Robust event detection of low signal‐to‐noise ratio (SNR) events, such as those characterized as induced or triggered seismicity, remains a challenge. The reason is the relatively small magnitude of the events (usually less than 2 or 3 on the Richter scale) and the fact that regional permanent seismic networks can only record the strongest events of a microseismic sequence. Monitoring using temporarily installed short‐period arrays can fill the gap of missed seismicity, but the challenge of detecting weak events in long continuous records is still present. Further, for low SNR recordings, commonly applied detection algorithms generally require prefiltering of the data based on a priori knowledge of the background noise. Such knowledge is often not available.
We present the Nonparametric Detection (NpD) algorithm, an automated algorithm which detects potential events without the requirement for prefiltering. Events are detected by calculating the energy contained within small individual time segments of a recording and comparing it with the energy contained within a longer surrounding time window. If the excess energy exceeds a given threshold criterion, which is determined dynamically based on the background noise for that window, then an event is detected. To characterize background noise for each time window, the algorithm uses nonparametric statistics to describe the upper bound of the spectral amplitude. Our approach does not require an assumption of normality within the recordings, and hence it is applicable to all data sets.
We compare our NpD algorithm with the commonly commercially applied short‐term average/long‐term average (STA/LTA) algorithm and another highly efficient algorithm based on power spectral density (PSD) using a challenging microseismic data set with poor SNR. For event detection, the NpD algorithm significantly outperforms the STA/LTA and PSD algorithms tested, maximizing the number of detected events while minimizing the number of false positives.