instantaneous frequency, amplitude and phase information from useful microseismic signals. The energy difference in the time–frequency domain between useful microseismic signals and strong noise is small. Therefore, based on the different phase characteristics of microseismic signals and noise in the NTFT phase spectrum, noise can be filtered out by reconstructing the microseismic signals in useful real-time frequency bands. The proposed simple bandpass filtering (SBPF) method is advantageous because the denoising result does not produce phase shifts, energy leakage or artefacts. The only parameter of the proposed method that needs to be defined is the instantaneous cutoff frequency; thus, the denoising operation is simple. We use both synthetic and real data to demonstrate the feasibility of the method for denoising complicated microseismic datasets.