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At least 235 records · Page 13Linked to original sources
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

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↗
USGS2023

Uncertainties in intensity-based earthquake magnitude estimates

Estimating the magnitude of historical earthquakes is crucial for assessing seismic hazard. Magnitudes of early‐instrumental earthquakes can be inferred using a combination of instrumental records, field observations, and the observed distribution of shaking intensity determined from macroseismic observations. For earthquakes before 1900, shaking intensity distributions often provide the only information to constrain earthquake magnitude. Considerable effort has been made to develop methods to estimate the magnitude of moderate‐to‐large historical earthquakes using shaking intensities derived from macroseismic data. In this study, we consider earthquakes in California with known instrumental magnitudes to explore uncertainties in estimating the magnitude of historical earthquakes from intensity information alone. We use three California‐specific intensity prediction equations (IPEs) and an IPE based on a global ground‐motion model (GMM) to determine optimum intensity‐based magnitudes for 33 moderate‐to‐large California earthquakes between 1979 and 2021. Intensity‐based magnitudes are close to instrumental magnitudes on average. However, intensity‐based magnitudes for individual events differ by as much as 2.2 magnitude units from instrumental magnitudes. This result reflects the weak dependence of ground motions and shaking intensities on moment magnitude and their strong dependence on stress drop. Considering the intensity distributions of the 1906 San Francisco and 1989 Loma Prieta earthquakes, we show that information that could constrain rupture length is discarded when considering only the 2D decay of intensity with distance. We also show that ground‐motion intensity conversion equations used in a GMM‐based approach may cause a systematic overestimation of large historical earthquake magnitudes. This study underscores both the reducible and potentially irreducible uncertainties associated with using intensity data to estimate magnitudes of historical earthquakes using IPEs and highlights the value of using additional information to constrain rupture dimensions. Using intensity observations alone, moment magnitude uncertainties are typically on the order of a full unit.

California↗
USGS2023

Shallow fault slip of the 2020 M5.1 Sparta, North Carolina, earthquake

The 2020 M 5.1 Sparta, North Carolina, earthquake is the largest in the eastern United States since the 2011 M 5.8 Mineral, Virginia, earthquake and produced a ∼2.5‐km‐long surface rupture, unusual for an event of this magnitude. A geological field study conducted soon after the event indicates oblique slip along a east‐southeast‐trending fault with a consistently observed thrust component. My analysis of regional seismic waveforms, Interferometric Synthetic Aperture Radar, and Global Positioning System survey data yields a compact shallow rupture extending from Earth’s surface down‐dip to the southwest over a ∼3 km fault length. The inferred kinematic rupture is primarily toward the up‐dip and eastward along‐strike directions and has predominantly thrust motion in the west, transitioning to roughly equal thrust and left‐lateral strike‐slip motion in the east. No normal faulting component, as proposed in an earlier geophysical study, is necessary to explain the data. The prevalence of only dip‐slip motions observed at Earth’s surface may demand slip partitioning between dip slip and lateral motions at depth.

North Carolina, Virginia, West Virginia↗
USGS2023

Comparison of co-recorded analog and digital systems for characterization of responses and uncertainties

One of the most prominent challenges related to legacy seismic data is determining how these data can be appropriately used in modern research applications. The wide variety of instrumentation used in the analog era, the format of recording on paper wrapped around a helicorder drum, and limited metadata information introduces ambiguities that are not typical of modern digital data. Therefore, techniques must be developed to help characterize uncertainties in legacy data. This article presents an analysis that compares corecorded signals from two instruments—a Trillium Compact or Press‐Ewing (PE) seismometer for sensing ground motion and two recording systems: a modern Q330 digitizer or heated‐stylus system. Analyses of the recordings in both time and frequency domains indicate time uncertainty on the order of one second, identify a flat response in a 10–60 s band for the PE and drum recorder, and highlight how specific features of scans and paper seismograms (e.g., repeated portions of scans and line thickness) can cause timing jumps or reduced trace amplitude.

Seismological Research Letters↗
USGS2023

Global seismic networks operated by the U.S. Geological Survey

The U.S. Geological Survey (USGS) Global Seismographic Network (GSN) Program operates two thirds of the GSN, a network of state‐of‐the‐art, digital seismological and geophysical sensors with digital telecommunications. This network serves as a multiuse scientific facility and a valuable resource for research, education, and monitoring. The other one third of the GSN is funded by the National Science Foundation (NSF), and the operations of this component are overseen by EarthScope. This collaboration between the USGS, EarthScope, and NSF has allowed for the development and operations of the GSN to be a truly multiuse network that provides near real‐time open access data, facilitating fundamental discoveries by the Earth science community, supporting the earthquake hazards mission of the USGS, benefitting tsunami monitoring by the National Oceanic and Atmospheric Administration, and contributing to nuclear test monitoring and treaty verification. In this article, we describe the installation and evolution of the seismic networks operated by the USGS that ultimately led to the USGS portion of the GSN (100 stations under network codes IU, IC, and CU) as they are today and envision technological advances and opportunities to further improve the utility of the network in the future. This article focuses on the USGS‐operated component of the GSN; a companion article on the GSN stations funded by the NSF and operated by the Cecil and Ida Green Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California at San Diego by Davis et al. (2023) appears in this volume.

Seismological Research Letters↗
USGS2024

Background seismic noise levels among the Caribbean network and the role of station proximity to coastline

The amplitude and frequency content of background seismic noise is highly variable with geographic location. Understanding the characteristics and behavior of background seismic noise as a function of location can inform approaches to improve network performance and in turn increase earthquake detection capabilities. Here, we calculate power spectral density estimates in one‐hour windows for over 15 yr of vertical‐component data from the nine‐station Caribbean network (CU) and look at background noise within the 0.05–300 s period range. We describe the most visually apparent features observed at the CU stations. One of the most prominent features occurs in the 0.75–3 s band for which power levels are systematically elevated and decay as a function of proximity to the coastline. Further examination of this band on 1679 contiguous USArray Transportable Array stations reveals the same relationship. Such a relationship with coastal distance is not observed in the 4–8 s range more typical of globally observed secondary microseisms. A simple surface‐wave amplitude decay model fits the observed decay well with geometric spreading as the most important factor for stations near the coast (<∼50 km). The model indicates that power levels are strongly influenced by proximity to coastline at 0.75–3 s. This may be because power from nearshore wave action at 0.75–3 s overwhelms more distant and spatially distributed secondary microseism generation. Application of this basic model indicates that a power reduction of ∼25 dB can be achieved by simply installing the seismometer 25 km away from the coastline. This finding may help to inform future site locations and array design thereby improving network performance and data quality, and subsequently earthquake detection capabilities.

Seismological Research Letters↗
USGS2024

Geologic input databases for the 2025 Puerto Rico – U.S. Virgin Islands National Seismic Hazard Model update: Crustal faults component

The last National Seismic Hazard Model (NSHM) for Puerto Rico and the U.S. Virgin Islands (PRVI) was published in 2003. In advance of the 2025 PRVI NSHM update, we created three geologic input databases to summarize new onshore and offshore fault source information in the northern Caribbean region between 62°–70° W and 16°–21° N. These databases, of fault sections, fault‐zone polygons, and geologic estimates of fault activity (fault‐slip rate and earthquake recurrence intervals) at specific sites, document updates to fault parameters used in prior seismic hazard models in PRVI. Fault sources were reviewed from published studies since 2003, which document substantial changes to the understanding of fault location, geometry, or activity. New fault section sources were added for features that meet the criteria of (1) length ≥7 km, (2) unequivocal evidence of recurrent tectonic Quaternary activity, and (3) documentation that is publicly available in a peer‐reviewed source. In addition, we revised several broad areal sources, such as the Mona and Anegada extensional zones. The 2003 model included three fault sections and two fault‐zone polygons (areal sources). These databases include 35 fault sections, 6 fault‐zone polygons, and 51 earthquake geology sites. To characterize fault activity rates, slip‐rate bins were assigned based on landscape expression and paleoseismic trench observations for faults without published slip‐rate sites. Additional fault sources were evaluated but not included in these databases due to a lack of published information about fault location, geometry, or recurrent Quaternary activity. The PRVI NSHM 2025 geologic input databases describe crustal faulting; the geometries and coupling of Puerto Rico subduction zone and Muertos Trough models are considered in a separate database. Updates to the fault sections, fault‐zone polygons, and earthquake geology databases can help inform the location and recurrence rate of damaging earthquakes in the PRVI NSHM implementation.

Puerto Rico, U.S. Virgin Islands↗
USGS2024

Evaluation of an open earthquake early warning system in Mexico, and laboratory tests of their sensors

Microelectromechanical system (MEMS) accelerometers are useful for seismological and engineering applications because of their ability to record unsaturated large seismic signals. Recent advances in MEMS technologies enable the design of instruments with improved capabilities that also allow the recording of small signals. As a result, MEMS can be useful across a broad dynamic range and for both major earthquakes and smaller magnitude events. Here, we analyze the performance of a MEMS‐based accelerometer with the capability of real‐time data transmission. We discuss the self‐noise level, dynamic range, and sensitivity, along with the timing precision and data transmission latency, and compare these parameters to other MEMS‐based instruments. We also summarize observations from a regional network deployed in southern Mexico over a period of 3+ yr for the purpose of earthquake early warning. In addition to the sensor evaluation, we present a large, openly available data set of strong motion data from the Mexican network that comprises continuous ground‐motion records from 24 accelerometers in the period between 2017 and 2022.

Seismological Research Letters↗
USGS2024

Earthquake rupture forecast model construction for the 2023 U.S. 50‐State National Seismic Hazard Model Update: Central and eastern U.S. fault‐based source model

As part of the U.S. Geological Survey’s 2023 50‐State National Seismic Hazard Model (NSHM), we make modest revisions and additions to the central and eastern U.S. (CEUS) fault‐based seismic source model that result in locally substantial hazard changes. The CEUS fault‐based source model was last updated as part of the 2014 NSHM and considered new information from the Seismic Source Characterization for Nuclear Facilities (CEUS‐SSCn) Project. Since then, new geologic investigations have led to revised fault and fault‐zone inputs, and the release of databases of fault‐based sources in the CEUS. We have reviewed these databases and made minor revisions to six of the current fault‐based sources in the NSHM, as well as added five new fault‐based sources. Implementation of these sources follows the current NSHM methodology for CEUS fault‐based sources, as well as the incorporation of a new magnitude–area relationship and updated maximum magnitude and recurrence rate estimates following the methods used by the CEUS‐SSCn Project. Seismic hazard sensitivity calculations show some substantial local changes in hazard (−0.4 g to 1.1 g ) due to some of these revisions and additions, especially from the addition of the central Virginia, Joiner ridge, and Saline River sources and revisions made to the Meers and New Madrid sources.

Seismological Research Letters↗