Comparison of sparse Gabor-based methods for detection of microseismic events

Event detection and selection is a challenging and time-consuming step in microseismic data processing because signals are often embedded in noisy recordings. Automated triggering-based algorithms can detect many potential events in the data. However, their precision rates are often low, thus requiring substantial manual labor to select desired events of interest. This study investigates two time-frequency methods which combine signal enhancement with automated event detection and selection, namely: (1) the sparse Gabor transform and (2) the neighboring block thresholding. Both methods use thresholding in the time-frequency domain to increase signal enhancement, followed by an energy detection criterion, leading to improved event detection with higher precision rates. However, the neighboring block thresholding causes amplitude fidelity issues; we observe changes in the relative and maximum amplitudes of the waveforms reconstructed from the thresholded coefficients. Conversely, the sparse Gabor transform attenuates the noise significantly while preserving the signals. This time-frequency method is thus suitable for enhanced event detection and subsequent processing, including magnitude estimation and moment tensor inversion.