Abstract

Immediately following the 18 March 2020 Mww 5.7 Magna, Utah, earthquake, work began on installing a network of three‐component, 5 Hz geophones throughout the Salt Lake Valley. After six days, 180 geophones had been sited within 35 km of the epicenter. Each geophone recorded 250 samples per second data onsite for 40 days. Here, we integrate the geophone data with data from the permanent regional seismic network operated by the University of Utah Seismograph Stations (UUSS). We use machine learning (ML) methods to create a new catalog of arrival time picks, earthquake locations, and P‐wave polarities for 18 March 2020–30 April 2020. We train two deep‐learning U‐Net models to detect P waves and S waves, assigning arrival times to maximal posterior probabilities, followed by a two‐step association process that combines deep learning with a grid‐based interferometric approach. Our automated workflow results in 142,000 P picks, 188,000 S picks, and over 5000 earthquake locations. We recovered 95% of the events in the UUSS authoritative catalog and more than doubled the total number of events (5000 vs. 2300). The P and S arrival times generated by our ML models have near‐zero biases and standard deviations of 0.05 s and 0.09 s, respectively, relative to corresponding analyst times picked at backbone stations. We also use a deep‐learning architecture to automatically determine 70,000 P‐wave first motions, which agree with 93% of 5876 hand‐picked up or down first motions from both the backbone and nodal stations. Overall, the use of ML led to large increases in the number of arrival times, especially S times, that will be useful for future tomographic studies, as well as the discovery of thousands more earthquakes than exist in the UUSS catalog.

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