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Advances in deep learning in the past decade have recently been applied to various algorithms in the seismic event monitoring data processing pipeline. In this article, we apply PhaseNet (Zhu and Beroza, 2018)—a deep learning model for seismic signal detection, to backprojection event detection in the Utah region using the Waveform Correlation Event Detection System (WCEDS). We compare PhaseNet-WCEDS with the original short-term average/long-term average (STA/LTA) version of WCEDS from Arrowsmith et al. (2016, 2018). Using the Unconstrained Utah Event Bulletin (Linville et al., 2019) as the “ground truth,” we present the precision and recall for each method for a variety of tuning parameters, with PhaseNet-WCEDS recall being approximately 86%, whereas STA/LTA-WCEDS recall was 66% across a range of detection thresholds. Furthermore, we show that the PhaseNet-WCEDS recall advantage holds across various subregions and event source types in the Utah region. We also introduce a local to near-regional event criteria test that reduces false positives by 55% whereas only reducing true positives by 7% for PhaseNet-WCEDS (60% and 17%, respectively, for STA/LTA-WCEDS). Using the event commonality score (ECS, Draelos et al., 2015), we explore the ECS-based event categories for PhaseNet-WCEDS and STA/LTA-WCEDS for two important subsets of our Utah data set—the Circleville aftershock sequence and events in the central mining region.

Multimodal, curated data sets and nuisance event catalogs remain rare in the explosion monitoring community relative to curated seismic data sets. The source of this relative absence is the difficultly in deploying multimodal receivers that sense the seismic, acoustic, and other modalities from multiphysics sources. We provide such a data set in this study that delivers seismic, infrasound, and electromagnetic (magnetometer) sensor records collected over a two-week period, within 255 km of a 10 ton buried chemical explosion called DAG-4 that was located at 37.1146°, −116.0693° on 22 June 2019 21:06:19.88 UTC. This catalog includes 485 seismic, seismoacoustic, and infrasound-only events that an expert analyst manually built by reviewing waveforms from 29 seismic and infrasound sensors. Our data release includes waveforms from these 29 seismic, infrasound, and seismoacoustic stations and two magnetometer stations and their station metadata. We deliver these waveforms in NNSA KB Core CSS.w format (i4) with a corresponding wfdisc table that provides the header information. We expect that this data set will provide a valuable, benchmark resource to develop signal processing algorithms and explosion monitoring methods against manual, human observations.

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