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1. ARRU Phase Picker: Attention Recurrent-Residual U-Net for Picking Seismic P - and S -Phase Arrivals

   https://doi.org/10.1785/0220200382  

   Liao, Wu-Yu; Lee, En-Jui; Mu, Dawei; Chen, Po; Rau, Ruey-Juin (March 2021, Seismological Research Letters)

   null (Ed.)

   Abstract Seismograms are convolution results between seismic sources and the media that seismic waves propagate through, and, therefore, the primary observations for studying seismic source parameters and the Earth interior. The routine earthquake location and travel-time tomography rely on accurate seismic phase picks (e.g., P and S arrivals). As data increase, reliable automated seismic phase-picking methods are needed to analyze data and provide timely earthquake information. However, most traditional autopickers suffer from low signal-to-noise ratio and usually require additional efforts to tune hyperparameters for each case. In this study, we proposed a deep-learning approach that adapted soft attention gates (AGs) and recurrent-residual convolution units (RRCUs) into the backbone U-Net for seismic phase picking. The attention mechanism was implemented to suppress responses from waveforms irrelevant to seismic phases, and the cooperating RRCUs further enhanced temporal connections of seismograms at multiple scales. We used numerous earthquake recordings in Taiwan with diverse focal mechanisms, wide depth, and magnitude distributions, to train and test our model. Setting the picking errors within 0.1 s and predicted probability over 0.5, the AG with recurrent-residual convolution unit (ARRU) phase picker achieved the F1 score of 98.62% for P arrivals and 95.16% for S arrivals, and picking rates were 96.72% for P waves and 90.07% for S waves. The ARRU phase picker also shown a great generalization capability, when handling unseen data. When applied the model trained with Taiwan data to the southern California data, the ARRU phase picker shown no cognitive downgrade. Comparing with manual picks, the arrival times determined by the ARRU phase picker shown a higher consistency, which had been evaluated by a set of repeating earthquakes. The arrival picks with less human error could benefit studies, such as earthquake location and seismic tomography. 

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2. Detecting Urban Earthquakes with the San Fernando Valley Nodal Array and Machine Learning

   https://doi.org/10.1785/0220250124  

   Omojola, Joses; Persaud, Patricia (August 2025, Seismological Research Letters)

   Abstract The San Fernando Valley (SFV), part of the Los Angeles metropolitan area, is a seismically active urban environment. Large-magnitude earthquakes, such as the 1994 Mw 6.7 Northridge event that occurred on a blind fault beneath the valley, caused significant infrastructure damage in the region, underscoring the need for enhanced seismic monitoring to improve the identification of buried faults and hazard evaluation. Currently, the Southern California Earthquake Data Center operates four broadband instruments within the valley; however, the network’s ability to capture small earthquakes beneath the region may be limited. To demonstrate how this data gap can be filled, we use recordings from the SFV array, comprised of 140 nodal instruments with interstation distances ranging from 0.3 to 2.5 km that recorded for one month. High-anthropogenic noise levels in urbanized areas tend to conceal earthquake signals; therefore, we applied a previously developed machine learning model fine-tuned on similar waveforms to detect events and pick seismic phases. In a two-step event association workflow, isolated phase picks were first culled, which eliminated false positive detections and reduced computational runtime. We located 62 events within a 209 km radius of our array with magnitudes ranging from ML 0.13 to 4, including 36 new events that were undetected by the regional network. One event cluster reveals a previously unidentified (5.3 km × 4 km) blind fault zone located ∼5 km beneath the southern part of the valley. Seismicity from this zone is rare in the regional catalog (<3 events per year), despite producing a Mb 4.4 event in 2014. Our results highlight the benefits of detecting small-magnitude seismicity for hazard estimation. Temporary nodal arrays can identify critical gaps in regional monitoring and guide site selection for permanent stations. In addition, our workflow can be applied to complement seismic monitoring in other urban settings. 

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3. Microseismicity at the Time of a Large Creep Event on the Calaveras Fault is Unresponsive to Stress Changes

   https://doi.org/10.26443/seismica.v3i2.1337  

   Huang, Litong; Schwartz, Susan; Brodsky, Emily (July 2024, Seismica)

   The potential relationship between surface creep and deeper geological processes is unclear, even on one of the world’s best-studied faults. From June to August 2021, a large creep event with surface slip of more than 16 mm was recorded on the Calaveras fault in California, part of the San Andreas fault system. This event initially appeared to be accompanied by along-fault migration of seismicity, suggesting it penetrated to depth. Other studies have suggested that surface creep events are likely a shallow feature, unrelated to deep seismicity. To provide more detail on the relationship between earthquakes, surface creep, and potential aseismic slip at seismogenic depth, we tripled the number of earthquakes in the Northern California Earthquake Catalog in the region of the creep event for all of 2021. This was accomplished by implementing earthquake detection techniques based on both template matching (EQCorrscan) and AI-based automatic earthquake phase picking (PhaseNet). After manual inspection, the detected earthquakes were first located using Hypoinverse and subsequently relocated via GrowClust. Our enhanced catalog indicates that the spatiotemporal pattern of earthquakes here is not strongly influenced by the creep event and is better explained by structural heterogeneity than transient stress changes, indicating a decoupling of seismicity rate and surficial creep on this major fault. 

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4. Automated Detection of Volcanic Seismicity Using Network Covariance and Image Processing

   https://doi.org/10.1785/0220240014  

   Maher, Sean P; Dawson, Phillip B; Hotovec-Ellis, Alicia J; Thelen, Weston A; Matoza, Robin S (May 2024, Seismological Research Letters)

   Abstract Seismicity at restless volcanoes commonly features a variety of signal types reflecting both volcanotectonic and fluid-driven source processes. However, traditional catalogs of seismicity are often incomplete, especially concerning events with emergent onsets such as those driven by the dynamics of magmatic and hydrothermal fluids. The detection of all discrete events and continuous seismic tremors, regardless of the underlying source processes, would therefore improve the ability of monitoring agencies to forecast eruptions and mitigate their associated hazards. We present a workflow for generalized detection of seismic events based on the network covariance matrix (Seydoux et al., 2016). Our contributions enable the method to simultaneously detect continuous and short-duration (<∼10 s) events, provide information about the frequency content of the signals, and to refine the initial detection times by an order of magnitude (from window lengths of 75 to 7.5 s). We test the workflow on a 15-month record of seismicity with 23 stations at Mammoth Mountain, California (July 2012–October 2013) and detect 62% of long-period events and 94% of volcanotectonic events in the existing Northern California Earthquake Data Center catalog. In addition, ∼3000 events are not included in the catalog, and thousands of tremor signals are found. The method is suitable for near-real-time analysis of continuous waveforms and can provide a valuable supplement to existing algorithms to improve the completeness of catalogs used for monitoring volcanoes. 

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5. Rapid Earthquake Association and Location

   https://doi.org/10.1785/0220190052  

   Zhang, Miao; Ellsworth, William L.; Beroza, Gregory C. (September 2019, Seismological Research Letters)

   ABSTRACT Rapid association of seismic phases and event location are crucial for real‐time seismic monitoring. We propose a new method, named rapid earthquake association and location (REAL), for associating seismic phases and locating seismic events rapidly, simultaneously, and automatically. REAL combines the advantages of both pick‐based and waveform‐based detection and location methods. It associates arrivals of different seismic phases and locates seismic events primarily through counting the number of P and S picks and secondarily from travel‐time residuals. A group of picks are associated with a particular earthquake if there are enough picks within the theoretical travel‐time windows. The location is determined to be at the grid point with the most picks, and if multiple locations have the same maximum number of picks, the grid point among them with smallest travel‐time residuals. We refine seismic locations using a least‐squares location method (VELEST) and a high‐precision relative location method (hypoDD). REAL can be used for rapid seismic characterization due to its computational efficiency. As an example application, we apply REAL to earthquakes in the 2016 central Apennines, Italy, earthquake sequence occurring during a five‐day period in October 2016, midway in time between the two largest earthquakes. We associate and locate more than three times as many events (3341) as are in Italy's National Institute of Geophysics and Volcanology routine catalog (862). The spatial distribution of these relocated earthquakes shows a similar but more concentrated pattern relative to the cataloged events. Our study demonstrates that it is possible to characterize seismicity automatically and quickly using REAL and seismic picks. 

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