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1. 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 themore »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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2. Toward Fully Autonomous Seismic Networks: Backprojecting Deep Learning-Based Phase Time Functions for Earthquake Monitoring on Continuous Recordings

   https://doi.org/10.1785/0220210274  

   Liao, Wu-Yu ; Lee, En-Jui ; Mu, Dawei ; Chen, Po ( March 2022 , Seismological Research Letters)

   Abstract Accurate and (near) real-time earthquake monitoring provides the spatial and temporal behaviors of earthquakes for understanding the nature of earthquakes, and also helps in regional seismic hazard assessments and mitigations. Because of the increase in both the quality and quantity of seismic data, an automated earthquake monitoring system is needed. Most of the traditional methods for detecting earthquake signals and picking phases are based on analyses of features in recordings of an individual earthquake and/or their differences from background noises. When seismicity is high, the seismograms are complicated, and, therefore, traditional analysis methods often fail. With the development of machine learning algorithms, earthquake signal detection and seismic phase picking can be more accurate using the features obtained from a large amount of earthquake recordings. We have developed an attention recurrent residual U-Net algorithm, and used data augmentation techniques to improve the accuracy of earthquake detection and seismic phase picking on complex seismograms that record multiple earthquakes. The use of probability functions of P and S arrivals and potential P and S arrival pairs of earthquakes can increase the computational efficiency and accuracy of backprojection for earthquake monitoring in large areas. We applied our workflow to monitor the earthquake activitymore »in southern California during the 2019 Ridgecrest sequence. The distribution of earthquakes determined by our method is consistent with that in the Southern California Earthquake Data Center (SCEDC) catalog. In addition, the number of earthquakes in our catalog is more than three times that of the SCEDC catalog. Our method identifies additional earthquakes that are close in origin times and/or locations, and are not included in the SCEDC catalog. Our algorithm avoids misidentification of seismic phases for earthquake location. In general, our algorithm can provide reliable earthquake monitoring on a large area, even during a high seismicity period.« less
3. Precision Seismic Monitoring and Analysis at Axial Seamount Using a Real‐Time Double‐Difference System

   https://doi.org/10.1029/2019JB018796  

   Waldhauser, F. ; Wilcock, W. S. D. ; Tolstoy, M. ; Baillard, C. ; Tan, Y. J. ; Schaff, D. P. ( May 2020 , Journal of Geophysical Research: Solid Earth)

   Seven three‐component ocean bottom seismometers (OBS) of the Ocean Observatories Initiative (OOI) Cabled Array on top of Axial Seamount are continuously streaming data in real time to the Incorporated Research Institutions for Seismology (IRIS). The OBS array records earthquakes from the submarine volcano which last erupted on 24 April 2015, about 4 months after the array came online. The OBS data have proven crucial in providing insight into the volcano structure and dynamics (Wilcock et al., 2016,https://doi.org/10.1126/science.aah5563). We implemented a real‐time double‐difference (RT‐DD) monitoring system that automatically computes high‐precision (tens of meters) locations of new earthquakes. The system's underlying double‐difference base catalog includes nearly 100,000 earthquakes and was computed using kurtosis phase onset picks, cross‐correlation phase delay times, and 3‐DPandSvelocity models to predict the data. The relocations reveal the fine‐scale structures of long‐lived, narrow (<200 m wide), outward dipping, convex faults on the east and west walls of the caldera that appear to form a figure 8‐shaped ring fault system. These faults accommodate stresses caused by the inflation of magma prior to and deflation during eruptions. The east fault is segmented and pulled apart in east‐west direction due to its interaction with the Juan de Fuca Ridge, which at thismore »location forms an overlapping spreading center. The RT‐DD system enables the monitoring and rapid analysis of variations in fine‐scale seismic and fault properties and has the potential to improve prediction of timing and location of the next Axial eruption expected to occur in the 2022–2023 time frame.

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4. A Detailed Earthquake Catalog for the San Jacinto Fault‐Zone Region in Southern California

   https://doi.org/10.1029/2019JB017641  

   White, Malcolm C. A. ; Ben‐Zion, Yehuda ; Vernon, Frank L. ( July 2019 , Journal of Geophysical Research: Solid Earth)

   We develop an automated processing procedure to derive a new catalog of earthquake locations, magnitudes, and potencies and analyze 9 years of data between 2008 and 2016 in the San Jacinto fault‐zone region. Our procedure accounts for detailed 3‐D velocity structure using a probabilistic global‐search location inversion and obtains high‐precision relative event locations using differential travel times measured by cross‐correlating waveforms. The obtained catalog illuminates spatiotemporal seismicity patterns in the fault zone with observations for 108,800 earthquakes in the magnitude range −1.8 to 5.4. Inside a focus region consisting of an 80‐km by 50‐km rectangle oriented parallel to the main fault trace, we estimate a 99% detection rate of earthquakes with magnitude 0.6 and greater and detect and locate about 60% more events than those present in the Southern California Seismic Network catalog. The results provide the most complete catalog available for the focused study region during the analyzed period and include both deeper events and very shallow patches of seismicity not present in the regional catalog. The seismicity exhibits a variety of complex patterns that contain important information on deformation processes in the region. The fraction of event pairs with waveforms having cross‐correlation coefficients ≥0.95 is only about 3%,more »indicating diverse processes operating in the fault zone.

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5. Local Explosion Detection and Infrasound Localization by Reverse Time Migration Using 3-D Finite-Difference Wave Propagation

   https://doi.org/10.3389/feart.2021.620813  

   Fee, David ; Toney, Liam ; Kim, Keehoon ; Sanderson, Richard W. ; Iezzi, Alexandra M. ; Matoza, Robin S. ; De Angelis, Silvio ; Jolly, Arthur D. ; Lyons, John J. ; Haney, Matthew M. ( February 2021 , Frontiers in Earth Science)

   Infrasound data are routinely used to detect and locate volcanic and other explosions, using both arrays and single sensor networks. However, at local distances (<15 km) topography often complicates acoustic propagation, resulting in inaccurate acoustic travel times leading to biased source locations when assuming straight-line propagation. Here we present a new method, termed Reverse Time Migration-Finite-Difference Time Domain (RTM-FDTD), that integrates numerical modeling into the standard RTM back-projection process. Travel time information is computed across the entire potential source grid via FDTD modeling to incorporate the effects of topography. The waveforms are then back-projected and stacked at each grid point, with the stack maximum corresponding to the likely source. We apply our method to three volcanoes with different network configurations, source-receiver distances, and topography. At Yasur Volcano, Vanuatu, RTM-FDTD locates explosions within ∼20 m of the source and differentiates between multiple vents. RTM-FDTD produces a more accurate location for the two Yasur subcraters than standard RTM and doubles the number of detected events. At Sakurajima Volcano, Japan, RTM-FDTD locates the source within 50 m of the active vent despite notable topographic blocking. The RTM-FDTD location is similar to that from the Time Reversal Mirror method, but is more computationally efficient. Lastly, at Shishaldinmore »Volcano, Alaska, RTM and RTM-FDTD both produce realistic source locations (<50 m) for ground-coupled airwaves recorded on a four-station seismic network. We show that RTM is an effective method to detect and locate infrasonic sources across a variety of scenarios, and by integrating numerical modeling, RTM-FDTD produces more accurate source locations and increases the detection capability.« less