Comparing Traditional and Deep Learning Signal Features for Event Detection in the Utah Region

Abstract

ABSTRACT 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.