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A Matched Filtering-Based Workflow for Characterizing Swarm and Aftershock Sequences

Periods of enhanced seismicity consume the resources of a regional seismic network (RSN) and diminish its ability to perform other core monitoring responsibilities. Two common scenarios for enhanced seismicity encountered by RSNs are swarm and aftershock sequences. Since the temporal onset of such seismic sequences is inherently unpredictable, it is important to proactively develop a strategy for expeditiously detecting, locating and estimating the magnitude of the events comprising the sequence. When successful, such a workflow can free up analyst time to perform important high-order response tasks, such as moment tensor estimation, while also lowering the magnitude of completeness of the sequence catalog.

This abstract summarizes the salient processing features comprising the University of Utah Seismograph Stations’ anticipated response to such a period of enhanced seismicity. The overarching goals of the workflow are to detect, locate and compute the magnitude of additional events in an earthquake sequence. The first major component in the workflow is a template matching scheme for event detection. An implicit advantage of template matching over deep-learning approaches is that template matching directly leverages a priori catalog information such as observed travel times and magnitudes from a subset of the events in the sequence. To compute high-precision locations, we compute differential traveltimes by comparing the observed traveltimes with the onsets of the detected event. Additionally, we compute a relative magnitude by comparing the amplitudes of the template waveforms with the amplitudes of the detections. To demonstrate the proposed workflow in a realistic context, we apply our pipeline to a swarm of earthquakes that occurred in the San Rafael swell region of the Colorado Plateau in March-April of 2019.

Presenting Author: Ben Baker

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