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1,661 records · Page 85Linked to original sources
USGS2026

Measuring stress In high pressure deformation experiments with high speed fiber-optics

High‐pressure, high‐temperature rock deformation experiments are essential for understanding deep Earth processes, but accurately measuring stress is challenging due to the inaccuracy introduced by seal friction within the apparatus and large inertia, which affects earthquake process measurements made far from the experimental fault. To overcome these limitations, we developed and implemented a simple, compact fiber‐optic sensor based on an External Cavity Fabry–Perot Interferometer for in situ load measurement on a piston inside the pressure vessel of a Griggs‐type high‐pressure apparatus. The sensor can be used at sample temperatures up to 800°C for both slow creep and fast rupture testing with bandwidth capability from DC to 6 MHz. Two important experimental results are described: (1) the first direct measurements of seal friction in this apparatus type; these measurements record seal friction approximately one‐tenth of the confining pressure under both low (150 MPa) and high (1 GPa) pressure conditions; and (2) high‐temporal resolution capture of dynamic stress drops during stick‐slip faulting events at high‐confining pressure, revealing near‐fault wave propagation details, high‐frequency oscillations, and implying high‐transient slip rates (6–12 m/s) previously inaccessible with external sensors.

The Seismic Record↗
USGS2024

Cross-fade sampling: Extremely efficient Bayesian inversion for a variety of geophysical problems

This paper introduces cross-fade sampling, a computationally efficient Markov Chain Monte Carlo simulation method that uses a semi-analytical approach to quickly solve Bayesian inverse problems that do not themselves have an analytical solution. Cross-fading is efficient in two ways. First, it requires fewer samples to obtain the same quality simulation of the target probability density function (PDF). Secondly, it is much faster to evaluate the posterior probability of each sample than conventional sampling methods for simulating Bayesian posterior PDFs. Conventional methods require evaluating the prior probability (which describes your a priori constraints) and data likelihood (which describes the fit between the observations and the predictions of the model) for each sample model. However, cross-fading does not require evaluating the data likelihood, meaning that ‘big data’ can be fit with zero additional computational cost. Further, the cross-fading approach can be used to calculate the marginal likelihood associated with a model design, facilitating model comparison and Bayesian model averaging. Topics covered in this paper include derivation of the cross-fade approach and how it can be used to simulate Bayesian posterior PDFs and compute the marginal likelihood, discussion of the class of problems to which cross-fading can be applied (with examples from earthquake statistics, earthquake ground motion modelling, volcanic eruption forecasting, and finite fault slip modelling), demonstration of efficiency relative to existing sampling methods and discussion of how cross-fading can be used to account for prediction errors (i.e. epistemic errors) as part of the geophysical inverse problem.

Geophysical Journal International↗
USGS2024

Limited preservation of strike-slip surface displacement in the geomorphic record

Offset geomorphic markers are commonly used to interpret slip history of strike-slip faults and have played an important role in forming earthquake recurrence models. These data sets are typically analyzed using cumulative probability methods to interpret average amounts of slip in past earthquakes. However, interpretation of the geomorphic record to infer surface slip history is complicated by slip variability, measurement uncertainty, and modification of offset features in the landscape. To investigate how well geomorphic data record surface slip, we use offset measurements from recent strike-slip surface ruptures ( n = 39), faults with geomorphic evidence of multiple strike-slip earthquakes ( n = 29), and synthetic slip distributions with added noise ( n > 10,000) to examine the constraints of the geomorphic record and the underlying assumptions of the cumulative offset probability distribution analysis method. We find that the geomorphic record is unlikely to resolve more than two paleo-slip distributions, except in specific cases with low slip variability, high slip-per-event, and semiarid climate. In cases where site-specific conditions allow for interpretation of more than two earthquakes, lateral extrapolation along a fault is not straightforward because on-fault displacement and distributed deformation may be spatially variable in each earthquake. We also find that average slip in modern earthquakes is adequately recovered by probability methods, but the reported prevalence of strike-slip faults with characteristic slip history is not supported by geomorphic data. We also propose updated methods to interpret slip history and construct uncertainty bounds for paleo-slip distributions.

Journal of Geophysical Research: Solid Earth↗
USGS2025

Critical minerals in mine waste

Introduction Critical minerals are commodities with vulnerable supply chains that play a vital role in supporting the United States’ economy, national defense and security, emerging technologies, and energy independence. The prosperity of our Nation depends on generating a resilient supply of domestic critical minerals; mine waste may be an untapped source of these commodities. Mine waste from centuries of legacy mining persist on the landscape and may contain critical minerals and other valuable commodities previously deemed uneconomic to recover. At modern mines, the financial viability of recovering byproduct critical minerals, which are not the primary target, may be marginal and can ultimately destine them to mine waste. Further, mine waste can be a liability for the mining company or, at legacy mines, the taxpayer because of its effect on the landscape. The U.S. Geological Survey (USGS) has several initiatives to evaluate critical mineral resources in various types of waste. This factsheet highlights studies of mine waste carried out by USGS scientists at the Geology, Energy & Minerals Science Center in collaboration with other science centers funded through the USGS Mineral Resources Program. Recovery of critical minerals from mine waste can aid in remediation efforts and increase domestic supply of vital mineral resources.

Fact Sheet↗
USGS1990

Preconditioned Conjugate-Gradient 2 (PCG2), a computer program for solving ground-water flow equations

This report documents PCG2: a numerical code to be used with the U.S. Geological Survey modular three-dimensional, finite-difference, ground-water flow model. PCG2 uses the preconditioned conjugate-gradient method to solve the equations produced by the model for hydraulic head. Linear or nonlinear flow conditions may be simulated. PCG2 includes two reconditioning options: modified incomplete Cholesky preconditioning, which is efficient on scalar computers; and polynomial preconditioning, which requires less computer storage and, with modifications that depend on the computer used, is most efficient on vector computers. Convergence of the solver is determined using both head-change and residual criteria. Nonlinear problems are solved using Picard iterations. This documentation provides a description of the preconditioned conjugate gradient method and the two preconditioners, detailed instructions for linking PCG2 to the modular model, sample data inputs, a brief description of PCG2, and a FORTRAN listing.

Water-Resources Investigations Report↗
USGS2026

Rapid characterization of the 2025 Mw 8.8 Kamchatka, Russia earthquake

The 29 July 2025 M w 8.8 Kamchatka, Russia, earthquake was the sixth largest instrumentally recorded earthquake. This event was seismically well observed at regional and teleseismic distances, but publicly available near‐source data were sparse at the time of the event, presenting unique challenges for rapid source and impact characterization. The U.S. Geological Survey (USGS) National Earthquake Information Center provides global real‐time monitoring for earthquakes, including rapid response information products that estimate source characteristics, shaking, and the resulting impacts. We describe the USGS rapid response earthquake information products following the Kamchatka event and discuss their implications for ongoing hazards in the region. We describe potential improvements to our response workflows motivated by this event, including more rapid constraints on source geometries and the automated selection of fault geometries for finite‐fault inversions. The rapid response products together support the interpretation of a unilateral southwestward rupture with significant slip on the southwestern end of the rupture extent. The M w 8.8–9.0 event in 1952, which ruptured a comparable extent of the Kuril–Kamchatka subduction interface, has many similarities to the 2025 rupture. This illustrates that slip deficits may remain following great earthquakes and highlights the usefulness of comparative studies between historic and modern events.

Kuril-Kamchatka subduction zone↗
USGS2025

Unlocking ecological insights from sub-seasonal visible-to-shortwave infrared imaging spectroscopy: The SHIFT campaign

We stand at the threshold of a transformative era in Earth observation, marked by space-borne visible-to-shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter- and intra-annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub-seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High-Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub-seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open-source processing methods and cloud-based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high-frequency time series data from space-borne VSWIR imaging spectrometers.

Ecosphere↗
USGS2026

Incorporating location uncertainty improves inference with stop-level North American Breeding Bird Survey data

Ecological models should account for uncertainty to be most effective and useful. Yet, uncertainty from model covariates—unlike that from other sources, such as sampling error or process variability—is seldom explicitly incorporated. This can cause underestimates of uncertainty to cascade through model parameter estimates, predictions, and downstream uses. Burner et al. proposed a method for quantifying uncertainty in covariates and incorporating it into models using informative Bayesian priors. This method was applied to stop-level Breeding Bird Survey (BBS) analyses, where land cover uncertainty at each stop arises from substantial stop location uncertainty. A limited validation of model-estimated land cover, using stops with known locations, indicated the method’s potential effectiveness, but it was not rigorously evaluated. We conduct a robust simulation-based test, generating stop locations, extracting land cover, and simulating bird communities across 210 BBS routes in the upper Midwest. We compare 3 models: a “known” model with true land cover, a “naive” model assuming consistent 800-m stop spacing, and a “full” model using informative priors to estimate land cover. Species parameter estimates and predicted prevalence patterns across gradients in land cover from the full model approached those of the known model and were substantially closer to the true values used in simulations relative to those from the naive model. Naive model parameters were more biased relative to the other models, and credible intervals of predicted species prevalence rarely included the true simulated values. The full model also produced land cover covariate estimates closer to true simulation values relative to the mean informative priors. Our results show that, for the BBS, informative priors enable more accurate stop-level analyses despite location uncertainty. In contrast, naive models that ignore this uncertainty yield poor inferences. More broadly, we demonstrate empirically the utility of informative priors to account for covariate uncertainty in ecological models.

Michigan, Minnesota, Wisconson↗
USGS2023

Stratigraphic notes—Volume 1, 2022

This is the first volume in the U.S. Geological Survey (USGS) series of reports on stratigraphy entitled “Stratigraphic Notes,” which consists of short papers that highlight stratigraphic studies, changes in stratigraphic nomenclature, and explanations of stratigraphic names and concepts used on published geologic maps. “Stratigraphic Notes” is a long-term (multiyear), multivolume publication containing articles that address updates or revisions to stratigraphic nomenclature (and whose content ultimately will be incorporated by National Geologic Map Database personnel into Geolex, https://ngmdb.usgs.gov/Geolex/ ). We welcome papers for the “Stratigraphic Notes” series from geoscientists of the USGS, of State Geological Surveys, and from academicians. Papers can be submitted for publication in “Stratigraphic Notes” by contacting the USGS Geologic Names Committee ( gnc@usgs.gov ). As new “Stratigraphic Notes” volumes are published, links to the volumes will be posted at https://doi.org/10.3133/pp1879 . This first volume ("Stratigraphic notes—Volume 1, 2022") includes articles that provide guidance for those who wish to submit papers to “Stratigraphic Notes,” as well as information on how to make your manuscripts compliant for geologic names reviews and how to organize your paper’s content to facilitate inclusion of new or revised names in Geolex. This volume also includes some specific guidance on conducting geologic names reviews of geologic and hydrogeologic reports.

Professional Paper↗
USGS2025

Groundwater-storage change in the north Phoenix aquifer, Arizona, 2020–23

The city of Phoenix, Arizona, relies primarily on surface water for municipal water supply. The city also maintains wells to withdraw groundwater, particularly in times of drought and reduced surface-water supply, and to recharge groundwater when excess surface water is available. As of 2023, withdrawals from the aquifer in the northeastern part of the city are a small volume of water, less than 3,000 acre-feet in most years. Each year a similar volume of water is recharged through injection wells. The withdrawal wells are permitted to produce in total more than 20,000 acre-feet per year; increased pumping could lead to future groundwater-storage declines. To better understand groundwater-storage change in the north Phoenix aquifer, a repeat microgravity monitoring network was established in 2020. Measurements of changes in Earth’s gravity provide a direct, non-invasive measurement of subsurface mass change. Groundwater-storage changes were small during the 2020–23 study period, consistent with the relatively small volumes of pumping and recharge. Groundwater levels measured in monitoring wells were stable or increased slightly during this period, although the number of monitoring wells within the area of the gravity monitoring network is sparse. In total, about 15,000 acre-feet of water were pumped and 31,000 acre-feet recharged through injection in the north Phoenix aquifer during the 2020–22 period. Within the monitored area, groundwater storage increased by a small amount, about 1,000 acre-feet, in 2020, and decreased by a larger amount, about 6,000 acre-feet, each year in 2021 and 2022. Storage decreased at 89 of 102 stations from 2020 to 2023. Groundwater-storage decreases greater than the volume of net pumping indicate down-gradient subflow from the aquifer is greater than recharge plus incoming subflow, drying of the unsaturated zone resulting from decreased land-surface recharge, or both. At present (2023), the aquifer appears able to store and supply the relatively small amounts of water needed without excessive drawdown or harmful effects, such as land subsidence and surface inundation. If pumping or recharge increases in the future, the established repeat microgravity network is well suited to capture the spatial extent and magnitude of groundwater-storage changes in the aquifer.

Arizona↗
USGS2025

Fiber-imaged supershear dynamics in the 2024 Mw 7 Mendocino Fault earthquake

Fault structure and rupture physics are deeply intertwined, and observations of this coupling are critical for understanding earthquake behavior. Rupture propagation is observable at fine scales using dense seismic networks. Fiber-optic sensing allows for long-term deployments of ultradense arrays that enable high-resolution measurements of infrequent, large earthquakes. We recorded the 2024 moment magnitude ( M w ) 7 Mendocino Fault earthquake with a nearby fiber-optic array and imaged its behavior with seismic beamforming. The rupture propagated to the east at subshear velocity; stagnated near the Mendocino Triple Junction, a zone of structural complexity; and subsequently transitioned to supershear velocity. The correlation between source physics and structure shows how lithospheric heterogeneity affects first-order characteristics of earthquake ruptures. Our results also demonstrate the potential for fiber-optic sensing to improve real-time estimation of key parameters for early warning.

California↗
USGS2024

Constraining mean landslide occurrence rates for non-temporal landslide inventories using high-resolution elevation data

Constraining landslide occurrence rates can help to generate landslide hazard models that predict the spatial and temporal occurrence of landslides. However, most landslide inventories do not include any temporal data due to the difficulties of dating landslide deposits. Here we introduce a method for estimating the mean landslide occurrence rate of deep-seated rotational and translational slides derived solely from high-resolution (≤3 m) elevation data and globally available estimates of the diffusion coefficient for sediment flux. The method applies a linear diffusion model to the roughest landslide deposits until they reach a representative non-landslide roughness distribution. This estimates the time for a landslide deposit to be unrecognizable in high-resolution digital elevation data, which we term the mean lifetime of the landslide. Using the mean lifetime and number of landslides within an area of interest, we can estimate the mean occurrence rate of landslides over that domain. We validate this approach using a comprehensive temporal inventory of landslides in western Oregon created using age-roughness curves that are calibrated with high-resolution elevation data and radiocarbon data. We find good agreement between our diffusion method and the existing age-roughness-derived estimates, producing mean lifetimes of 4500 and 5200 years (4% difference), respectively. Hazard maps produced using the two methodologies generally agree, with the maximum differences in landslide probability reaching 0.1. Due to the relative abundance of high-resolution elevation data compared with age-dated landslides, our method could help constrain landslide occurrence rates in areas previously considered unfeasible.

Oregon↗
USGS2025

Fault displacement model for surface principal rupture of strike-slip faults

The probability distribution model for principal displacement accommodated on the surface main trace is a critical input to the fault displacement hazard analysis. This article presents a new model for strike-slip ruptures in the moment magnitude ( M ) range of 6 to 8.3. The new model is the outcome of a multi-year research effort to update the widely used model developed by Petersen and others in 2011. Updates include the adoption of the Fault Displacement Hazard Initiative database and enhancements to rupture and displacement data preparation. Statistical formulation and estimation have also been updated substantially. A three-parameter modified normal distribution that we refer to as the negative Exponentially Modified Gaussian distribution is adopted to model the probability distribution of the natural logarithm of principal displacement. Formulation for the mean parameter of the modified normal includes a random earthquake term, a nonlinear scaling relation with M , and an ellipse function for along-main-trace variation. The aleatory variability of the updated model now depends on M as well as site’s along-main-trace position. These updates not only significantly improve the fit to the distribution of the observed displacements but also yield reasonable 95th percentile predictions for M > 7.5 events. Alternative models representing the estimation uncertainty of the M -scaling relation are also developed. These new models are compared to the previous model in terms of percentile predictions and the calculated hazard curves. The steeper hazard curves from the new models yield a lower exceedance rate than the normal-distribution based model developed previously by Petersen and others.

Earthquake Spectra↗
USGS1986

Potential for leakage among principal aquifers in the Memphis area, Tennessee

The principal aquifers in the Memphis area consist primarily of sand or sand and gravel, and the confining beds consist of clay, silt, sand, and lignite. The Jackson Formation and upper part of the Claiborne Group serve as the confining bed separating the water table aquifers from the Memphis Sand; the Flour Island Formation separates the Memphis Sand from the Fort Pillow Sand. Differences in total hydraulic head among the principal aquifers in the Memphis urban area result in vertical hydraulic gradients which create a potential for inter-aquifer exchange of water. Throughout this area, the gradient is downward from the water table aquifers to the Memphis Sand. In the central part of the Memphis urban area, the vertical hydraulic gradient is upward from the Fort Pillow Sand to the Memphis Sand, and in the eastern and western parts, it is downward from the Memphis Sand to the Fort Pillow Sand. The vertical distribution of carbon-14 data for water from the fluvial deposits, Memphis Sand, and Fort Pillow Sand shows an increase in the relative age of the water with depth. The areal distribution of carbon-14 data for water from the upper part of the Memphis Sand indicates that relatively recent water has been brought into the major cone of depression in the potentiometric surface of the Memphis Sand, either by horizontal movement or from downward vertical leakage. The normal, near-surface geothermal gradient in the Memphis area was determined to be 0.6 C/100 ft. Deviations from the normal geothermal gradient, in areas affected by intense pumping from the Memphis Sand, indicate that downward vertical leakage occurs from the water table aquifers through the Jackson-upper Claiborne confining bed to the Memphis Sand. The velocity of downward vertical leakage of water from the Memphis Sand through the Flour Island confining bed to the Fort Pillow Sand was determined to be 0.0066 ft/day by analysis of borehole temperature data from an observation well in the northeastern part of the Memphis area. From this velocity and the head difference between the Memphis Sand and the Fort Pillow Sand at this locality, the hydraulic conductivity of the Flour Island confining bed was determined to be 0.00114 ft/day. (Lantz-PTT)

Tennessee↗
USGS2026

Predictable seismic cycles result from structural rupture barriers on oceanic transform faults

Earthquakes of magnitude ( M ) >5.5 on oceanic transform faults (OTFs) repeatedly rupture the same locked patches, sometimes quasiperiodically. These patches are separated by “barriers” that halt earthquake propagation and slip mostly aseismically. However, the physical processes governing this systematic behavior remain unclear. We analyzed two barriers along the Gofar transform fault that have arrested ~15 M 6 earthquakes over the past three decades. Ocean bottom seismometer data indicate that the barriers hosted intense microseismicity before the mainshocks and comprise multistrand faults and transtensional stepovers with 100- to 400-m lateral offset. These characteristics contradict earthquake rupture termination models invoking velocity-strengthening friction or large geometric steps and instead point to damage-enhanced porosity and dilatancy-strengthening mechanisms. By isolating rupture segments, the barriers regulate the quasiperiodic recurrence of OTF earthquakes.

Science↗
USGS2025

To heal or not to heal?: 1. The effect of pore fluid pressure on the frictional healing behavior of lithologies in Oklahoma

The competition between fault healing (i.e., re-strengthening) and fault loading determines the timing and magnitude of fault failure within the seismic cycle. Repeating earthquakes can give observational estimates of fault healing rates, however, it is difficult to link laboratory studies of frictional healing and observed healing rates from repeating earthquakes in part because of uncertainty in lithology at depth. Due to well-constrained and relatively simple geology, earthquakes in Oklahoma can be linked to the granitic basement rock and to the Arbuckle Group, which is primarily composed of dolomite at earthquake depths. Here, we conduct friction experiments to measure healing rates of the two earthquake-bearing lithologies at confining pressures representative of earthquake depths and pore pressures ranging from 0% to 80% of the confining pressure. We measure frictional healing by executing slide-hold-slide tests with hold times ranging from 3 s to 3000 s. The friction experiments on the Troy Granite indicate that pore fluid pressure does not greatly affect healing rate. On the other hand, the dolomite of the Arbuckle Group exhibits decreased healing with increased pore fluid pressure, with weakening at the highest pore pressure. We hypothesize that this is due to an increase in dissolution of dolomite at high pore pressures/low effective normal stress. These healing rates are used in the companion paper to understand the moment-recurrence time behavior of repeating earthquakes in Prague, Oklahoma. This work has implications for possible enhanced dissolution and weakening behavior of the Arbuckle Group during wastewater injection activities.

Oklahoma↗
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↗
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↗