Processing automatic seismic event detections: an iterative sorting algorithm improving earthquake hypocentres using interevent cross-correlation

Abstract

SUMMARY We present an iterative classification scheme using interevent cross-correlation to update an existing earthquake catalogue with similar events from a list of automatic seismic event detections. The algorithm automatically produces catalogue quality events, with improved hypocentres and reliable P- and S-arrival time information. Detected events are classified into four event categories with the purpose of using the top category, with the highest assessed event quality and highest true-to-false ratio, directly for local earthquake tomography without additional manual analysis. The remaining categories have varying proportions of lower quality events, quality being defined primarily by the number of observed phase onsets, and can be viewed as different priority groups for manual inspection to reduce the time spent by a seismic analyst. A list of 3348 event detections from the geothermally active volcanic region around Hengill, southwest Iceland, produced by our migration and stack detector (Wagner et al. 2017), was processed using a reference catalogue of 1108 manually picked events from the same area. P- and S-phase onset times were automatically determined for the detected events using correlation time lags with respect to manually picked phase arrivals from different multiple reference events at the same station. A significant improvement of the initial hypocentre estimates was achieved after relocating the detected events using the computed phase onset times. The differential time data set resulting from the correlation was successfully used for a double-difference relocation of the final updated catalogue. The routine can potentially be implemented in real-time seismic monitoring environments in combination with a variety of seismic event/phase detectors.