Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ∼5  dB, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ∼0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ∼2–5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ∼15  dB.

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The capability to discriminate low‐magnitude earthquakes from low‐yield anthropogenic sources, both detectable only at local distances, is of increasing interest to the event monitoring community. We used a dataset of seismic events in Utah recorded during a 14‐day period (1–14 January 2011) by the University of Utah Seismic Stations network to perform a comparative study of event classification at local scale using amplitude ratio (AR) methods and a machine learning (ML) approach. The event catalog consists of 7377 events with magnitudes MC ranging from −2 and lower up to 5.8. Events were subdivided into six populations based on location and source type: tectonic earthquakes (TEs), mining‐induced events (MIEs), and mining blasts from four known mines (WMB, SMB, LMB, and CQB). The AR approach jointly exploits Pg‐to‐Sg phase ARs and Rg‐to‐Sg spectral ARs in multivariate quadratic discriminant functions and was able to classify 370 events with high signal quality from the three groups with sufficient size (TE, MIE, and SMB). For that subset of the events, the method achieved success rates between about 80% and 90%. The ML approach used trained convolutional neural network (CNN) models to classify the populations. The CNN approach was able to classify the subset of events with accuracies between about 91% and 98%. Because the neural network approach does not have a minimum signal quality requirement, we applied it to the entire event catalog, including the abundant extremely low-magnitude events, and achieved accuracies of about 94%–100%. We compare the AR and ML methodologies using a broad set of criteria and conclude that a major advantage to ML methods is their robustness to low signal‐to‐noise ratio data, allowing them to classify significantly smaller events.

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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.