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At least 235 records · Page 13Linked to original sources
USGS2022

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↗
USGS2022

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↗
USGS2023

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↗
USGS2022

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.

Oregon↗
USGS2022

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↗
USGS2022

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↗
USGS2022

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↗
USGS2022

A fault‐based crustal deformation model with deep driven dislocation sources for the 2023 update to the U.S. National Seismic Hazard Model

A fault‐based crustal deformation model with deep driven dislocation sources is applied to estimate long‐term on‐fault slip rates and off‐fault moment rate distribution in the western United States (WUS) for the 2023 update to the National Seismic Hazard Model (NSHM). This model uses the method of Zeng and Shen (2017) to invert for slip rate and strain‐rate parameters based on inputs from Global Positioning System (GPS) velocities and geologic slip‐rate constraints. The model connects adjacent major fault segments in California and the Cascadia subduction zone to form blocks that extend to the boundaries of the study area. Faults within the blocks are obtained from the NSHM geologic fault section database. The model slip rates are determined using a least‐squares inversion with a normalized chi‐square of 6.6. I also apply a time‐dependent correction called “ghost transient” effect to account for the viscoelastic responses from large historic earthquakes along the San Andreas fault and Cascadia subduction zone. Major discrepancies between model slip rates and geologic slip rates along the San Andreas fault, for example, from the Cholame to the Mojave and San Bernardino segments of the San Andreas, are well reduced after the ghost transient correction is applied to GPS velocities. The off‐fault moment rate distribution is consistent with regional tectonics and seismicity patterns with a total rate of 1.6 × 10 19    N · m / yr "> 1.6 × 10 19 N ⋅ m / yr for the WUS.

western United States↗
USGS2022

A global catalog of calibrated earthquake locations

We produced a globally distributed catalog of earthquakes and nuclear explosions with calibrated hypocenters, referred to as the Global Catalog of Calibrated Earthquake Locations (GCCEL). This dataset currently contains 18,782 events in 289 clusters with >3.2 million arrival times observed at 19,258 stations. The term “calibrated” refers to the property that the hypocenters are minimally biased by unknown Earth structure. In addition, we calculate uncertainties using empirically determined variability of the arrival‐time data itself, specific to each calibrated cluster of hypocenters. Outliers in the arrival‐time dataset are removed based on measured variability of the data. In each cluster, we estimate the empirically determined uncertainty for each set of station‐phase arrival times. We use a version of the hypocentroidal decomposition multiple event relocation algorithm specifically adapted for calibrated relocations of clusters of seismic events. Most clusters are calibrated by fitting the subset of direct crustal first arrivals ( Pg and Sg ) with a locally appropriate travel‐time model to estimate the cluster hypocentroid. A few clusters are calibrated by aligning the pattern of relative locations in space and time with one or more events for which a ground‐truth hypocenter is available from an independent source with known uncertainty, such as a nuclear explosion. Epicentral uncertainties in GCCEL typically range from 1 to 5 km with a 90% confidence interval. Most events have depth constraint from one or more sources, usually with an uncertainty of ≤5 km. GCCEL is a significant resource for research at local, regional, and global scales because it provides minimally biased absolute hypocenters, meaningful associated error estimates, and curated arrival times as a reference dataset that can be used as prior constraints in the development of new regional, national, and global earthquake catalogs; validation of new location techniques; and the generation of advanced Earth models.

Seismological Research Letters↗
USGS2023

Earthquake scenarios for Quito, Ecuador; Cali, Colombia; and Santiago De Los Caballeros, Dominican Republic

Earthquake risk associated with Quito, Ecuador; Cali, Colombia; and Santiago de los Caballeros, Dominican Republic is examined by generating a set of hypothetical earthquake scenarios considering seismic sources, recent seismicity, and major historical earthquakes recorded in the vicinity. In this study, particular focus is given to the development of earthquake scenarios for use in emergency planning in each city as well as stimulating discussion with respect to addressing the gaps in current understanding of built stock inventory and their vulnerability when subjected to strong shaking. Exposure and vulnerability models from the Global Earthquake Model foundation, hazard and local site information available for the cities, and the U.S. Geological Survey near‐real‐time products are utilized to estimate potential consequences for postearthquake response planning. Results showed that the historic city centers remain the most susceptible to experiencing severe damage resulting in widespread casualties. Similarly, the scenarios highlight areas susceptible to shaking induced ground failure hazards, which may pose additional challenges when responding to such earthquakes. Moderate earthquakes originating from nearby seismic sources, for example, Quito fault system for Quito or the Septentrional fault zone in the case of Santiago de Los Caballeros, could potentially be of greater consequence in terms of direct economic impact and disruption to the city when compared to very large distant subduction interface earthquakes.

Cali, Santiago De Los Caballeros, Quito↗
USGS2022

High‐precision characterization of seismicity from the 2022 Hunga Tonga‐Hunga Ha'apai volcanic eruption

The earthquake swarm accompanying the January 2022 Hunga Tonga‐Hunga Ha'apai (HTHH) volcanic eruption includes a large number of posteruptive moderate‐magnitude seismic events and presents a unique opportunity to use remote monitoring methods to characterize and compare seismic activity with other historical caldera‐forming eruptions. We compute improved epicentroid locations, magnitudes, and regional moment tensors of seismic events from this earthquake swarm using regional to teleseismic surface‐wave cross correlation and waveform modeling. Precise relative locations of 91 seismic events derived from 59,047 intermediate‐period Rayleigh‐ and Love‐wave cross‐correlation measurements collapse into a small area surrounding the volcano and exhibit a southeastern time‐dependent migration. Regional moment tensors and observed waveforms indicate that these events have a similar mechanism and exhibit a strong positive compensated linear vector dipole component. Precise relative magnitudes agree with regional moment tensor moment magnitude ( ⁠ M w "> M w ⁠ ) estimates while also showing that event sizes and frequency increase during the days after the eruption followed by a period of several weeks of less frequent seismicity of a similar size. The combined information from visual observation and early geologic models indicate that the observed seismicity may be the result of a complex series of events that occurred after the explosive eruption on 15 January, possibly involving rapid resupply of the magma chamber shortly after the eruption and additional faulting and instability in the following weeks. In addition, we identify and characterize an M w "> M w 4.5 event five days before the paroxysmal explosion on 15 January, indicating that additional seismic events preceding the main eruption could have been identified with improved local monitoring. Our analysis of the HTHH eruption sequence demonstrates the value of potentially utilizing teleseismic surface‐wave cross correlation and waveform modeling methods to assist in the detailed analysis of remote volcanic eruption sequences.

Futuna↗
USGS2022

Earth’s upper crust seismically excited by infrasound from the 2022 Hunga Tonga–Hunga Ha’apai eruption, Tonga

Records of pressure variations on seismographs were historically considered unwanted noise; however, increased deployments of collocated seismic and acoustic instrumentation have driven recent efforts to use this effect induced by both wind and anthropogenic explosions to invert for near‐surface Earth structure. These studies have been limited to shallow structure because the pressure signals have relatively short wavelengths (<∼300 m). However, the 2022 eruption of Hunga Tonga–Hunga Ha’apai (also called “Hunga”) volcano in Tonga generated rare, globally observed, high‐amplitude infrasound signals with acoustic wavelengths of tens of kilometers. In this study, we examine the acoustic‐to‐seismic coupling generated by the Hunga eruption across 82 Global Seismographic Network (GSN) stations and show that ground motion amplitudes are related to upper (0 to ∼5 km) crust material properties. We find high (>0.8) correlations between pressure and vertical component ground motion at 83% of the stations, but only 30% of stations show this on the radial component, likely due to complex tilt effects. We use average elastic properties in the upper 5.2 km from the CRUST1.0 model to estimate vertical seismic/acoustic coupling coefficients ( ⁠ S V / A ">

Seismological Research Letters↗
USGS2023

Earthquake detection with tinyML

Earthquake detection is the critical first step in earthquake early warning (EEW) systems. For robust EEW systems, detection accuracy, detection latency, and sensor density are critical to providing real‐time earthquake alerts. Traditional EEW systems use fixed sensor networks or, more recently, networks of mobile phones equipped with microelectromechanical systems (MEMS) accelerometers. Internet of things edge devices, with built‐in tiny machine learning (tinyML) capable microcontrollers, and always‐on, internet‐connected, stationary MEMS accelerometers provide the opportunity to deploy ML‐based earthquake detection and warning using a single‐station approach at a global scale. Here, I test and evaluate tinyML deep learning algorithms for earthquake detection on a microcontroller. I show that the tinyML earthquake detection models can generalize to earthquakes outside the training set.

Seismological Research Letters↗
USGS2023

ShakeMap Atlas 4.0 and AtlasCat: An archive of the recent and the historical earthquake ShakeMaps, and impacts for global hazard analyses and loss model calibration

Archival earthquake studies often focus on event and source characteristics for use in earthquake catalogs, seismotectonic understanding, and ground‐motion studies—many of these targeting better constraints for probabilistic seismic‐hazard analyses. The ShakeMap Atlas, in contrast, focuses on spatial distribution of shaking for the historical events, providing the best constraints at all locations that experienced significant shaking for each event, facilitating analyses of human experience, damage, and induced hazards (ground failure). The aim of the Atlas is to gain a general understanding and depiction of the shaking distribution for a suite of canonical earthquakes, and, coupled with loss data for each event, to provide a basis for earthquake loss model calibration, among other uses. Although the initial motivation for developing the ShakeMap Atlas was calibrating the U.S. Geological Survey (USGS) Prompt Assessment of Global Earthquakes for Response system, over time, the Atlas has proved to be a useful tool for its users, and, as such, its scope has been vastly expanded in this newest version. The fourth version of the USGS ShakeMap Atlas is an openly available compilation of over 14,000 ShakeMaps of significant global earthquakes between 1900 and 2020. This revision includes: (1) the latest version of the ShakeMap software that provides refined uncertainty estimations and improved methods to combine macroseismic observations with updated ground‐motion models; (2) an updated earthquake source catalog; (3) a refined strategy to select suites of prediction and conversion equations based on a new seismotectonic regionalization scheme; and (4) expanded macroseismic intensity and ground‐motion datasets. We also tabulate reported economic losses and fatalities for Atlas events where such data are openly available. These changes make the new ShakeMap Atlas a self‐consistent, calibrated catalog invaluable for investigating near‐source ground motions, as well as seismic hazard, scenario, risk, and loss‐model development and testing.

California↗
USGS2023

Ground‐motion variability from kinematic rupture models and the implications for nonergodic probabilistic seismic hazard analysis

The variability of earthquake ground motions has a strong control on probabilistic seismic hazard analysis (PSHA), particularly for the low frequencies of exceedance used for critical facilities. We use a crossed mixed‐effects model to partition the variance components from simulated ground motions of M w "> M w 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone. Total variability of simulated ground motions is approximately equivalent to empirical models. The high contribution from rupture speed suggests an avenue to reducing variability through research on the causes and predictions of rupture speed on specific faults. Simulations show a strong spatial heterogeneity in the variability that manifests from directivity effects. We illustrate the impact of this spatial heterogeneity on hazard using a partially nonergodic PSHA framework. The results highlight the benefit of accounting for directivity effects in nonergodic PSHA, in which models that account for additional processes controlling ground motions are paired with reductions in the modeled ground‐motion variability.

Salt Lake City segment of the Wasatch fault zone↗
USGS2023

Inconsistent citation of the Global Seismographic Network in scientific publications

The highly used Global Seismographic Network (GSN) is a pillar of the seismological research community and contributes to numerous groundbreaking publications. Despite its wide recognition, this survey found that the GSN is not consistently acknowledged in scientific literature and is underrepresented by roughly a factor of 3 in citation searches. Publication tracking is a key metric that factors into operational decisions and funding support for the network; thus, consistent and proper citation of the GSN is important. This study not only serves as a reminder for researchers using GSN observations to cite the network’s digital object identifiers (DOIs) but also promotes a community‐wide conversation among researchers, journal editors, network operators, and other stakeholders regarding more standardized policies and review processes to ensure seismic networks are properly and consistently recognized for their contributions to research.

Seismological Research Letters↗
USGS2023

MLAAPDE: A machine learning dataset for determining global earthquake source parameters

The Machine Learning Asset Aggregation of the Preliminary Determination of Epicenters (MLAAPDE) dataset is a labeled waveform archive designed to enable rapid development of machine learning (ML) models used in seismic monitoring operations. MLAAPDE consists of more than 5.1 million recordings of 120 s long three‐component broadband waveform data (raw counts) for P , Pn , Pg , S , Sn , and Sg arrivals. The labeled catalog is collected from the U.S. Geological Survey National Earthquake Information Center’s (NEIC) Preliminary Determination of Epicenters bulletin, which includes local to teleseismic observations for earthquakes ∼ M 2.5 and larger. Each arrival in the labeled dataset has been manually reviewed by NEIC staff. An accompanying Python module enables users to develop customized training datasets, which includes different time‐series lengths, distance ranges, sampling rates, and/or phase lists. MLAAPDE is distinct from other publicly available datasets in containing local (14%), regional (36%), and teleseismic (50%) observations, in which local, regional, and teleseismic distance are 0°–3°, 3°–30°, and 30°+, respectively. A recent version of the dataset is publicly available (see Data and Resources ), and user‐specific versions can be generated locally with the accompanying software. MLAAPDE is an NEIC supported, curated, and periodically updated dataset that can contribute to seismological ML research and development.

Seismological Research Letters↗