We describe a prototype detection framework that automatically clusters events in real time from a rapidly unfolding aftershock sequence. We use the fact that many aftershocks are repetitive, producing similar waveforms. By clustering events based on correlation measures of waveform similarity, the number of independent event instances that must be examined in detail by analysts may be reduced. Our system processes array data and acquires waveform templates with a short-term average (STA)/long-term average (LTA) detector operating on a beam directed at the P phases of the aftershock sequence. The templates are used to create correlation-type (subspace) detectors that sweep the subsequent data stream for occurrences of the same waveform pattern. Events are clustered by association with a particular detector. Hundreds of subspace detectors can run in this framework a hundred times faster than in real time. Nonetheless, to check the growth in the number of detectors, the framework pauses periodically and reclusters detections to reduce the number of event groups. These groups define new subspace detectors that replace the older generation of detectors. Because low-magnitude occurrences of a particular signal template may be missed by the STA/LTA detector, we advocate restarting the framework from the beginning of the sequence periodically to reprocess the entire data stream with the existing detectors.

We tested the framework on 10 days of data from the Nevada Seismic Array (NVAR) covering the 2003 San Simeon earthquake. One hundred eighty-four automatically generated detectors produced 676 detections resulting in a potential reduction in analyst workload of up to 73%.

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