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1,688 records · Page 77Linked to original sources
USGS2026

3D semantic mapping of surface geological features

Semantic mapping in 3D is fundamental to a wide range of geoscientific studies and applications, including geomorphology, hazard assessment, and environmental monitoring. However, automatically segmenting geological features from large-scale photogrammetric datasets remains a significant challenge. We present a methodology to address this gap. Using overlapping images collected over environments of interest, Structure-from-Motion (SfM) produces georeferenced point clouds and estimates camera poses. Existing large vision models, such as Segment Anything Model, segment objects in the images, generating pixel-segmentation associations. To produce pixel-point associations, we project the points back onto the camera image planes. As objects are independently segmented across multiple images with different perspectives, we develop a segmentation mosaicking algorithm to build probabilistic point-segmentation associations that combines the pixel-segmentation associations and pixel-point associations. Our methodology is validated using both synthetic data generated by Kubric and real-world UAV-SfM data. The implementation is designed to be compatible with existing SfM software, including Agisoft and OpenDroneMap, for photogrammetry mapping in geoscience studies. As a case study, we apply our method to the semantic mapping of precariously balanced rocks (PBRs), which provide upper-bound constraints on historical ground motion shaking intensity. To support object-level identification of PBRs, we additionally integrated Grounding DINO, enabling text-prompted segmentation of features of interest within UAV imagery. This case study demonstrates the effectiveness of our method in generating a 3D semantic map of PBRs, enabling spatial distribution of PBR fragility for earthquake hazard analysis.

Computers & Geosciences↗
USGS2025

Accurate simulation of flow through dipping aquifers with MODFLOW 6 using enhanced cell connectivity

In simulations of groundwater flow through dipping aquifers, layers of model cells are often “deformed” to follow the top and bottom elevations of the aquifers. When this approach is used in MODFLOW, adjacent cells within the same model layer are vertically offset from one another, and the standard conductance-based (two-point) formulation for flow between cells does not rigorously account for these offsets. The XT3D multi-point flow formulation in MODFLOW 6 is designed to account for geometric irregularities in the grid, including vertical offsets, and to provide accurate results for both isotropic and anisotropic groundwater flow. A recent study evaluated the performance of the standard formulation and XT3D using a simple, synthetic benchmark model of a steeply dipping aquifer. Although XT3D generally improved the accuracy of flow simulations relative to the standard formulation as expected, neither formulation produced accurate flows in cases that involved large vertical offsets. In this paper, we explain that the inability of XT3D to produce accurate flows in the steeply dipping aquifer benchmark was not due to an inherent limitation of the flow formulation, but rather to the limited cell connectivity inherent in the most commonly used discretization packages in MODFLOW 6. Furthermore, we demonstrate that XT3D is able to produce the expected accuracy when adequate cell connectivity is introduced using MODFLOW's unstructured grid type and the aquifer is discretized vertically using at least two model layers.

Groundwater↗
USGS2026

Cotton farming pesticides affect ileal microbiota activity expressions of virulome but not resistome or metabolic pathways in a sedentary wild passerine

The increased use of agrochemicals to enhance crop production has had detrimental environmental effects including implication in the sharp decline of North American farmland-breeding birds. Here, using a combination of deep shotgun metatranscriptomics and pesticide exposure data, we sought to assess whether exposure to cotton ( Gossypium spp.) production had a differential effect on ileum multi-kingdom microbial activity, metabolism, anti-microbial resistance, and virulence factors of sedentary northern mockingbirds ( Mimus polyglottos ) sampled from two cotton-producing areas (16 birds in total) and one uncultivated area (7 birds) in Texas, USA. Both Shannon Index values (Adj. r 2 = 0.174, F (1,21) = 5.633, p = 0.027) and a Mantel test (Spearman ρ = 0.184, p = 0.013) supported a relationship between metabolically active microbiota Bray–Curtis dissimilarities and differences in pesticide mixtures among study areas. Virulence factor richness (Adj. r 2 = 0.182, F (1,21) = 5.890, p = 0.024), Shannon Index (Adj. r 2 = 0.231, F (1,21) = 7.612, p = 0.012), and load (sum of virulence factor abundances; Adj. r 2 = 0.160, F (1,21) = 5.194, p = 0.033) were related to total pesticide load (total quantity of pesticides). We found no pesticide effects on expression of either antimicrobial resistance genes or metabolic pathways.

Texas↗
USGS2024

Estimating domestic self-supplied water use in Rhode Island, 2014–21

Water withdrawal from private groundwater wells is often unaccounted for in water planning studies, and water from private wells can be a source of exposure to environmental contaminants. The sizes of populations that depend on private wells for domestic water use and the amounts of water that are withdrawn from these wells are generally poorly represented in data collection efforts because of the challenges of locating, metering, or gathering withdrawal information from individual property owners. To address this problem, the U.S. Geological Survey, in cooperation with the Rhode Island Water Resources Board, estimated the volume of water withdrawn from domestic self-supply wells and the populations who use them for the State of Rhode Island at a 30-meter pixel spatial resolution and one-month temporal resolution between July 2014 and June 2021. The number of people reliant on domestic self-supply wells has increased in Rhode Island over the study period; however, the statewide estimate of total water withdrawal has not statistically increased. Withdrawals from private wells are largest in the inland areas of the western part of the State, and the towns of Scituate and Charlestown have the highest estimated withdrawals. Statewide monthly withdrawals ranged from 3.987 million gallons per day in March 2018 to 7.767 million gallons per day in September 2016. The median per capita domestic water use rate was 46.0 gallons per capita per day.

Rhode Island↗
USGS2026

Bottom-up characterization of geologic methane emissions in the San Juan Basin in the southwestern USA

Methane is a potent greenhouse gas that plays an important role in atmospheric chemistry and global warming. The current global methane budget has large uncertainties, and a better understanding of the budget would help to guide strategies for reducing anthropogenic emissions to fight climate change. Natural geologic methane emissions are a particularly poorly constrained source, with top-down estimates from 14 C in ice cores suggesting much lower geologic emissions than bottom-up scaling of direct flux measurements. Our study aims to contribute to resolving this discrepancy through improved bottom-up characterization of geologic methane seepage in the San Juan Basin in southwestern Colorado and northwestern New Mexico, USA. We performed 983 new flux chamber measurements in this basin during summer 2022 and winter 2023 field campaigns. Our results, in combination with prior measurements, suggest that natural seepage in the San Juan Basin only occurs on or near the Fruitland coal outcrop. Specifically, our new measurements confirm previous measurements of seepage along the northwestern exposure of the Fruitland outcrop in Colorado (a known hydrodynamic overpressure region) and for the first time, identified seepage locations along the southernmost Fruitland outcrop exposure in New Mexico, in association with a coal cleat and a fault. Overall, seepage along the Fruitland coal outcrop is heterogeneously distributed, with both positive and negative (interpreted as microbial soil sink) methane fluxes. Features that are hypothesized to be predictive of seepage (e.g., faults) were not associated with positive methane fluxes in areas outside of the Fruitland outcrop. Our best estimate for total geologic methane seepage in the San Juan Basin from spatial interpolation and statistical upscaling is approximately 0.14 Tg CH 4 /yr, with a range from 0.029 to 0.48 Tg CH 4 /yr. This best-estimate value is lower than a previous bottom-up estimate from a gridded seepage inventory, but higher than a previous top-down estimate.

Colorado, New Mexico↗
USGS2023

Hidden system identification: Basin modeling as a tool for examining sedimentary geothermal resource potential

Three-dimensional (3D) geologic and temperature models have been developed for the onshore U.S. Gulf Coast. The results from these models identify areas of moderate- to high-temperature (90°-150°C and >150°C; respectively) geothermal resources at depths <6 km. This modeling study addresses the fundamental challenge of predicting where opportune temperature and lithology coincide. Unlike traditional geothermal systems with surface expressions of hydrothermal circulation (e.g., hot springs, fumaroles, sinter), sedimentary geothermal systems (SGS) are generally hidden. Historically, simplified efforts to predict subsurface temperatures in sedimentary basins have focused on linear temperature extrapolation that does not consider the variable thermal properties of different lithologies or lithologic changes with depth (e.g., compaction, lithification). Therefore, the need to understand basin architecture and predict temperatures in 3D within SGS is paramount to identifying geothermal resources and determining economic feasibility. Basin modeling software has long been used to characterize the subsurface conditions of sedimentary basins, including temperature, in the pursuit of finding hydrocarbons. This tool can also be adapted to evaluate the potential of geothermal resources in a sedimentary basin by predicting the confluence of desirable temperatures and reservoir lithologies. In this work, PetroMod basin modeling software was used to create a regional geologic model of the onshore U.S. Gulf Coast, covering over 500,000 km 2 calibrated to temperature data from wells. Inputs include structural surfaces from commercial databases, lithology information derived from published literature, and corrected bottom-hole temperatures (BHT) from over 6,000 wells. The resulting 3D geologic model can be used to predict temperatures throughout the basin. Maps were exported showing the depth, depositional unit, and reservoir lithology at which temperatures of 90°C and 150°C were reached, revealing over 400,000 km 2 of moderate- to high-temperature resources at depths <6 km. These maps function as a first-order screening tool to identify areas where low-, moderate-, or high-grade resource potential may exist, based on temperature and if optimal reservoir lithologies or depositional units of interest are present. Depending on the success criteria of a project, the same maps can be exported for any isotherm or incorporate other 1407 Gardner and Birdwell subsurface properties. The methodology employed in this work can be applied in any sedimentary basin with available subsurface data. Further calibration incorporating other data, including pressure and porosity, can expand the utility of basin modeling for geothermal evaluations. Basin modeling is a powerful but underutilized tool for identifying prospective geothermal resources in sedimentary basins.

Conference Paper↗
USGS2022

South Carolina and Landsat

South Carolina, the eighth State admitted to the union, transcends its size with its deep, rich history; striking beauty; vast natural resources; and extensive cultural diversity. Home to part of the Blue Ridge Mountains of the Central Appalachians, the Upstate is graced with more than 100 waterfalls, while the Lowcountry borders the Atlantic Ocean with 187 miles of coastline and 35 barrier islands. Forests cover two-thirds of the State, and forestry and agriculture together, as agribusiness, make up South Carolina’s leading industry. Two historic crops—cotton and tobacco—still rank in the top 10 commodities, though corn and soybeans now rank higher. Poultry, cattle, peanuts, and flowers also make the list. South Carolina’s population totals more than five million. Other residents include a variety of wildlife, bird, reptile, and fish species, including Ursus americanus (black bears), Alligator mississippiensis (American alligators), and Tursiops truncatus (bottlenose dolphins). More than 100 tree species also reside in South Carolina, which pays homage to one with its “The Palmetto State” nickname. South Carolina’s subtropical climate, long coastline, and lower elevations make it highly susceptible to tornado and hurricane activity and coastal flooding. Projected sea-level rise is a growing concern. A view from space can help monitor and manage natural resources on the land and in rivers, marshes, and the coast. Landsat reveals not just what an area looks like now, but also insights from decades ago.

South Carolina↗
USGS2026

Groundwater source water assessment area delineation of public supply wells in the Treasure Valley, western Idaho

The Idaho Department of Environmental Quality (IDEQ) is responsible for assessing the source water for public water supply systems including the delineation of source water assessment areas for wells, which are defined as the subsurface areas contributing water to a well during a period of time. Part of the IDEQ assessment plan is to update source water assessments over time. The U.S. Geological Survey (USGS) developed a new groundwater-flow model for part of the western Snake River Plain aquifer in southwestern Idaho. This groundwater- flow model provided a new opportunity for IDEQ to update source water assessment for public water supply wells within the model boundary. The USGS, in cooperation with IDEQ, used the USGS Treasure Valley Groundwater Flow Model along with a MODPATH particle tracking post-processing program to reassess public-water supply source water assessment areas. The USGS created an automated script that requests a well location, depth, and pumpage rate, and then incorporates that information into the Treasure Valley Groundwater Flow Model combined with the MODPATH model to create 3-, 6-, and 10-year time of travel zones that are output to a geographic information system shapefile. The TVSWA_Delineator script provides a fast, transparent tool for IDEQ to reassess or create new public-water supply source water assessment area delineations based on new and updated information and is available as a Python script and a Windows executable with the compiled script and all necessary components. TVSWA_Delineator utilizes a quadtree approach to rediscretize the Treasure Valley Groundwater Flow Model grid around the pumping well location from 1-mile cell lengths telescoped down to 1/16 mile. This rediscretization increases the source water assessment area delineation resolution near the pumping well and minimizes the effect of weak sinks in the MODPATH model. The stress inputs for each month of the final 10 years (2006 to 2015) of the Treasure Valley Groundwater Flow model historical simulation were averaged for the particle tracking analysis. The MODPATH model used backwards tracking of particles released at each stress period to create the source water assessment area delineations. This report documents the creation of the TVSWA_Delineator Python script, including adaptations made to the Treasure Valley Groundwater Flow Model, creation of a companion MODPATH model, and post-processing results to provide source water assessment areas.

Idaho↗
USGS2024

Forecasting storm-induced coastal flooding for 21st century sea-level rise scenarios in the Hawaiian, Mariana, and American Samoan Islands

Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated Hawaiian, Mariana, and American Samoan Islands as a result of climate change and sea-level rise. We followed a hybrid (dynamical and statistical) downscaling approach to map flooding due to waves and storm surge at 10-square meter resolution along all 1,870 kilometers of these islands’ coastlines for annual (1-year), 20-year, and 100-year return-interval storm events and +0.00 meter (m), +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m sea-level rise scenarios. We quantified the coastal flood depths and extents using the latest climate forcing from Intergovernmental Panel for Climate Change’s Sixth Assessment Report Coupled Model Intercomparison Project. The data generated using these methods provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when climate change and sea-level rise increase coastal storm-induced flooding to help identify areas where management and (or) restoration could potentially help reduce the risk to, and increase the resiliency of, the coastal communities in the populated Hawaiian, Mariana, and American Samoan Islands.

Hawaii↗
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↗
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↗
USGS2024

Subducting plate structure and megathrust morphology from deep seismic imaging linked to earthquake rupture segmentation at Cascadia

The origin of rupture segmentation along subduction zone megathrusts and linkages to the structural evolution of the subduction zone are poorly understood. Here, regional-scale seismic imaging of the Cascadia margin is used to characterize the megathrust spanning ~900 km from Vancouver Island to the California border, across the seismogenic zone to a few tens of kilometers from the coast. Discrete domains in lower plate geometry and sediment underthrusting are identified, not evident in prior regional plate models, which align with changes in lithology and structure of the upper plate and interpreted paleo-rupture patches. Strike-slip faults in the lower plate associated with oblique subduction mark boundaries between regions of distinct lower plate geometry. Their formation may be linked to changes in upper plate structure across long-lived upper plate faults. The Juan de Fuca plate is fragmenting within the seismogenic zone at Cascadia as the young plate bends beneath the heterogeneous upper plate resulting in structural domains that coincide with paleo-rupture segmentation.

Oregon, Washington↗
USGS2025

Utilizing downhole datasets for modelling the aeromagnetic signature of the Iron Creek Co-Cu deposit in the Idaho Cobalt Belt

The Idaho Cobalt Belt in east-central Idaho is host to some of the largest domestic Co resources, including the informal Iron Creek deposit. The two main ore zones of this deposit, the Iron Creek and the Ruby, are hosted in greenschist-grade interbedded argillite/siltstone and quartz-rich units of the Mesoproterozoic Apple Creek Formation of Lemhi Group. The primary ore mineral is cobalt-bearing pyrite, which occurs with pyrrhotite, chalcopyrite, and magnetite. This study integrates mineralogical and core-scale geophysical measurements with regional-scale aeromagnetic surveys. The high magnetite content within the Ruby zone produces elevated magnetic susceptibility, but the relatively limited spatial footprint of the ore zone would produce a small-scale anomaly that may be overlooked in regional surveys. The low magnetite content in the Iron Creek zone results in low magnetic susceptibility, creating a relatively low amplitude geophysical response. By characterizing the magnetic properties and mineralogy of these ore zones, this study enhances the interpretation of aeromagnetic data, enabling the identification of small or faint anomalies as potential Co targets. These findings improve can improve exploration strategies, both within the Idaho Cobalt Belt as well as for similar deposit types globally.

Idaho↗
USGS2026

Forecasting volcanic activity in Germany—A multi-criteria approach

Igneous activity, including shallow intrusions and volcanism, has the potential to disrupt underground critical infrastructure. Notably, future underground infrastructure projects like high-level radioactive waste repositories must be sited in areas of extremely low disruption probability by igneous activity. In Germany, according to the Repository Site Selection Act of 2017 (Standortauswahlgesetz, or StandAG), areas in which Quaternary volcanism is either present or future volcanic activity is expected within the next 1 million years (m.y.) must be excluded from the site selection process. Although the locations of regions with Quaternary volcanism are reasonably well known in Germany, forecasting potential igneous activity at intraplate volcanic fields is challenging, as many processes and their interactions control the spatial distribution of volcanic centers. Here, a semi-quantitative, multi-criteria approach is proposed for a regional evaluation of the relative potential of future igneous activity in Germany. A variety of geoscientific indicators are used, including seismic anomalies in Earth’s mantle, gravity data, tectonic activity, sutures, ground motion, earthquakes, mantle degassing centers, and geochronological data of volcanic rocks. The indicators describe the sequence of processes from potential melt generation in Earth’s mantle, through ascent and accumulation of melt within the lithosphere, to eruption at Earth’s surface. In total, 15 out of 30 proposed geoscientific indicators are selected and quantified using 20 total assigned parameters. Defined threshold values are used to spatially delimit relevant parameter properties to focus on areas with higher potential of future magmatic activity. To consider uncertainties of parameters and their underlying processes, which are usually more spatially extensive below ground, buffer zones are defined in which values of relevance decrease with increasing distance from the initial lateral shape of a parameter. Normalized parameters are combined into an index, whose spatial value distribution is used to differentiate the relative potential of future igneous activity (within the next 1 m.y.). The sensitivity of the results is shown by varying the weighting factors for the relevant parameters in country-wide index maps. Thereby, profiles illustrate the distribution of the resulting index values and respective index fractions of various parameters. Different index maps for the relative potential of future igneous activity are presented and can be used for hazard assessments.

Professional Paper↗
USGS2024

A partially nonergodic ground-motion model for Fourier amplitude spectra for the San Francisco Bay area, California, USA

We develop a partially nonergodic ground-motion model (GMM) for Fourier amplitude spectra for the San Francisco Bay Area, California, USA, using the Bayless and Abrahamson (2019) GMM as a reference ergodic GMM and developing location-dependent adjustments to the predicted median and variance. We compile regional ground-motion data from moment magnitude (𝑀 w ) >3 earthquakes occurring during 2000–2022 for which magnitude information is available in the U.S. Geological Survey Comprehensive Catalog (Guy et al., 2015). The data set predominantly consists of records from 𝑀 w 3.5–4.5 earthquakes but includes three well-recorded 𝑀 w > 5 events. Ground-motion residuals are evaluated using the time-averaged shear-wave velocity in the top 30 m (𝑉 S30 ) from the California-specific map of Thompson et al. (2018) and basin-depth site parameters from the seismic velocity model of Aagaard and Hirakawa (2021). The 𝑉 S30 dependence and basin-depth scaling of the reference ergodic GMM of Bayless and Abrahamson (2019) are evaluated and modified with the updated data set. We compute maps of site adjustments using a varying-coefficient model that considers the spatial correlation structure and uncertainties at each observation location. The spatial covariance model is developed using ground-motion residuals that are standardized by the uncertainty model, which allows for consideration of the aleatory variability in developing the site adjustments. The covariance model is fit considering the means and standard deviations of the site terms at all locations. The use of partially nonergodic median adjustments results in modified variance components of the within-event variability. Due to the low number of large-magnitude earthquakes that control seismic hazard in the data set, we do not modify between-event variance; however, we present adjustments to site-to-site variability for use in partially nonergodic hazard assessments.

California↗
USGS2026

Indirect mineral import reliance and provenance

Mineral commodity supply chain analyses rely on international trade data reported by individual countries as quantities of a mineral commodity form imported from (or exported to) a partner. However, export quantities frequently exceed a country’s domestic production, or occur when no production data are reported, suggesting that the trade partner is merely an intermediary in a transshipment. These discrepancies can result in misleading conclusions regarding supply chain vulnerabilities and dependencies. We present a two-stage methodology to reconcile gaps between reported material sources and actual producers. First, we construct trade networks for specific mineral forms, treating production as a type of import to distinguish producing nations from entrepôts. By tracing flows through these networks, we attribute a target country’s imports to original producers via both direct (in a single trade link) and indirect (transferring through intermediaries) pathways. Second, these production-attributed flows are incorporated into multi-stage supply chains to determine the upstream provenance of feedstock for domestic refining and processing. This approach provides a more representative picture of trade reliance. For example, while the United States (U.S.) Geological Survey reports no imports of unwrought antimony metal from Russia in 2022 (U.S. Geological Survey (2025). Mineral Commodity Summaries 2025. 10.3133/mcs2025), our analysis reveals that over 16% of U.S. imports can be traced back to Russian mining through intermediate processing in countries such as China, India, and Vietnam. Additionally, our analysis of the aluminum supply chain shows that while the U.S. is reported as 52% net import reliant on aluminum materials in 2022, it is 100% reliant on foreign bauxite, 7% of which arrived indirectly. This unreported reliance, which is predominantly tied to bauxite mined in Brazil (43%) and Jamaica (28%), highlights our methods ability to capture the supply chain’s dependence on foreign feedstock that may be missing in single-stage trade data.

Mineral Economics↗
USGS2025

Igneous differentiation in arcs

This chapter assesses crystallization-differentiation of convergent-margin magmas both from the standpoints of natural rock suites and from laboratory phase-equilibrium experiments. Crystallization-differentiation of dry subalkaline and alkaline basalts are summarized at the beginning for reference and context. Experiments crystallizing arc basalts through andesites at middle to upper crustal pressures (100–400 MPa), saturated either with H 2 O or mixed H 2 O-CO 2 , produce liquid lines of descent similar to arc igneous suites, with higher f O 2 s giving results closer to calc-alkaline magma series. Early enrichment of melts in SiO 2 with little or no enrichment in FeO* (total Fe as FeO) comes about by the combined effects of H 2 O suppressing crystallization of plagioclase relative to olivine, shifting plagioclase to more calcic – therefore lower SiO 2 – compositions, and suppressing silicate minerals to greater extents than Fe-rich spinel. Also contributing to SiO 2 enrichment is crystallization near the liquidus of higher proportions of olivine relative to calcic clinopyroxene, compared with MORB, but it is unclear if this is brought about by H 2 O or elevated pressure. Exhumed arc terranes, however, generally fail to expose evidence for primitive liquids having reached and differentiated at middle to upper crustal depths, at least in any abundance. Instead, most differentiation in arcs appears to take place in the lower crust or near the crust-mantle transition, and although hydrous experiments at such pressures produce liquids broadly similar to arc suites in their SiO 2 enrichment, the experimental liquids differ in becoming peraluminous at early stages of differentiation while melts have basaltic, basaltic andesitic, or andesitic SiO 2 concentrations. Crystallization of abundant calcic clinopyroxene close to the liquidus causes the high-pressure experimental liquids to become peraluminous with only small increases in SiO 2 concentration. In contrast, natural arc volcanic and plutonic suites become gradually less metaluminous with increasing SiO 2 , crossing to peraluminous at dacitic to rhyolitic SiO 2 concentrations. Hybridism, encompassing magma-mixing, assimilation of old crust, and incorporation of near-solidus melts from only shortly older deep, antecedent arc intrusions may accompany natural crystallization-differentiation, preventing melts from attaining peraluminous compositions at low-SiO 2 concentrations. If so, then nearly all arc suites are to substantial extents composites, rather than preserving simple, progressive liquid lines of descent. Some comments and assessments are also made on the participation of garnet in arc differentiation and on the production of arc batholiths.

Book chapter↗
USGS2024

Updating the crustal fault model for the 2023 National Seismic Hazard Model for Alaska

We present the crustal fault model for Alaska, based on geologic observations, as a primary input for the 2023 revision of the U.S. Geological Survey National Seismic Hazard Model. We update the 2013 Alaska Quaternary fault and fold database to produce a simplified model of 105 fault sections and four fault zone polygons with basic geologic parameters including slip sense and rate. Significant updates include the following: (1) a slip rate of ∼53 mm/year on the Queen Charlotte Fault indicating it accommodates all of the plate boundary motion; (2) quantified slip rates on megathrust splay faults in the southern Prince William Sound region and near Kodiak Island; (3) improved details of structures in the Chugach-St. Elias orogen; (4) revision of the Castle Mountain Fault from right-lateral slip to a predominantly reverse fault; (5) improved Interior Alaska tectonic models that clarify relationships between the Denali, Totschunda, and thrust faults on both sides of the Alaska Range; (6) identified large earthquake sources in the eastern Brooks Range; and (7) omission of the Chatham Strait section of the Denali Fault. The fault model underscores that the collision of the Yakutat microplate is the dominant driver of active crustal faulting in most of Alaska.

Alaska, Yukon↗