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USGS2024

Precariously balanced rocks in northern New York and Vermont, U.S.A.: Ground-motion constraints and implications for fault sources

Precariously balanced rocks (PBRs) and other fragile geologic features have the potential to constrain the maximum intensity of earthquake ground shaking over millennia. Such constraints may be particularly useful in the eastern United States (U.S.), where few earthquake‐source faults are reliably identified, and moderate earthquakes can be felt at great distances due to low seismic attenuation. We describe five PBRs in northern New York and Vermont—a region of elevated seismic hazard associated with historical seismicity. These boulders appear to be among the most fragile PBRs in the region, based on reports from hobbyists. The PBRs are glacial erratics, best evidenced by glacial striations on bedrock pedestals. The pedestals themselves are locally high knobs, often situated on regionally high topography; this setting limits soil development and indicates that any outwash deposits were likely ephemeral. As a result, PBR ages can be reliably established by the retreat of the last continental ice sheet, ∼15–13 ka. To quantify the fragility of the PBRs, we surveyed them with ground‐based light detection and ranging and calculated geometric parameters from the point clouds, field observations, and seismic responses. Preliminary validation of the 2023 time‐independent U.S. National Seismic Hazard Model (NSHM) shows that the existence of PBRs is generally consistent with the median site‐specific hazard curves. Only the Blue Ridge Road site suggests a modest reduction in hazard. To visualize the ensemble of data, we mapped the minimum permissible distance to potential source faults around each PBR site as a function of source magnitude by using the ground‐motion models from the 2023 NSHM. Viewed in this manner, our data are consistent with potential M ∼6.5 earthquake‐source faults in many parts of the Lake Champlain Valley and northern Adirondack Mountains. Our work illustrates a potential pathway for better constraining earthquake‐source faults in regions of cryptic faults.

Bulletin of the Seismological Society of America↗

USGS2026

The potential impact of three-dimensional distributed slip models derived from real-time GNSS data on the performance of the ShakeAlert earthquake early warning system for slab interface earthquakes

The ShakeAlert® earthquake early warning (EEW) system is designed to warn users of imminent strong ground motion with sufficient time to take protective actions. ShakeAlert currently uses three algorithms to characterize the earthquake source. One estimates the location and magnitude using the first few seconds of the P wave and, while fast, tends to underestimate magnitude for M w 7.0+ earthquakes. A second estimates the location, orientation, length, and corresponding magnitude of a line source using observed peak ground acceleration and contributes primarily to M w 5.5+ earthquakes. The third infers earthquake magnitude from peak ground displacement measured using Global Navigation Satellite System (GNSS) data and offers nonsaturating magnitudes for M w 7.0+ earthquakes. Other EEW algorithms exist that infer temporally evolving spatially variable slip on a 3D fault surface from real‐time GNSS data, information that might enable more accurate and timely alerts in the event of large‐magnitude subduction interface earthquakes. Here, we evaluate the potential contribution of one such algorithm, BEFORES ( Minson et al. , 2014 ), to improve ShakeAlert performance through a simulated real‐time implementation of Bayesian evidence‐based fault orientation and real‐time earthquake slip (BEFORES) and other ShakeAlert algorithms using data for eight M w 7.6+ earthquakes. The test results demonstrate that BEFORES can produce well‐constrained and accurate magnitude estimates as soon as or sooner than other EEW algorithms, in turn enabling it to increase the amount of warning time users receive in many cases. However, with a modified Mercalli intensity (MMI) threshold of 3.5, which is commonly used for issuing alerts, BEFORES would tend to alert large geographic regions that did not feel strong shaking (MMI 6+). This effect can be mitigated using a higher alert threshold of MMI 4.5 without negative impact on the amount of warning time obtainable with BEFORES.

Bulletin of the Seismological Society of America↗

USGS2026

Detecting earthquakes in noisy real-time GNSS data with deep learning for improved PGD magnitude estimation

To disseminate accurate and useful warnings, earthquake early warning (EEW) systems must quickly determine the size and location of an earthquake to estimate expected shaking. Traditional seismic‐based algorithms tend to underestimate the true magnitudes of large earthquakes, a phenomenon known as magnitude saturation. This limitation motivated the recent inclusion of Global Navigation Satellite Systems (GNSS) data into the U.S. Geological Survey’s ShakeAlert EEW system with the Geodetic First Approximation of Size and Time (GFAST) algorithm because GNSS data do not saturate with large ground motions. However, the noise levels of GNSS data are very high compared with traditional seismic data, which obscures P ‐wave arrivals and can result in less accurate magnitude estimations if displacement amplitudes are low, such as for lower magnitude earthquakes or large source–station distances. In this study, we develop a deep‐learning model that detects earthquakes in GNSS data and use the Ridgecrest, California, earthquake sequence as a case study to demonstrate how the model could act as a filter to reduce the amount of low‐quality data that enters an algorithm like GFAST. To preserve our limited real earthquake data for model inference, we generated a training dataset composed of >700,000 synthetic displacement waveforms. We combined the synthetic waveforms with real‐time GNSS noise to produce realistically noisy training waveforms and then tested our model on additional synthetic data and performed inference using the real data that were held back. We discuss the performance of our trained model on both the unseen synthetic data and real inference data. Our model can be used to selectively filter only high‐quality data where an earthquake signal is observed for input into an algorithm like GFAST (outperforming a simple signal‐to‐noise ratio–based filter) to reduce the error in GFAST’s real‐time earthquake magnitude estimations.

California↗

USGS2024

Prediction of regional broadband strong ground motions using a teleseismic source model of the 18 April 2014 Mw 7.3 Papanoa, Mexico, earthquake

To estimate predicted ground motion from a teleseismic slip model, we use a low‐ and high‐frequency hybrid method to simulate the regional, strong ground motions observed following the 18 April 2014 moment magnitude ( ⁠M w ⁠ ) 7.3 Papanoa, Mexico, earthquake. To generate the regional ground motion at low frequencies (<1 Hz), a teleseismically derived, finite‐fault, kinematic model is used to define the earthquake source, taking into account slip‐model variations identified with a parameter sampling approach that considers possible errors in the fault geometry, the hypocenter depth, and the rupture velocity. A 3D crustal model is used to calculate the low‐frequency ground motions using a finite‐element calculation that includes topography and considers variations in the source model to estimate the uncertainty in the calculations. High frequencies (>1 Hz) are added using a 1D full‐wave propagation code that estimates uncertainties by considering multiple random distributions of slip with different spatial correlation lengths. The synthetic, broadband (0.05–10.0 Hz) ground motions are obtained by combining the low‐ and high‐frequency portions match filtered at 1 Hz. These synthetic ground motions are compared with the regional observations using velocity records, peak ground acceleration, and medians of the orientation‐independent response spectra of the horizontal components (RotD50) calculated at periods of 0.2, 0.3, 0.5, 1.0, 2.0, 3.0, 5.0, 7.5, and 10.0 s. The results indicate that ground motions estimated at these periods using our hybrid approach based primarily on a teleseismically derived source model are comparable to the values observed for the 2014 Papanoa earthquake at regional distances. The approach could be used to estimate strong‐motion spectral levels expected for regions with limited local and regional recordings and could also fill in magnitude or distance gaps in ground‐motion prediction relations utilized in the assessment of seismic hazard.

Papanoa↗

USGS2025

An empirical Green’s function approach for isolating directivity effects in earthquake ground-motion amplitudes

In this study, we apply an empirical Green’s function (eGf) method within a ground‐motion modeling framework to mitigate trade‐offs between source, path, and site effects. Many physical processes contribute to spatial variations in observed ground motions, including earthquake radiation pattern, directivity, variable path attenuation, and site effects. Current nonergodic ground‐motion models use spatially varying coefficients for path and site effects, but they do not address trade‐offs with complex earthquake source effects. To quantify the influence of directivity on ground‐motion amplitudes, we use records from multiple smaller earthquakes with epicenters near that of a larger event. We use these small magnitude events as eGfs and estimate repeatable path and site effects at individual stations, assuming that the average adjustments are not controlled by directivity. We adjust residuals from the larger earthquake using the eGf terms, isolating effects related to the rupture. This method clearly enhances the observed broadband directivity observed in the 2022 M 5.1 and 2007 M 5.4 Alum Rock earthquake ground motions, reinforcing the conclusion that their ruptures were unilateral. For the 2004 M 6.0 Parkfield earthquake, we find a bilateral rupture model better fits the data because variations in rupture velocity, slip rate, and slip distribution seem to have a stronger effect on the ground motions than rupture direction alone. Applying eGf adjustments reduces the standard deviation of the rupture models over the three earthquakes by 32% on average and by up to 57% for the 2022 Alum Rock earthquake, confirming we have effectively removed repeatable effects related to the wave propagation path and site response. We propose a novel measure of the frequency‐dependent directivity amplification strength as the reduction in ground‐motion residual variability gained by fitting a directivity model; for the three earthquakes considered, this parameter varies between 25% and 75%, indicating that directivity can strongly influence ground motions and should be considered in ground‐motion modeling.

California↗

USGS2025

Magnitude, depth and methodological variations of spectral stress drop within the SCEC/USGS Community Stress Drop Validation Study using the 2019 Ridgecrest Earthquake Sequence

We present the first ensemble analysis of the 56 different sets of results submitted to the ongoing Community Stress Drop Validation Study using the 2019 Ridgecrest, California, earthquake sequence. Different assumptions and methods result in different estimation of the source contribution to recorded seismograms, and hence to the source parameters (principally corner frequency, f c ⁠ , spectral stress drop, Δσ, and seismic moment, M 0 ⁠ ) obtained from modeling calculated source spectra. For earthquakes smaller than magnitude (M) 2.5 there is negligible correlation between the f c values obtained by different studies, implying that no present method is reliable using available data. For larger magnitude events, correlation between f c measurements of different studies, within even a small M range is always higher than spectral ⁠Δσ , because the f c measurements simply reflect the underlying physical decrease in f c with increasing M. We model the observed trends of submitted f c with both magnitude and depth. Most methods report an increase in spectral Δσ with M, although a magnitude‐invariant spectral Δσ is within the confidence limits. The depth dependence is smaller and depends on whether a study allows attenuation to vary with source depth; a combination of depth‐dependent attenuation correction, and depth‐dependent shear‐wave velocity can compensate for reported depth trends. We model the submitted values to remove differing M and depth variation to investigate the relative interevent variability. We find consistent relative variation between individual events, and also lower relative spectral Δσ in the northwest of the aftershock sequence, and higher on the cross fault and in the region of main fault intersection. This large‐scale comparison implies that absolute spectral Δσ estimates are dependent on the methods used; studies of different regions or using different methods should not be directly compared and improved constraints on path and site corrections are needed to resolve these absolute spectral Δσ differences.

California↗

USGS2026

ShakeAlert Earthquake Early Warning System performance during the Mw 7.0 offshore Cape Mendocino earthquake

The 5 December 2024 M w 7.0 Offshore Cape Mendocino earthquake was a challenging test of the U.S. West Coast ShakeAlert earthquake early warning system due to its offshore epicenter and limited near‐source station coverage. We analyzed real‐time performance of all components of the ShakeAlert system, including the seismic algorithms (earthquake point‐source integrated code [EPIC] and Finite‐fault rupture Detector [FinDer]), the geodetic algorithm (Geodetic First Approximation of Size and Time–peak ground displacement [GFAST‐PGD]), and network telemetry during the event. EPIC created the first solution for this earthquake 15 s after origin time with an initial magnitude estimate of M 5.6 and location error of 10 km from the Advanced National Seismic System epicenter. An early spurious trigger from station CE.89101 fortuitously maintained location accuracy and, correspondingly, magnitude accuracy. FinDer contributed its first solution at 18 s with a location estimate closer to the seismic network and produced two distinct rupture geometries, leading to minor fluctuations in estimated intensity contours. GFAST‐PGD did not meet alerting thresholds but otherwise performed as expected. Network latencies were <2 s for most stations, supporting the rapid detection of this earthquake by the system. Roughly five million alerts were delivered to cell phone devices in California and Oregon during this event. This was also the first instance of a school district‐wide ShakeAlert‐powered system being activated. Comparisons to recorded seismograms demonstrate that the maximum warning times before potentially damaging shaking (intensity 6+) were in the range of 5–55 s. Although the ShakeAlert system provided accurate solutions and useful alert delivery, this earthquake raised awareness of potential issues within the system, including the need for improved offshore location estimates, a combination of solutions from ShakeAlert servers, and handling of spurious triggers.

California↗

USGS2025

Capturing the uncertainty of seismicity observations in earthquake rate estimates: Implications for probabilistic seismic hazard analysis and the USGS National Seismic Hazard Model

The rate of earthquakes in a region is a fundamental input to Probabilistic Seismic Hazard Analysis. We present a Monte Carlo method for computing that rate from seismicity catalogs while including a range of data and analysis uncertainties. This method is applied to regions for which the b value is assumed to be spatially invariant. Each region is broken down into epochs for which each epoch is estimated to have a uniform magnitude of completeness ( ⁠⁠ M c ). The distribution of earthquake rates for M ≥ M c is determined for each epoch by considering the Poisson likelihood of rates given the number of observed earthquakes with M ≥ M c ⁠ . We use a Monte Carlo process to include the uncertainty in b , ⁠ M c , and individual event magnitudes. The result for each epoch is the joint distribution of the Poisson rate of earthquakes with magnitudes larger than the minimum value used to calculate hazard ( ⁠⁠ M 1 ) and the Gutenberg–Richter b values, which control the extrapolation to other magnitudes. The rate for each region is either the duration‐weighted average over the epochs or, to better capture temporal variations, we also consider mixture models. The mixture models also provide an avenue to allow temporal variations in b values. To implement this joint distribution in a logic tree, we use the mean and 95% confidence branches, each of which is parameterized with an M ≥ M 1 rate and b value. We explore different ways of defining those branches, as well as non‐Gutenberg–Richter branches, and their impact on hazard estimates. The mean hazard, but not the fractiles, is robust with respect to these choices. To illustrate these new methods, we use synthetic data and catalogs from recent U.S. Geological Survey National Seismic Hazard Models for the Central and Eastern United States and for Puerto Rico and the U.S. Virgin Islands.

central and eastern United States, Puerto Rico, U.↗

USGS2024

SCEC/USGS Community Stress Drop Validation Study: How spectral fitting approaches influence measured source parameters

Spectral source parameters used to estimate an earthquake’s stress drop (Δσ) can vary significantly across measurement approaches. The Statewide California Earthquake Center/U.S. Geological Survey Community Stress-Drop Validation Study was initiated to compare source parameter estimates, focusing initially on a dataset from the 2019 Ridgecrest earthquake sequence. As part of that validation effort, here we focus on one potential source of uncertainty: whether spectral fitting approaches alone, applied to a common set of spectra from the 2019 Ridgecrest sequence result in different source parameter estimates. By using a common set of benchmark spectra analyzed across a consistent frequency band of 1–40 Hz, we eliminate many sources of variability. A subgroup of validation study participants volunteered to estimate the low-frequency displacement (Ω0) and corner frequency ( f c ) by fitting a smooth function to benchmark displacement spectra. Participants used linear- or log-sampled spectra, assumed a Brune or Boatwright spectral model, and applied different misfit criteria. We compare 17 approaches used to estimate Ω0, f c , and Δσ for 54 earthquake spectra. Our results reveal that 35% of events have Δσ estimates within a factor of two, whereas others exhibit variations exceeding an order of magnitude. The variability in Ω0 and f c can largely be attributed to whether a spectrum is consistent with the smooth function of an idealized simple crack model. The trade-off between Ω0 and f c may be more pronounced when using linearly sampled spectra, as higher frequency spectral bumps control the fits. As expected, methods that assumed a Boatwright model tended to have lower Ω0 and somewhat higher f c compared to those assuming a Brune model, although resulting Δσ estimates are similar. When compared to the overall validation study results, the fitting approach alone may account for between 5% and 90% (25% on average) of the total variability in spectral Δσ.

California↗

USGS2024

Limited evidence of late Quaternary tectonic surface deformation in the eastern Tennessee seismic zone, USA

The ~300-km-long eastern Tennessee seismic zone (ETSZ), USA, is the second-most seismically active region east of the Rocky Mountains. Seismicity generally occurs below the Paleozoic fold-and-thrust belt within the Mesoproterozoic basement, at depths of 5–26 km, and earthquake magnitudes during the instrumental record have been moment magnitude ( M w )≤4.8. Evidence of surface deformation may not exist or be difficult to detect because of the vegetated and soil-mantled landscape, landslides, locally steep topography, anthropogenic landscape modification, or long, irregular recurrence intervals between surface-rupturing earthquakes. Despite the deep seismicity, analog models indicate that accumulation of strike-slip or oblique-slip displacement at depth could be expected to propagate upward through the Paleozoic section, producing a detectable surficial signal of distributed faulting. To identify potential surface deformation, we interrogated the landscape at different spatial scales. We evaluated morphotectonic and channel metrics, such as channel sinuosity and catchment-scale hypsometry. Additionally, we mapped possible fault-related topographic features on 1-m lidar. Finally, we integrated our observations with available bedrock and Quaternary surficial mapping and subsurface geophysical data. At a regional scale, most morphotectonic and channel metrics have a strong lithologic control. Within smaller regions of similar lithology, we observe changes in landscape metrics like channel sinuosity and catchment-scale hypsometry that spatially correlate with new lineaments identified in this study and previously mapped east–west Cenozoic faults. These faults have apparent left-lateral offsets, are optimally oriented to slip in the current stress field, and match kinematics from recent focal mechanisms, but do not clearly preserve evidence of late Pleistocene or Holocene tectonic surface deformation. Most newly mapped lineaments might be explained by either tectonic or non-tectonic origins, such as fluvial or karst processes. We also re-evaluated a previously described paleoseismic site and interpret that the exposure does not record evidence of late Pleistocene faulting but instead is explained by fluvial stratigraphy.

Tennessee↗

USGS2026

Site-specific amplifications in Northwestern Turkiye: A generic approach

In this study, we derive generic site amplification functions applicable to Northwestern Türkiye and follow guidance on site classifications established by the United States National Earthquake Hazards Reduction Program (NEHRP). We employ the one-dimensional (1-D) equivalent linear ground response analysis method and focus on recordings from a select suite of free-field strong motion (SM) stations. These particular SM stations are associated with a comprehensive geotechnical site conditions dataset comprising 76 shear-wave velocity profiles. Despite their varying resolutions and depths, this diversity allows for a more representative analysis across different site conditions, enhancing the robustness of our findings. For analyses, we utilize a robust set of global ground motion records, selected from both within and outside of Türkiye, to serve as strong and weak input bedrock motions. Site amplification is determined by propagating these bedrock records through 76 site-specific Afet ve Acil Durum Yönetimi Başkanlığı (AFAD) station profiles and calculating the spectral acceleration ratio between the ground surface and the input motion. Our analyses reveal substantial disparities between the amplification functions derived from 1-D site response analyses using strong and weak input motions. These disparities are central to our analysis and inform the subsequent comparison with site amplification functions reported in the literature. We compare the generic site amplification functions derived in this study with those reported in the literature for regions with similar geological and seismic conditions to Northwestern Türkiye. For example, site amplification functions for NEHRP Site Classes C and D have been documented for Greece and the United States utilizing frequency-dependent methods such as the quarter-wavelength approximation. Our findings reveal that the generic site amplification functions derived in this study exhibit differences in peak amplitudes and predominant frequencies compared to those in the literature, reflecting regional variations in geotechnical and seismic properties. This study represents an initial effort to formulate generic site amplification functions for Türkiye, with a particular emphasis on the Northwestern region. A thorough literature review indicated no prior studies have proposed such functions specifically for this area.

Northwestern Turkiye↗

USGS2026

Efficient physics‐informed ground‐motion simulations with reduced‐order models: CyberShake implications and high‐resolution site terms for southern San Andreas fault earthquakes

Recent advances in Probabilistic Seismic Hazard Analysis (PSHA) leverage physics‐based ground‐motion simulations to estimate seismic hazard, such as the CyberShake project. However, computational costs quickly escalate when performing PSHA for numerous faults or sites and can become prohibitively expensive. To reduce computational demands, CyberShake uses reciprocity and interpolates physics‐informed corrections from simulations conducted at fewer locations, but the accuracy of these interpolations remains poorly quantified. To quantify the interpolation accuracy, we derive high‐resolution, frequency‐dependent site terms for southern California and compare them with interpolated site terms using the CyberShake approach. We accomplish this by performing a set of earthquake point‐source simulations distributed along the nonplanar fault geometry for the southern San Andreas fault (SSAF) extending from Bombay Beach to Lake Hughes. Using SeisSol, we simulate three minutes of viscoelastic seismic wave propagation for these sources and store the horizontal‐component Green’s functions for 480,000 sites. We then use a scientific machine learning approach based on interpolated proper orthogonal decomposition to construct an accurate reduced‐order model of the Green’s functions to efficiently predict effective amplitude spectra (EAS) for finite‐source rupture models of SSAF earthquakes. Using minimum curvature interpolation with tension, as used in CyberShake, we compare the interpolated site terms against our high‐resolution site terms. We identify local discrepancies with EAS differing by up to a factor of approximately three. Furthermore, we identify locations where unexpectedly high or low ground motions are missed when using the interpolated dataset for these earthquakes. We estimate that our approach may be used within CyberShake to reduce the time‐to‐solution by a factor of 336 for the entire earthquake rupture forecast. Our analysis of physics‐based site terms provides more insight into the seismic hazard due to SSAF ruptures and guides future developments by combining high‐performance computing and reduced‐order modeling techniques for PSHA.

California↗

USGS2025

Revisiting an enigma on California's north coast: The Mw6.5 Fickle Hill earthquake of 21 December 1954

Many earthquakes occur along the North Coast of California in the vicinity of the Mendocino Triple Junction (MTJ), where the Pacific, Gorda, and North American (NA) plates meet, and on the adjacent plate boundaries. The MTJ marks the nexus of the Mendocino and San Andreas faults with the Cascadia subduction zone (CSZ). Historically, most large earthquakes around the MTJ have been within the offshore Gorda plate and its subducted portion beneath the NA plate. North of the MTJ, active faults mapped in the NA plate are part of the CSZ fold‐and‐thrust belt. Although some events have been detected in the NA plate, no large historic events have been associated with mapped surface faults. The 21 December 1954 M w 6.5 earthquake in Humboldt County is one possible exception. Using published data from catalogs and articles, unpublished data from Berkeley’s archives, and S ‐ P times interpreted from two U.S. Coast and Geodetic Survey (USCGS) accelerometers, we determine a probability cloud for the earthquake’s hypocenter using NonLinLoc. The highest probability location lies beneath Fickle Hill just east of the city of Arcata, California, at 40.87° N, 124.03° W, and ∼11 km depth. Using P ‐wave polarities from Berkeley stations and the digitized waveforms from the accelerometers, we find that the focal mechanism most consistent with the data indicates thrust movement with strike, dip, and rake of 350°, 10°, and 90°, respectively, at a depth of 14 km. Given the depth uncertainties of both this event and the megathrust, this implies that the earthquake most likely took place on the subduction interface rather than on the mapped faults in the Mad River fault zone that trend 322° and dip to the northeast. The revisited intensity in the epicentral region also supports a location beneath Fickle Hill to the east of the city of Arcata, California.

California, Idaho, Nevada, Oregon, Washington↗

USGS2025

Near-surface material and topography generate anomalous high-frequency ground motion amplification in Chugiak, Alaska

An ∼3 km long nodal array oriented approximately east–west was deployed in Chugiak, Alaska, by the U.S. Geological Survey during 2021. The array intersects with the permanent NetQuakes station NP.ARTY, where peak ground acceleration (PGA) value of 1.98 g was recorded during the 2018 M w 7.1 Anchorage, Alaska, earthquake, in sharp contrast to the PGA of ∼0.3 g at a site just 4 km to the west. Seismic data for M w 1.8–4.3 aftershocks from the M w 7.1 event recorded by the nodal array confirm the anomalously large ground motions obtained at NP.ARTY as well as similar amplifications at nodes within ∼1 km to the east. Here, we performed 0–10 Hz 3D finite‐difference simulations, including high‐resolution surface topography, to explore the cause of the unexpectedly large amplification. As expected, the simulations computed with a regional 3D tomography velocity model severely underpredict the 0–10 Hz acceleration records at almost all sites. Adding a near‐surface low‐velocity taper to 300 m depth amplifies the accelerations by up to a factor of 5 and enables a reasonable match between the nodal data and simulations at sites to the west of NP.ARTY. However, this model still underpredicts the spectral energy in the area covered by glacial sediments by up to an order of magnitude. The addition of a till layer using a depth‐dependent shear‐wave velocity ( ⁠⁠ V s ) profile along with a homogeneous, 8 m thick low‐velocity layer with V s = 250 m/s representing the kame terraces improves the fit to data to within a factor of 2 at nodes located on top of the glacial sediments. Our study shows that the anomalously large high‐frequency amplification recorded at and near NP.ARTY can be explained by a combination of topographic effects and near‐surface low‐velocity material with amplification effects on the high‐frequency ground motion by up to about 40% and an order of magnitude, respectively.

Alaska↗

USGS2024

Status and performance of the ShakeAlert® earthquake early warning system: 2019-2023

The U.S. Geological Survey (USGS)‐operated ShakeAlert® system is the United States West Coast earthquake early warning system ( Given et al. , 2018 ). In this study we detail ShakeAlert’s performance during some of the largest events seen by the system thus far. Statewide public alerting using ShakeAlert messages was authorized in California in October 2019. Over the next few years, public alerts were expanded into Oregon and then into Washington ( U.S. Geological Survey, 2024 ). ShakeAlert source results are routinely compared to the USGS Comprehensive Catalog (ComCat; Guy et al. , 2015 ; U.S. Geological Survey, Earthquake Hazards Program, 2017 ), which contains the earthquake location and magnitude determined using complete waveform data. M 4.5 and larger is the threshold used for public alerting and was deliberately set below the level where damage is likely to compensate for cases where the system underestimates the magnitude. Between 17 October 2019 and 1 September 2023, the ShakeAlert system created 95 events with maximum magnitude estimates of M ≥4.5, the public alerting threshold. 94 of the 95 events were due to real earthquakes. Seven were categorized “false” per ShakeAlert’s internal definition that there was no matching catalog event within 100 km and 30 s of origin time; however, all but one of these were real earthquakes that were poorly located, primarily because they were at the edges of the seismic network. Three detected events were labeled “missed” because they were very poorly located (>100 km location error). In addition, the system did not produce solutions for four ComCat events M ≥4.5 ( U.S. Geological Survey, Earthquake Hazards Program, 2017 ), which were all at the edge of the alerting and network boundaries. The ShakeAlert system has accurately detected the majority of earthquakes that have occurred within the operational region since completing the public rollout, and alerts from the system have been delivered to millions of cell phone users throughout the West Coast.

California, Oregon, Washington↗

USGS2025

Overview of The SCEC/USGS Community Stress Drop Validation Study using the 2019 Ridgecrest earthquake sequence

We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress‐drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude‐spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress‐drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two‐week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. In this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite‐fault modeling, ground‐motion‐based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between‐method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress‐drop values.

California↗

USGS2024

What do we know without the catalog? Eliciting prior beliefs from experts for aftershock models

Fitting parametric seismological models to earthquake catalogs often comes with numerical challenges, especially when catalogs are small. An alternative way to quantify parameter values for a seismic region is by eliciting expert opinions on the seismological characteristics that each parameter corresponds to. For instance, expert beliefs on aftershock patterns can be formulated into prior distributions for aftershock parameters, for example, for the epidemic‐type aftershock sequence (ETAS) model. We illustrate such a method by not only eliciting priors for ETAS parameters for the Pacific Northwest (PNW), a subduction zone with a complex tectonic environment, but also a relatively small catalog. We compare these priors with those suggested by the ETAS literature for global subduction zones, discussing implications for aftershock forecasting for the PNW.

The Seismic Record↗

USGS2025

ShakeAlert® version 3: Expected performance in large earthquakes

The ShakeAlert earthquake early warning (EEW) system partners along with U.S. Geological Survey (USGS) licensed operators deliver EEW alerts to the public and trigger automated systems when a significant earthquake is expected to impact California, Oregon, or Washington. ShakeAlert’s primary goal is to provide usable warning times before the arrival of damaging shaking. EEW is most likely to achieve this goal in large‐magnitude earthquakes. In recent years, ShakeAlert has gone through a series of upgrades to its underlying scientific algorithms aimed at improved performance during large earthquakes. Version 3 of this software recently went live in the production system and includes improvements to all algorithms. The main seismic algorithms that detect an earthquake and characterize its location, magnitude, and fault rupture orientation are faster than older versions. Other key changes include: using real‐time geodetic data to characterize the magnitude growth in large earthquakes; the introduction of an alert pause procedure to compromise between speed near the epicenter and improved accuracy at larger distances; and the inclusion of a nonergodic site‐response model in the ground‐motion predictions. ShakeAlert has achieved its primary goal of usable warning times before strong shaking at some locations in real‐time operations in recent M 6 earthquakes. Using offline tests, we demonstrate usable warning times are possible for many sites with peak shaking values of modified Mercalli intensity (MMI) 7–8 in M 7+ earthquakes and also for many MMI 8–9 sites in M 8+ earthquakes. ShakeAlert partners use a variety of MMI and magnitude thresholds in deciding when to alert their users within bounds set by the USGS. Our study shows that there is room to raise the magnitude thresholds up to about M 5.5 without adversely affecting performance in large earthquakes. The ground‐motion criteria are more complex owing to a significant drop‐off in warning times between the MMI 4 and 5 levels of predicted shaking. However, widely used ShakeAlert products, such as the MMI 3 and 4 contour products, can provide sufficiently long warning times before strong shaking in moderate‐to‐great earthquakes to enable a range of protective actions.

Bulletin of the Seismological Society of America↗