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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. Seismic Traveltime Tomography of Southern California Using Poisson‐Voronoi Cells and 20 Years of Data

   https://doi.org/10.1029/2021JB023307  

   Fang, Hongjian ; White, Malcolm C. A. ; Lu, Yang ; Ben‐Zion, Yehuda ( May 2022 , Journal of Geophysical Research: Solid Earth)

   We derive new, 3D, isotropic models of seismic compressional and shear wavespeeds, Vp and Vs, respectively, their ratio, Vp/Vs, and a catalog of relocated earthquakes for Southern California from more than 10 million P‐ and S‐wave arrivals associated with over 0.3 million earthquakes that occurred between 2000 and 2020. We augment high‐quality analyst‐reviewed phase arrival picks from the Southern California Earthquake Data Center with S‐wave arrival picks obtained with an automated algorithm, and we derive new wavespeed models via traveltime tomography formulated using Poisson‐Voronoi cells (Fang et al., 2020,https://doi.org/10.1785/0220190141). The results contribute to improved regional wavespeed models, particularly the Vp/Vs model, and absolute event locations. The obtained models correlate well with regional geological features and yield more accurate synthetic waveforms than other regional models do for waves with periods shorter than 5 s in much of the modeled region. The derived event catalog exhibits tighter spatial clustering than the standard regional catalog, thereby helping to characterize subsurface features of major faults. The regional 1D averaged Vp/Vs ratio shows high values at shallow depths, decreases to a minimum at about 10 km, then increases again at greater depths below 15 km. Deep seismicity correlates well with regions of Vp/Vs ratio lower than 1.75, which may indicate an increased brittle‐to‐ductile transition depth with an important influence on crustal mechanics. The new wavespeed models and seismic catalog can be useful for various studies including analyses of seismicity patterns and simulations of crustal deformation and ground motion.

    

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3. PhaseLink: A Deep Learning Approach to Seismic Phase Association

   https://doi.org/10.1029/2018JB016674  

   Ross, Zachary E. ; Yue, Yisong ; Meier, Men‐Andrin ; Hauksson, Egill ; Heaton, Thomas H. ( January 2019 , Journal of Geophysical Research: Solid Earth)

   Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks and underlies most seismicity catalogs produced around the world. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. We present PhaseLink, a framework based on recent advances in deep learning for grid‐free earthquake phase association. Our approach learns to link phases together that share a common origin and is trained entirely on millions of synthetic sequences ofPandSwave arrival times generated using a 1‐D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real‐time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training data set. We demonstrate the state‐of‐the‐art performance of PhaseLink on a challenging sequence from southern California and synthesized sequences from Japan designed to test the point at which the method fails. For the examined data sets, PhaseLink can precisely associate phases to events that occur only ∼12 s apart in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real‐time seismic monitoring, and streamline automated processing of large seismic data sets.

    

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4. Source parameters inversion of the global large earthquakes using 3-D SEM Green's functions: strain Green's function calculation and validation

   https://doi.org/10.1093/gji/ggac135  

   Zhang, Lei ; Hao, Jinlai ; Deng, Wenze ; Ji, Chen ; Zhang, Chuhan ( April 2022 , Geophysical Journal International)

   Precisely constraining the source parameters of large earthquakes is one of the primary objectives of seismology. However, the quality of the results relies on the quality of synthetic earth response. Although earth structure is laterally heterogeneous, particularly at shallow depth, most earthquake source studies at the global scale rely on the Green's functions calculated with radially symmetric (1-D) earth structure. To avoid the impact of inaccurate Green's functions, these conventional source studies use a limited set of seismic phases, such as long-period seismic waves, broad-band P and S waves in teleseismic distances (30° < ∆ < 90°), and strong ground motion records at close-fault stations. The enriched information embedded in the broad-band seismograms recorded by global and regional networks is largely ignored, limiting the spatiotemporal resolution. Here we calculate 3-D strain Green's functions at 30 GSN stations for source regions of 9 selected global earthquakes and one earthquake-prone area (California), with frequency up to 67 mHz (15 s), using SPECFEM3D_GLOBE and the reciprocity theorem. The 3-D SEM mesh model is composed of mantle model S40RTS, crustal model CRUST2.0 and surface topography ETOPO2. We surround each target event with grids in horizontal spacing of 5 km and vertical spacing of 2.0–3.0 km, allowing us to investigate not only the main shock but also the background seismicity. In total, the response at over 210 000 source points is calculated in simulation. The number of earthquakes, including different focal mechanisms, centroid depth range and tectonic background, could further increase without additional computational cost if they were properly selected to avoid overloading individual CPUs. The storage requirement can be reduced by two orders of magnitude if the output strain Green's functions are stored for periods over 15 s. We quantitatively evaluate the quality of these 3-D synthetic seismograms, which are frequency and phase dependent, for each source region using nearby aftershocks, before using them to constrain the focal mechanisms and slip distribution. Case studies show that using a 3-D earth model significantly improves the waveform similarity, agreement in amplitude and arrival time of seismic phases with the observations. The limitations of current 3-D models are still notable, dependent on seismic phases and frequency range. The 3-D synthetic seismograms cannot well match the high frequency (>40 mHz) S wave and (>20 mHz) Rayleigh wave yet. Though the mean time-shifts are close to zero, the standard deviations are notable. Careful calibration using the records of nearby better located earthquakes is still recommended to take full advantage of better waveform similarity due to the use of 3-D models. Our results indicate that it is now feasible to systematically study global large earthquakes using full 3-D earth response in a global scale.

    

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5. Enhanced Regional Earthquake Catalog with Alaska Amphibious Community Seismic Experiment Data

   https://doi.org/10.1785/0220220226  

   Ruppert, Natalia A. ; Barcheck, Grace ; Abers, Geoffrey A. ( November 2022 , Seismological Research Letters)

   Abstract The Alaska Amphibious Community Seismic Experiment (AACSE) comprised 75 ocean-bottom seismometers and 30 land stations and covered about 650 km along the segment of the subduction zone that includes Kodiak Island, the Alaska Peninsula and the Shumagin Islands between May 2018 and September 2019. This unprecedented onshore-offshore dataset provided an opportunity to compile a greatly enhanced earthquake catalog for the region by both increasing the number of detected earthquakes and improving the accuracy of their source parameters. We use all available regional and AACSE campaign seismic data to compile an earthquake catalog for the region between Kodiak and the Shumagin Islands including the Alaska Peninsula (51° N–59° N, 148° W–163° W). We apply the same processing and reporting standards to additional picks and events as the Alaska Earthquake Center currently uses for compilation of the authoritative regional earthquake catalog. Over 7200 events (both newly detected and previously reported) have been processed with AACSE data. We added about 30% more events, 60% more phase picks, lowered the magnitude of completeness by about 0.2 on average across the region, and improved location errors. All data have been published in public data archives. In addition, we test the machine-learning earthquake detection and picking algorithm EarthquakeTransformer (EQT) on the AACSE seismic dataset, comparing EQT-determined P and S picks with the new catalog. EQT is entirely trained on land data, whereas AACSE is amphibious. Overall, EQT finds 59% of P and 63% of S arrivals in the catalog within 300 km epicentral distance. The percent of catalog picks detected by EQT varies inversely with earthquake epicentral distance, and EQT performs particularly poorly on data from earthquakes recorded by instruments in the outer rise. 

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