Waveform correlation is garnering attention as a method for detecting, locating, and characterizing similar seismic events. To explore the opportunities for using waveform correlation in broad regional monitoring, we applied the technique to a large region of central Asia over a three-year period, monitoring for events at regional distances using three high-quality stations. We discuss methods for choosing quality templates and introduce a method for choosing correlation detection thresholds, tailored for each template, for a desired false alarm rate. Our SeisCorr software found more than 10,000 detections during the three-year period using almost 2000 templates. We discuss and evaluate three methods of confirming detections: bulletin confirmation, high correlation with a template, and multistation validation. At each station, 65%-75% of our detections could be confirmed, most by multistation validation. We confirmed over 6500 unique detections. For monitoring applications, it is of interest that a significant portion of the Comprehensive Nuclear-Test-Ban Treaty Organization's Late Event Bulletin (LEB) catalog events was detected and that adding our confirmed detections for the LEB catalog would more than double the catalog size. Waveform correlation also allows for relative magnitude calculation, and we explore the magnitudes of detected events. The results of our study suggest that doing broad regional monitoring using historical and real-time-generated templates is feasible and will increase detection capabilities.

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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.

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