Abstract

Technological advances in combination with the onslaught of data availability allow for large seismic data streams to automatically and systematically be recorded, processed, and stored. Here, we develop an automated approach to identify small, local earthquakes within these large continuous seismic data records. Our aim is to automate the process of detecting small seismic events triggered by a distant large earthquake, recorded at a single station. Specifically, we apply time‐domain short‐term average (STA) to long‐term average (LTA) ratio algorithms to three‐component data to create a catalog of detections. We remove some of the false detections by requiring the detection be recorded on a minimum of two channels. To calibrate the algorithm, we compare our automatic detection catalog to a set of analyst‐derived P‐wave arrival times for a subset of small earthquakes occurring in the December 2008 Yellowstone swarm. Of the four STA/LTA algorithms we test (1 s/10 s; 4 s/40 s; 8 s/80 s; 16 s/160 s), the 1 s/10 s and 4 s/40 s detectors proved most effective at identifying the majority of events in the swarm. We apply these detectors to ±45  hrs and ±5  hrs of USArray data from the 2011 Japan M 9.0 and the 2010 Chile M 8.8 earthquakes, respectively. Using time‐of‐day versus number of detection relationships, we identify 38 of the 728 available stations that exhibit strong anthropogenic noise following the 2011 Japan earthquake. Our detection algorithm identified three regional earthquakes concurrent with the passage of the S‐ and surface waves of the Chile mainshock at USArray station R11A that locate in the Coso region of California, as well as events in Texas following the Japan earthquake.

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