Microseismic events enhancement and detection in sensor arrays using autocorrelation based filtering
This addresses the challenge of noise in passive microseismic data for geophysical monitoring, but it is incremental as it builds on existing filtering techniques.
The paper tackled the problem of detecting and enhancing microseismic events buried in noise from surface sensor arrays by proposing an autocorrelation-based stacking method and multi-channel detection scheme, validated on synthetic and real data.
Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time-delayed arrival from the event, we propose an autocorrelation-based stacking method that designs a denoising filter from all the traces, as well as a multi-channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace's autocorrelation is centered at zero in the lag domain. The effect of white noise is concentrated near zero lag, so the filter design requires a predictable adjustment of the zero-lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with colored noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces.