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USGS2026

Cruise summary—Samoa Basin abyssal mapping—Box coring leg, Pago Pago, Territory of American Samoa to Pago Pago, American Samoa, April 11– May 1, 2026

Expedition Summary U.S. Geological Survey scientists led a box coring effort to the Samoa Basin to characterize minerals and the surrounding abyssal sediments and fauna. Thirty-eight box cores were deployed between April 13, 2026, and April 28, 2026. Thirty-six box cores recovered sufficient material to determine nodule density, and 35 recovered sufficient material for subcores to be collected. The purpose of this Data Report is to provide a summary of samples collected, initial results that were obtained shipboard, and briefly mention planned future analyses from this expedition.

Data Report↗

USGS2025

Footprints of past mining in Alaska (USA) derived from high-resolution satellite imagery

Mapping the land area used for mining in the past is essential for guiding the remediation of affected landscapes and assessing the resource potential of related waste products. Despite significant recent progress delineating footprints of active and inactive mining globally, the known inventory of such mine lands remains incomplete. Here, I describe a new map dataset of footprints of land surface disturbance and waste at sites of past mining in Alaska (USA) based on visual interpretation of satellite imagery. This dataset maps 6–14 times the area of previous regional and global mine footprint maps in Alaska and is the first in the region to explicitly delineate mine waste landforms (e.g., tailings piles). The data are publicly available from the U.S. Geological Survey under a “no rights reserved” Creative Commons (CC0) license agreement.

Alaska↗

USGS2024

Separating signals in elevation data improves supervised machine learning predictions for hydrothermal favorability

A recent study identified topography (land surface elevation above sea level) as an important input dataset (feature) for predicting the location of hydrothermal systems in the Great Basin in Nevada. Yet, topography is generally a result of more than one geological process and may consequently contain multiple distinct signals. For example, the geologic evolution of the Great Basin has produced both crustal thickening (i.e., regional-scale trends in elevation) and thinning via Basin and Range extensional faulting (i.e., valley-scale topographic relief). We postulate that these geologic processes may affect the occurrence of hydrothermal systems differently. Therefore, we separate the regional trend from the valley-scale signal in the Great Basin, and then use them separately to evaluate the importance of each as predictors for hydrothermal favorability. Our prior work applying supervised machine learning (ML) using the data from the Nevada Machine Learning Project demonstrated that employing a training strategy that randomly selects negative training sites produces better performing models for predicting hydrothermal favorability than a training strategy that uses expert-selected negatives. The models created using both training strategies exhibited a west-east geographic trend in the predictions for the favorability of hydrothermal resources. These models generally predicted higher favorability in western Nevada and lower favorability in eastern Nevada. This west-east trend in predicted favorability correlates with elevation across the Great Basin, which trends higher from west to east. By separating the original elevation feature into distinct features for elevation trend (i.e., regional-scale topography) and detrended elevation (i.e., valley-scale or local relative topography), we find that models using the separated topographic signals consistently outperform competing models that use the original elevation feature. Although western Nevada still exhibits higher favorability than eastern Nevada, using separated signals for regional elevation and local structure reduces the west-east prediction trend in the region and emphasizes structures associated with hydrothermal upflow. This work emphasizes how carefully engineering features to represent geological conditions relevant to hydrothermal systems allows ML algorithms to detect important patterns for predicting hydrothermal resource favorability and leads to better model performance.

Conference Paper↗

USGS2024

Lithium resource in the Smackover Formation brines of Southern Arkansas

Lithium-rich brine deposits occur throughout the United States, including in the Smackover Formation. The concentration of lithium in Smackover Formation brines was predicted across southern Arkansas by using a machine-learning model that incorporated lithium concentration data and geologic information. Between 5.1 and 19.0 million metric tons of lithium are calculated to be present in the brines of the Smackover Formation in southern Arkansas. The range in possible total lithium reflects the uncertainty in machine-learning predictions of lithium concentrations and the range of Smackover Formation porosity. This estimate quantifies the in-place lithium resource and does not consider the technological and economic feasibility of extracting the lithium from the brines.

Arkansas↗

USGS1976

Non-radiometric borehole geophysical detection of geochemical halos surrounding sedimentary uranium deposits

Roll-type uranium deposits are formed by the concentration of uranium by ground water in geochemical cells. Non-uranium minerals having different solubilities may be deposited ahead of or behind the uranium minerals, forming halos that surround the ore. In addition, oxidizing and reducing environmental conditions may cause zones of mineral alteration to develop beyond the limits of the uranium deposit. Certain physical property anomalies that are commonly associated with halos can be detected by relatively fast and inexpensive borehole geophysical measurements made either in individual holes, or between two adjacent holes. Borehole measurements that have been found to be useful include electrical resistivity, induced polarization, and magnetic susceptibility. Electrical resistivity is increased by the presence of calcite and other cementing minerals that sometimes create permeability barriers in the neighborhood of uranium deposits. Induced polarization (IP) response is increased by sulfide and clay minerals that are commonly found in anomalous concentrations near roll-type deposits. Magnetic susceptibility is usually decreased by the oxidation of magnetite to hematite or limonite in the zone of chemical alteration that is left as a trail behind roll fronts. Borehole measurements of electrical resistivity, induced polarization and magnetic susceptibility were made in the vicinity of a uranium roll-type deposit in south Texas. Results indicate that mineral halos can be detected by borehole measurements made. in wide-spaced drill holes, and that the total amount of drilling needed to find a deposit can be reduced substantially by this exploration approach.

Open-File Report↗

USGS2025

Site-specific, extended ShakeMaps for earthquake engineering applications

The U.S. Geological Survey (USGS) routinely produces ShakeMaps of shaking intensity across the globe. Due to practical constraints, the number of response spectral periods was limited to three standard periods (0.3, 1.0, and 3.0 sec). We have recently developed the tools that are necessary to expand this functionality to include 22 periods (matching the current U.S. National Seismic Hazard Model periods) as well as the orientation-independent components (e.g., “RotD50”). We refer to ShakeMap products that include these extensions as “extended ShakeMaps.” The added level of complexity motivated us to also develop a user-friendly tool called the “ShakeMap Sampling Tool” (SST) that gives all the estimated shaking metrics for a specific location (or list of locations). Additionally, we develop a web application where users can input locations of interest and view/download the SST results. We further familiarize users with the concept of “Composite ShakeMaps.” For earthquakes sequences such as a mainshock and larger foreshocks and aftershocks, this provides a map of the maximum value of each shaking metric, which is useful for overall loss estimates, the full extent of ground failure triggering potential, and a better portrayal of the repeated shaking levels at a given point for a series of earthquakes. Such a site-specific shaking history facilitates earthquake forensics at building or infrastructure sites for which damage may be of concern, as described in the Disproportionate Damage Earthquake trigger specified in the IEBC (2018, Section 405.2.2) and in developing ATC-145 guidelines (Guidelines for Post-Earthquake Assessment, Repair, and Retrofit of Buildings). The composite ShakeMap can be combined with the SST for a variety of earthquake-hazard applications, such as systematically inferring triggering shaking estimates at specific sites of geotechnical interest for landsliding, liquefaction, and lateral-spreading hazards.

Kahramanmaraş↗

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↗

USGS2025

Application of Hydrologic Simulation Program—FORTRAN (HSPF) as part of an integrated hydrologic model for the Salinas Valley, California

The U.S. Geological Survey (USGS), in cooperation with the Monterey County Water Resources Agency, conducted studies to help evaluate the surface-water and groundwater resources of the Salinas Valley study area, consisting of the entire Salinas River watershed and several smaller, adjacent coastal watersheds draining into Monterey Bay. The Salinas Valley study area is a highly productive agricultural region that depends on the coordinated use of surface water and groundwater to meet demand for irrigation and public water supply. To continue to meet these demands, a better understanding of the historical water balance and the effects of water-resource development on the long-term sustainability of water resources in the Salinas Valley study area is needed.

California↗

USGS2025

Assessing earthquake risks to lifeline infrastructure systems in the United States

The security and economic stability of the United States rely heavily on robust lifeline infrastructure systems and yet the risks to such systems are seldom quantified at the national scale. For example, while earthquake risks to buildings in the United States have been investigated at the national scale regularly, such risks to gas pipelines have rarely been investigated nationally. In this paper, we use examples from two critical infrastructure sectors to demonstrate (1) the nature of earthquake risks to lifeline infrastructure systems, (2) complexities involved in regional seismic risk assessments, and (3) how such risks change with time. We found that bridge risks can be underestimated by at least 64 % when viewed from repair costs instead of traffic demands and that regional risks can be underestimated by 19 % when spatial correlations of ground motion are ignored. Further, exceedance of traffic demand can be 50 times more likely to occur when viewed at the regional scale than when viewed at an individual bridge. Similarly, exceedance of repairs can be 180 times more likely to occur when viewed at the pipeline network level than at a segment-specific level. Finally, sensitivity analyses with the 2018 and 2023 USGS National Seismic Hazard Models indicate an increase in bridge risk of at least 24 % and an increase in exposed gas pipeline mileage of 43 %. The evolution of risks, complexities involved in assessments, and limited resources jointly underscore the need for more routine updates to nationwide seismic risk assessments of lifeline systems in the United States.

International Journal of Critical Infrastructure P↗

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↗

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

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↗

USGS2024

Analysis of factors affecting plume remediation in a sole-source aquifer system, southeastern Nassau County, New York

Several plumes of dissolved, chlorinated solvents, including trichloroethylene, have been identified in a sole-source aquifer near the former Northrop Grumman Bethpage Facility and Naval Weapons Industrial Reserve Plant sites in southeastern Nassau County, New York. Past investigations have documented that the groundwater contamination originated from this industrial area and now extends to the south, in the direction of groundwater flow. The intermixed plumes are commonly referred to as the “Navy Grumman groundwater plume.” Detailed groundwater-flow modeling was needed for the New York State Department of Environmental Conservation (NYSDEC) to evaluate design options necessary for the construction, operation, optimization, maintenance, and monitoring of a groundwater extraction and treatment cleanup plan selected in a December 2019 Amended Record of Decision by the NYSDEC to comprehensively address these plumes. Consequently, the NYSDEC began a cooperative study with the U.S. Geological Survey in 2020 to better understand the local hydrogeologic framework using two independent approaches to characterize aquifer heterogeneity and update an existing regional groundwater-flow model to provide transient boundary conditions for new inset groundwater-flow models of the plume area. We developed these detailed inset models for the two independent aquifer characterizations using history-matching techniques coupled with a novel approach to risk-based management optimization of the remedial design. We also used the updated regional model to assess this optimized groundwater extraction and treatment design for potential saltwater intrusion. The ensembles of parameters resulting from history matching provided a platform with which to evaluate capture by water-supply and remedial wells using particle-tracking techniques. Using the ensemble to select a risk stance, we performed multiobjective optimization to identify various configurations of remedial pumping that are consistent with external constraints and that favor potentially competing objectives. Multiple solutions provide tradeoffs that NYSDEC can consider. In general, pumping redistribution may help to prevent further contamination migration downgradient. These and other study results are intended to support decisions for the remedial design focused on the local area encompassing the full extent of the Navy Grumman groundwater plume.

New York↗

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↗