Some systemic risks to progress on seismic hazard assessment
No abstract available.
Seismological Research Letters↗
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Toppling of a Trona Pinnacles Spire following the M5.5 RidgecrestaAftershock of June 2020
The 2019 M w 7.1 Ridgecrest California earthquake rupture passed within 4 km of the Trona Pinnacles, a large group of tufa rock pillars. Reconnaissance following the Ridgecrest mainshock documented fresh damage to several of the Pinnacles. Repeated aerial photogrammetric surveys also documented damage during subsequent aftershocks. Here, we describe the photogrammetric data with emphasis on a specific rock spire that toppled during an M w 5.5 aftershock. We calculate the volumes of the intact spire and of its subsequent debris. To explore the utility of the pinnacles as fragile geologic features for constraining past earthquake shaking intensity, we calculate the quasi static, horizontal acceleration required to break the spire at its base. We also examine the response of this feature to observed shaking using a dynamic model of the spire. In this case, we find that the quasi‐static estimate provides a conservative maximum constraint on fragility. The dynamic model of the spire suggests that shaking during the M w 7.1 mainshock likely generated tensile stresses in excess of the spire’s bulk strength, thereby making it vulnerable to collapse in subsequent aftershocks.
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Ready for real time: Performance of Global Navigation Satellite System in 2019 Mw 7.1 Ridgecrest, California, rapid response products
Global Navigation Satellite Systems (GNSSs) have undergone notable advancement in the last few decades, leading to the availability of a dataset with capabilities well beyond its original intended purpose. The proliferation of high‐rate (1 Hz or greater) GNSS receivers in areas of seismological interest now allows for routine consideration of dynamic earthquake ground motions, with centimeter‐level displacement accuracy via precise point positioning methods. Real‐time (RT) GNSS observations, from stations that are both telemetered and processed to displacement with minimal latency, have lower accuracy compared to post‐processed (PP) GNSS displacements due to imprecise knowledge of atmospheric conditions, satellite clocks, and satellite orbits in RT. Whether the quality of RT high‐rate GNSS is sufficient for use in rapid response products remains to be thoroughly examined. Here, we highlight RT GNSS displacement time series processed during the 2019 M w "> M w Mw 7.1 Ridgecrest, California, earthquake in the context of common rapid‐response products, magnitude estimation, and kinematic fault‐slip models. We discuss how these data can be used to supplement RT seismic data for rapid characterization of significant earthquakes. We find that kinematic fault‐slip models using RT GNSS data retain the general spatiotemporal characteristics of those with PP data, with subtle differences in size and amplitude of modeled slip asperities. We demonstrate the effect of these rapid seismic source models using RT GNSS data on the U.S. Geological Survey product ShakeMap—a downstream ground‐motion prediction algorithm informed by the rupture dimensions estimated in the slip model. Discrepancies in the ShakeMap estimate are minor, within ±12% change, with the most severe variation at the fault edges. Our analysis suggests that, when used in conjunction with available seismic data sources, RT GNSS is sufficient and valuable for rapid earthquake characterization.
California↗
Classifying Worldwide Standardized Seismograph Network records using a simple convolution neural network
The U.S. Geological Survey (USGS) maintains an archive of 189,180 digitized scans of analog seismic records from the World‐Wide Standardized Seismograph Network (WWSSN). Although these scans have been made public, the archive is too large to manually review, and few researchers have utilized large numbers of these records. To facilitate further research using this historical dataset, we develop a simple convolutional neural network (CNN) that rapidly (∼4.75 s/film chip) classifies scanned film chip images (called “chips,” because they are individually cut segments of 70 mm film) into four categories of “interestingness” to earthquake seismologists based on the presence of earthquakes and other seismic signals in the record: “no interest,” “little interest,” “interest,” and “high interest.” The CNN, dubbed “Seismic Analog Record Network” (SARNet), can identify four types of seismic traces (“no events,” “minor events,” “major events,” and “errors”) in 200 × 200 pixel subcrops with an accuracy of 92% using a confidence threshold of 85%. SARNet then converts 100 random subcrops from each film chip into the overall classification of interestingness. In this task, SARNet performed as well as expert human classifiers in determining the film chip’s overall interest grade. Applying SARNet to 34,000 film chips in the WWSSN archive found that 21% of the images were of “high interest” and had an “indeterminate” rate of only 4%. Thus, the need for the manual review of images was reduced by 79%. Sorting of film chips derived from SARNet will expedite further exploration of the archive of digitized analog seismic records stored at the USGS.
Seismological Research Letters↗
Working with dynamic earthquake rupture models: A practical guide
Dynamic rupture models are physics‐based simulations that couple fracture mechanics to wave propagation and are used to explain specific earthquake observations or to generate a suite of predictions to understand the influence of frictional, geometrical, stress, and material parameters. These simulations can model single earthquakes or multiple earthquake cycles. The objective of this article is to provide a self‐contained and practical guide for students starting in the field of earthquake dynamics. Senior researchers who are interested in learning the first‐order constraints and general approaches to dynamic rupture problems will also benefit. We believe this guide is timely given the recent growth of computational resources and the range of sophisticated modeling software that are now available. We start with a succinct discussion of the essential physics of earthquake rupture propagation and walk the reader through the main concepts in dynamic rupture model design. We briefly touch on fully dynamic earthquake cycle models but leave the details of this topic for other publications. We also highlight examples throughout that demonstrate the use of dynamic rupture models to investigate various aspects of the faulting process.
Seismological Research Letters↗
Beyond the teleseism: Introducing regional seismic and geodetic data into routine USGS finite‐fault modeling
The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) routinely produces finite‐fault models following significant earthquakes. These models are spatiotemporal estimates of coseismic slip critical to constraining downstream response products such as ShakeMap ground motion estimates, Prompt Assessment of Global Earthquake for Response loss estimates, and ground failure assessments. Because large earthquakes can involve slip over tens to hundreds of kilometers, point‐source approximations are insufficient, and it is vital to rapidly assess the amount, timing, and location of slip along the fault. Initially, the USGS finite‐fault products were computed in the first several hours after a significant earthquake, using teleseismic body wave and surface wave observations. With only teleseismic waveforms, it is generally possible to obtain a reliable model for earthquakes of magnitude 7 and larger. Here, we detail newly implemented updates to NEIC’s modeling capabilities, specifically to allow joint modeling of local‐to‐regional strong‐motion accelerometer, Global Navigation Satellite System (GNSS), and Interferometric Synthetic Aperture Radar (InSAR) observations in addition to teleseismic waveforms. We present joint inversion results for the 2015 M w ">
Seismological Research Letters↗
Testing the ShakeAlert earthquake early warning system using synthesized earthquake sequences
We test the behavior of the United States (US) West Coast ShakeAlert earthquake early warning (EEW) system during temporally close earthquake pairs to understand current performance and limitations. We consider performance metrics based on source parameter and ground‐motion forecast accuracy, as well as on alerting timeliness. We generate ground‐motion times series for synthesized earthquake sequences from real data by combining the signals from pairs of well‐recorded earthquakes (4.4≤M≤7.1) using time shifts ranging from −60 to +180 s. We examine fore‐ and aftershock sequences, near‐simultaneous events in different source regions, and simulated out‐of‐network and offshore earthquakes. We find that the operational ShakeAlert algorithms Earthquake Point‐source Integrated Code (EPIC) and Finite‐Fault Rupture Detector (FinDer) and the Propagation of Local Undamped Motion (PLUM) method perform largely as expected: EPIC provides the best source location estimates and is often fastest but can underestimate magnitudes or, in extreme cases, miss large earthquakes; FinDer provides real‐time line‐source models and unsaturated magnitude estimates for large earthquakes but currently cannot process concurrent events and may mislocate offshore earthquakes; PLUM identifies pockets of strong ground motion, but can overestimate alert areas. Implications for system performance are: (1) spatially and temporally close events are difficult to identify separately; (2) challenging scenarios with foreshocks that are close in space and time can lead to missed alerts for large earthquakes; and (3) in these situations the algorithms can often estimate ground motion better than source parameters. To improve EEW, our work suggests revisiting the current algorithm weighting in ShakeAlert, to continue developments that focus on using ground‐motion data to aggregate alerts from multiple algorithms, and to investigate methods to optimally leverage algorithm ground‐motion estimates. For testing and certification of EEW performance in ShakeAlert and other EEW systems where applicable, we also suggest that 25 of our 73 scenarios become part of the baseline data set.
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Using machine learning techniques with incomplete polarity datasets to improve earthquake focal mechanism determination
Earthquake focal mechanisms are traditionally produced using P ‐wave first‐motion polarities and commonly require well‐recorded seismicity. A recent approach that is less dependent on high signal‐to‐noise exploits similar waveforms to produce relative polarity measurements between earthquake pairs. Utilizing these relative polarity measurements, it is possible to produce composite focal mechanisms for clusters within microseismic sequences using regional networks. However, missing or low‐confidence polarity measurements still limit our ability to calculate high‐quality composite focal mechanisms. Here, we replaced unreliable polarity measurements with estimates using iterative random forests, an unsupervised ensemble machine learning method. Using the imputed (“replaced”) polarity data, we then categorically clustered the events into families. As a case study, we applied this modified composite mechanism workflow to a multistation template matched catalog of an earthquake swarm that occurred during 2020 near the Maacama fault in northern California. We found that our modified methodology produced higher‐quality earthquake families and improved composite focal mechanisms, with fault‐plane uncertainties <35° for 94% of the families compared with 34% of families using the previous methodology.
Seismological Research Letters↗
An interactive viewer to improve operational aftershock forecasts
The U.S. Geological Survey (USGS) issues forecasts for aftershocks about 20 minutes after most earthquakes above M 5 in the United States and its territories, and updates these forecasts 75 times during the first year. Most of the forecasts are issued automatically, but some forecasts require manual intervention to maintain accuracy. It is important to identify the sequences whose forecasts will benefit from a modified approach so the USGS can provide accurate information to the public. The oaftools R package ( Paris and Michael, 2022 ) includes functions that analyze and plot earthquake sequences and their forecasts to identify which sequences require such intervention. The package includes the Operational Aftershock Forecast (OAF) Viewer, which incorporates the functions into an interactive web environment that can be used to explore aftershock sequences. The OAF Viewer starts with a global map and table of mainshocks. After a mainshock has been selected, the map and a new table show its aftershocks and the OAF Viewer generates five analytical plots: (1) magnitude–time, which is used to look for patterns in the data; (2) cumulative number, to see how the productivity of the sequence compares to a Reasenberg and Jones (1989) aftershock model over time; (3) magnitude–frequency, to compare the ratio of large to small magnitudes and extrapolate to higher magnitudes with sparse data and lower magnitudes with incomplete data; (4) forecast success, to compare the forecasts with observations for a sequence; and (5) parameter–time, which examines the temporal evolution of the forecast model parameters. The user can interact with the functions provided by the oaftools package through the OAF Viewer or by incorporating the functions into their own analysis methods. The OAF Viewer will help seismologists understand complexities in the data, communicate with the public and emergency managers, and improve the OAF system by maintaining operational awareness.
Seismological Research Letters↗
Revised earthquake recurrence intervals in California, USA: New paleoseismic sites and application of event likelihoods
Recurrence intervals for ground rupturing earthquakes are critical data for assessing seismic hazard. Recurrence intervals are presented here for 38 paleoseismic sites in California. Eleven of these include new or updated data; the remainder use data previously included in the Unified California Earthquake Rupture Forecast Version 3 (UCERF3). The methods and results are consistent with UCERF3. In addition, revised recurrence intervals are presented at every site. The revised recurrence intervals incorporate uncertainty in the interpretation of paleoseismic evidence, which is expressed as event likelihood. Event likelihood is the probability that the evidence has been correctly interpreted as a unique earthquake. Event likelihoods are estimated here for 85 inferred past earthquakes at eight paleoseismic sites in California, using a single, consistent methodology. The average event likelihood is 0.85. The revised recurrence intervals are 16% longer, on average, than conventional estimates, and their confidence intervals are disproportionately wider. These recurrence intervals are suitable for inclusion in a “grand inversion” rupture forecast, and they may be important for addressing a systematic misfit in the UCERF3 grand inversion. The revised recurrence intervals may also be important for assessing the unusually long earthquake hiatus in California. Other applications may not need to consider event likelihoods because the effects are small relative to typical uncertainties.
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Viscoelastic fault-based model of crustal deformation for the 2023 update to the U.S. National Seismic Hazard Model
The 2023 update to the National Seismic Hazard (NSHM) model is informed by several deformation models that furnish geodetically estimated fault slip rates. Here I describe a fault‐based model that permits estimation of long‐term slip rates on discrete faults and the distribution of off‐fault moment release. It is based on quantification of the earthquake cycle on a viscoelastic model of the seismogenic upper crust and ductile lower crust and mantle. I apply it to a large dataset of horizontal and vertical Global Positioning System (GPS) interseismic velocities in the western United States, resulting in long‐term slip rates on more than 1000 active faults defined for the NSHM. A reasonable fit to the GPS dataset is achieved with a set of slip rates designed to lie strictly within a priori geologic slip rate bounds. Time‐dependent effects implemented via a “ghost transient” have a profound effect on slip rate estimation and tend to raise calculated slip rates along the northern and southern San Andreas fault by up to several mm/yr.
Seismological Research Letters↗
Western U.S. deformation models for the 2023 update to the U.S. National Seismic Hazard Model
This report describes geodetic and geologic information used to constrain deformation models of the 2023 update to the National Seismic Hazard Model (NSHM), a set of deformation models to interpret these data, and their implications for earthquake rates in the western United States. Recent updates provide a much larger data set of Global Positioning System crustal velocities than used in the 2014 NSHM, as well as hundreds of new faults considered as active sources for the 2023 NSHM. These data are interpreted by four geodetic models of deformation that estimate fault slip rates and their uncertainties together with off‐fault moment release rates. Key innovations in the 2023 NSHM relative to past practice include (1) the addition of two new (in addition to two existing) deformation models, (2) the revision and expansion of the geologic slip rate database, (3) accounting for fault creep through development of a creep‐rate model that is employed by the four deformation models, and (4) accounting for time‐dependent earthquake‐cycle effects through development of viscoelastic models of the earthquake cycle along the San Andreas fault and the Cascadia subduction zone. The effort includes development of a geologic deformation model that complements the four geodetic models. The current deformation models provide a new assessment of outstanding discrepancies between geologic and geodetic slip rates, at the same time highlighting the need for both geologic and geodetic slip rates to robustly inform the earthquake rate model.
Seismological Research Letters↗
Western U.S. geologic deformation model for use in the U.S. National Seismic Hazard Model 2023
Fault geometry and slip rates are key input data for geologic deformation models, which are a fundamental component of probabilistic seismic hazard analyses (PSHAs). However, geologic sources for PSHA have traditionally been limited to faults with field‐based slip rate constraints, which results in underrepresentation of known, but partially characterized, active faults. Here, we evaluate fault geometries and geologic fault slip rates for the western United States to construct a new geologic deformation model for the U.S. National Seismic Hazard Model 2023 update (NSHM23). In previous NSHM iterations, only faults with published geologic slip rates were included. In the NSHM23 fault sections database compilation, this inclusion criterion was expanded to include faults without known slip rates. In this updated geologic deformation model, preferred slip rates and associated uncertainty distributions are incorporated for faults with slip rates derived from field studies. For faults without site‐specific slip rates, we evaluate a suite of uncertainty distributions derived from broad slip rate categories in the U.S. Geological Survey Quaternary Fault and Fold Database. Preferred slip rate distributions are selected via comparison with geodetic strain rates in tectonic subregions. The resultant moment of the geologic deformation model is generally in deficit compared with the geodetic moment within each region. Primary advances in the NSHM23 geologic deformation model include the following: (1) slip rates are presented as preferred values with uncertainties rather than single values; (2) the representation of the western U.S. active fault network is more complete; and (3) the geologic deformation model leverages geodetic information to assess regional constraints on geologic fault slip rates.
Seismological Research Letters↗
Introduction to the digitization of seismic data: A user’s guide
Modern seismic data are collected, distributed, and analyzed using digital formats, and this has become a standard for the field. Although most modern seismometers still make use of analog electronic circuits, their data are converted from an analog voltage output to time‐tagged counts by way of digitization. Although much of the digitization process is not complicated to conceptualize, there is a fair bit of jargon in digitizer specifications, and a few pitfalls that can arise in the processes of recording and analyzing ground‐motion data. In this article, we review some of the fundamental physical properties of data acquisition systems and the basic steps in digitizing data from an analog instrument (specifically a seismometer). We then briefly discuss the digitization process and some of the key properties needed to make these data useful for seismological applications. Finally, we discuss some of the filtering processes that naturally arise from digitization and how it can affect the processing workflow. The end goal is to provide a user guide that will enable seismologists to have a working knowledge of the digitization process. We focus on aspects central to seismological applications and have tried to avoid getting bogged down in signal processing formalism.
Seismological Research Letters↗
Education initiatives to support earthquake early warning: A retrospective and a roadmap
As of May 2021, public alerting is now operational for the ShakeAlert earthquake early warning system for the West Coast of the United States in California, Oregon, and Washington. Successful early warning systems require the scientific and technical implementation to be coupled with social and humanitarian considerations, including education and outreach campaigns. Community engagement with the over 50 million people who live in ShakeAlert states is important to increase public safety, security, and awareness of local earthquake hazards, how to prepare, and how ShakeAlert earthquake early warning can help. Here, we describe the efforts of the ShakeAlert Educational Resources Working Group around the rollout of public alerting in the Pacific Northwest for Oregon in March 2021 and Washington in May 2021, respectively. Our initial approach was to engage in formative dialogue with community members in each ShakeAlert state, develop educational activities and animations based on the collective feedback, and then to disseminate our resources in both formal (K‐16) and informal (free choice) learning environments through workshops and outreach events. The rollout of public alerting in the Pacific Northwest provided an opportunity to directly engage with various publics around a specific event, and to collaborate with local news and social media, communications and social science professionals, educators, emergency managers, and scientific and technical experts on the ShakeAlert system. Following the rollouts, we developed a strategic plan for the next five years of ShakeAlert to promote earthquake early warning, as the importance of earthquake preparedness competes with the stressors of everyday life. Because earthquake early warning systems are rapidly expanding worldwide, our education and outreach efforts provide a roadmap for building successful education and outreach campaigns, leading up to public alerting and maintaining earthquake preparedness in the public consciousness following rollout.
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Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model
It has been nearly a decade since updates to seismic and fault sources in the central and eastern United States (CEUS) were last assessed for the 2012 Central and Eastern United States Seismic Source Characterization for nuclear facilities (CEUS-SSCn) and 2014 United States Geological Survey National Seismic Hazard Model (NSHM) for the conterminous U.S. In advance of the 2023 NSHM update, we created 3 related geospatial databases to summarize and characterize new fault source information for the CEUS. These include fault section, fault-zone polygon, and earthquake geology (fault slip rate, earthquake recurrence intervals) databases which document updates to fault parameters used in prior seismic hazard models in this region. The 2012 CEUS-SSCn and 2014 NSHM fault models served as a foundation, as we revised and added fault sources where new published studies documented significant changes to our understanding of fault location, geometry, or activity. We added 9 new fault sections that meet the criteria of (1) a length ≥7 km, (2) evidence of recurrent Quaternary tectonic activity, and (3) documentation that is publicly available in a peer-reviewed source. The prior CEUS models only included 6 fault sections (sources) and 10 fault-zone polygons (previously called repeating large magnitude earthquake (RLME) polygons). The revised databases include 15 fault sections and 10 fault zone polygons. Updates to the faults constitute a 150% increase in fault sections, but no change in the number of fault-zone polygons, although some fault-zone polygons differ from RLME polygons used in prior models. No faults were removed from past models. Several seismic zones and suspected faults were evaluated but not included in this update due to a lack of information about fault location, geometry, or recurrent Quaternary activity. These updates to the fault sections, fault-zone polygons, and earthquake geology databases will inform fault geometry and activity rates of CEUS sources during the 2023 NSHM implementation.
Seismological Research Letters↗
Lower seismogenic depth model of western U.S. Earthquakes
We present a model of the lower seismogenic depth of earthquakes in the western United States (WUS) estimated using the hypocentral depths of events M > 1, a crustal temperature model, and historical earthquake rupture depth models. Locations of earthquakes are from the Advanced National Seismic System Comprehensive Earthquake Catalog from 1980 to 2021 supplemented with seismicity in southern California for event hypocenters that were relocated by Hauksson et al. (2012) to obtain higher precision and better resolution in the model. We calculated the average depth of the deepest 10% of the merged catalog using an adaptive radius of 50 km or more. Along the San Andreas fault, the deepest seismogenic depths are located at 23 km around the Cholame segment, whereas the shallowest depths are located at about 10 km along the Rodgers Creek and Maacama faults. For the WUS outside California, the depth generally varies between 10 and 25 km with an average around 14 km but could extend to 35 km along Cascadia subduction zone. We find good agreement between the small‐magnitude depths and rupture depths derived from coseismic slip of large earthquakes across the region. Our estimates are generally deeper than the previous seismogenic depths determined for the Uniform California Earthquake Rupture Forecast, Version 3 model based on work by Petersen et al. (1996) who used seismicity cross sections along major fault zones in California. Our new seismogenic depth distribution correlates closely with crustal temperature derived from WUS heat flow ( Blackwell et al. , 2011 ). This correlation allowed us to develop a map of the brittle–ductile transition that we use to replace seismogenic depths in the model east of the Intermountain West Seismic Belt where the seismicity rate is low. This updated depth model is useful for recalibrating the lower geologic fault rupture depths, and constraining deformation and seismicity source models in updates of the U.S. Geological Survey National Seismic Hazard Model.
western United States↗
GPS velocity field of the Western United States for the 2023 National Seismic Hazard Model update
Global Positioning System (GPS) velocity solutions of the western United States (WUS) are compiled from several sources of field networks and data processing centers for the 2023 U.S. Geological Survey National Seismic Hazard Model (NSHM). These solutions include both survey and continuous‐mode GPS velocity measurements. I follow the data processing procedure of Parsons et al. (2013) for the Uniform California Earthquake Rupture Forecast, version 3 and McCaffrey, Bird, et al. (2013) and Zeng and Shen (2013) for their WUS deformation models in support of the 2014 NSHM update. All GPS velocity vectors are first rotated to a common North American reference frame. I edit the velocities to remove outliers and data with significant influence from volcanism. The solutions are then combined into a final GPS velocity field consisting of 4979 horizontal velocity vectors. I compute strain rates based on these GPS velocities using the method of Shen et al. (2015) . These strain rates correlate closely with seismicity rates in the WUS. The results are used for WUS geodetic and geologic deformation modeling in support of the 2023 NSHM update.
Seismological Research Letters↗