Frequency Domain Detection with Nearest Neighbor Clustering to Detect Dynamically Triggered Remote Small Earthquakes within the Footprint of the EarthScope USArray Transportable Array

To better understand earthquake source processes involved in dynamically triggering remote aftershocks, we design and test an automated algorithm to detect far-field aftershocks from two large earthquakes. Our goal is to create a method that is tractable for large datasets, ensures robust catalogs, and delivers lower magnitude of completeness than current catalogs. We use data recorded by the EarthScope's USArray Transportable Array (TA), which has a uniform 70km grid spacing and large spatial coverage. We test 3-hours of data following the 12 September 2007 Sumatra M8.5 earthquake and the 07 November 2012 M7.4 Guatemala earthquake, when the TA network was on the west-coast and east-coast of the contiguous US, respectively. This allows exploration of a broad range of tectonic environments and regions of both small tectonic earthquakes (2007 data) and significant extraction sector activity, such as mine blasts (2012 data). The main steps in our data processing include: (1) A frequency domain detection algorithm to identify signals above the noise floor in the 4-12 Hz range to create a detection catalog. (2) Iteratively implementing a nearest-neighbor technique to remove detection outliers from the catalog. (3) Partitioning the detections into high density clusters. (4) Building an earthquake catalog where, for each cluster, we assume the station location that first recorded the seismic energy is a good proxy for the location of the local earthquake and this signal's arrival time is a good proxy for the time of the earthquake. Our results identify 7 and 9 events in the 2007 and 2012 data, respectively, which is a significant increase over the 3 and 2 earthquakes listed in the ANF catalog during the same time periods. This method is ideal for identifying the signature of small earthquakes within the wavetrain of large remote mainshocks recorded by the TA network.