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USGS2024

Exploring the uncertainty of machine learning models and geostatistical mapping of rare earth element potential in Indiana coals, USA

Rare earth elements and yttrium (REEs) have a wide range of applications in high- and low-carbon technologies. The strategic significance of REEs has grown due to their expanding applications in manufacturing industries and the constrained availability of these essential resources. This research explores the applicability of machine learning models and their uncertainty for assessing the REE potential in coal beds using various coal parameters as inputs. The work focuses on developing a predictive model based on geological variables, excluding considerations related to potential shifts in the commodities market. The Indiana Coal Quality Database was used as the data source. The promising and unpromising indicators derived from the outlook coefficient of samples from the database were used as the REE potential indicator for machine learning classification models. The filter-based approach with bootstrap was used to evaluate the importance of the coal parameters and their prediction uncertainties. Four machine learning methods (linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), a data balancing and augmentation approach (Synthetic Minority Over-sampling Technique), and bootstrap resampling techniques were used for building the models and evaluating their prediction capabilities under uncertainty. It was determined that the SVM bootstrap model with ten-times balanced and augmented data provided superior results compared with other models. Finally, stochastic spatial maps of the REE potential within the coal basin were generated using sequential indicator simulation. The spatial maps of the REE potential showed that a 29% area of the Indiana section of the Illinois coal basin has economic potential of REEs, with 90% confidence.

Indiana↗

USGS2024

The 3D Elevation Program—Supporting Ohio's economy

Introduction High-quality elevation data are proving to be a resource of great economic value in dealing with many important issues in Ohio. Current and accurate high-resolution elevation data support flood risk management, water quantity and quality assessment, precision farming, conservation planning, impervious-surface modeling, forest and other natural resources management, abandoned mine and geologic hazard assessment, karst mapping, and siting of wellhead pads for horizontal drilling. These data also support coastal zone management, traffic safety and preliminary engineering site-selection studies for transportation infrastructure, solar potential and other renewable energy planning, aviation safety, and identification of features of interest or concern such as archaeological sites and orphan oil and gas wells. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

Ohio↗

USGS2024

Beyond the wedge: Impact of tidal streams on salinization of groundwater in a coastal aquifer stressed by pumping and sea-level rise

Saltwater intrusion (SWI) is a well-studied phenomenon that threatens the freshwater supplies of coastal communities around the world. The development and advancement of numerical models has led to improved assessment of the risk of salinization. However, these studies often fail to include the impact of surface waters as potential sources of aquifer salinity and how they may impact SWI. Based on field-collected data, we developed a regional, variable-density groundwater model using SEAWAT for east Dover, Delaware. In this location, major users of groundwater from the surficial aquifer are the City of Dover and irrigation for agriculture. Our model includes salinized marshland and tidal streams, along with irrigation and municipal pumping wells. Model scenarios were run for 100 years and included changes in pumping rates and sea-level rise (SLR). We examined how these drivers of SWI affect the extent and location of salinization in the surficial aquifer by evaluating differences in chloride concentration near surface waters and the subsurface freshwater-saltwater interface. We found the presence of the marsh inverts the typical freshwater-saltwater wedge interface and that the edge of the interface did not migrate farther inland. Additionally, we found that tidal streams are the dominant pathways of SWI at our site with salinization from streams being exacerbated by SLR. Our results also show that spatial distribution of pumping affects both the magnitude and extent of salinization, with an increase in concentrated pumping leading to more intensive salinization than a more widely distributed increase of the same total pumping volume.

Delaware↗

USGS2024

Spatial differences in soil nutrients along a hydrographic gradient on floodplains in Dongting Lake

The spatial heterogeneity of soil nutrients is crucial for the water bird and whole floodplain wetland ecosystem in large lakes, and it is influenced by the dramatic water level changes and sedimentation progress in West Dongting Lake (WDL). Soil samples were collected at various soil depths along the Yuan River and Li River that feed into WDL. The concentrations of soil total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), and soil grain size were tested. The stoichiometric ratios of C, N, P, and the mean value of soil grain size ( Mz ) were calculated. The differences of soil TOC, TN, TP and the stoichiometric ratio at different sites and soil depths were compared. Linear regression was used to explore the relationships of Mz and nutrient concentrations, and relationships between TOC, TN, and TP. Redundancy analysis was used to explore the relationship between soil nutrients, heavy metal concentrations, and plant community diversity. The results showed that the distributions of soil TOC, TN, and TP concentrations differed across regions in west Dongting Lake along the Yuan and Li Rivers. Total organic carbon concentration differed at different sedimentation depths. Soil grain size showed negative effect with soil TOC, TN, and TP concentrations in this region. Plant community diversity correlated positively with soil TOC and negatively with Hg. West Dongting Lake was N limited despite the high wet deposition of N. It could potentially be attributed to the insufficient presence of aerobic environments for microbes during intermittent flooding of the floodplain, coupled with feeble mineralization. This study can provide valuable insights for the conservation of water bird habitats and wetland ecosystems.

West Dongting Lake↗

USGS2025

Analysis of the potential effects of Uzbekistan’s mineral endowment on the critical mineral supply of tungsten

Tungsten appears on the 2018 and 2022 U.S. Geological Survey critical mineral lists in part because of a very high global production concentration in China, which produces almost 83 percent of the world’s mined tungsten. Using known parameters and values from other tungsten mining operations, we created hypothetical scenarios in which three tungsten deposits in Uzbekistan are considered for development. Our results show that all three deposits are likely to be economically viable to develop under 2024 market conditions. If the three studied tungsten deposits were put into production, Uzbekistan could become the third-leading tungsten-producing country in the world and increase world output of tungsten by 2.7 percent. Putting these tungsten deposits in Uzbekistan into production could slightly reduce the tungsten global market concentration, therefore reducing the supply disruption potential for tungsten.

Open-File Report↗

USGS2026

Roadway runoff induced acute mortality in juvenile coho salmon during spring storm events

Extensive mortalities of adult coho salmon ( Oncorhynchus kisutch ), often called “Urban Runoff Mortality Syndrome” (URMS), have been documented during the fall in creeks where water quality has been degraded by roadway runoff. The primary cause of mortality is 6PPD-quinone (6PPDQ; N -(1,3-dimethylbutyl)- N ′-phenyl- p -phenylenediamine quinone)–an ozone transformation product that forms on all vehicle tires. Laboratory studies have shown that juvenile coho salmon are highly sensitive to 6PPDQ exposure. Unlike adults, juveniles reside in impacted watersheds year-round, including during the spring when 6PPDQ concentrations can frequently exceed lethal thresholds during storms. To assess the potential incidence of URMS in springtime rearing habitats for juvenile coho salmon, we conducted a paired water quality and toxicology study at Miller Creek, a runoff-impacted watershed in Normandy Park, WA, USA. Using a small field facility, three naïve groups of juvenile coho salmon ( N = 720) were exposed to either creek water or groundwater ( N = 120 per treatment per storm), across three spring storms while comparing water quality and mortality end points. In creek water during exposures, peak 6PPDQ concentrations reached 73–110 ng/L, exceeding reported median lethal concentrations (LC 50 ) for coho salmon. Over each 24–73 h storm exposure period, ∼80% of Miller Creek-exposed juvenile salmon died. No mortality occurred among control fish exposed to groundwater. These results indicate previously unidentified mortality risks for juvenile life stages of coho salmon during spring storms, suggesting substantial and year-round water quality impediments to coho salmon health and recovery across roadway runoff-impacted spawning, rearing, and migratory habitats.

Washington↗

USGS2025

Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km 2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.

California↗

USGS2026

Patterns and drivers of cliff erosion in Big Sur, California, USA using repeat photogrammetry, 2017–2023

Seacliff erosion in steep terrain poses major risks to transportation and critical infrastructure. In Big Sur, California, USA, seacliff erosion threatens the sustainability of the central coast stretch of California State Route 1, a transportation corridor that is critical to the region's economy. Published cliff retreat rates for the region range from 1 to 40 cm yr −1 , highlighting that high-resolution, process-based studies could enhance understanding of the causes of spatial and temporal variability. We quantified cliff erosion and investigated its drivers along ∼13 km of the Big Sur coastline at week–month timescales during the late fall to early spring wet seasons between January 2017 and June 2023 by analyzing 3D point clouds developed from aerial imagery using four-dimensional structure-from-motion (4D SfM) photogrammetry techniques. We calculated cliff face retreat rates of 2.23 ± 3.06 cm yr −1 (mean ±1 σ ), an order of magnitude lower than long-term estimated rates for the region (which included large deep-seated landslides), but in line with short-term rates reported across California. Change detection imagery comparison, cliff profiles through time, and statistical analysis reveal a cyclical cliff evolution process in which erosion by wave action at the cliff base destabilizes the cliff and primes it for subsequent failure during precipitation events. Although more erosion by volume could be attributed to precipitation-induced increases in soil moisture (784 m 3 km −1 yr −1 ) compared with erosion attributed to wave power (282 m 3 km −1 yr −1 ), our observations underscore the coupled nature of these processes in driving cliff evolution, consistent with established theory and observations.

California↗

USGS2025

Bayesian mapping of regionally grouped, sparse, univariate earth science data

Some earth science data are naturally grouped by region, and it is often desirable to map these data by region. However, if there are only a few samples within each region, then the map should be smoothed in an appropriate way to mitigate the problems that arise from having only a few samples. A smoothing algorithm based on a Bayesian hierarchical model is developed and presented in this report. This algorithm has several features that make it especially suitable for mapping earth science data: it can account for measurements that are censored, it can process multiple datasets with different measurement errors and different censoring thresholds, and it can calculate the uncertainty in any statistic that is mapped. The algorithm is demonstrated by mapping gold concentrations that are measured in streambed sediments in the Taylor Mountains quadrangle in southwestern Alaska.

Alaska↗

USGS2024

Remote sensing evapotranspiration in ensemble-based framework to enhance cascade routing and re-infiltration concept in integrated hydrological model applied to support decision making

Integrated hydrological models (IHMs) help characterize the complexity of surface–groundwater interactions. The cascade routing and re-infiltration (CRR) concept, recently applied to a MODFLOW 6 IHM, improved conceptualization and simulation of overland flow processes. The CRR controls the transfer of rejected infiltration and groundwater exfiltration from upslope areas to adjacent downslope areas where that water can be evaporated, re-infiltrated back to subsurface, or discharged to streams as direct runoff. The partitioning between these three components is controlled by uncertain parameters that must be estimated. Thus, by quantifying and reducing those uncertainties, next to uncertainties of the other model parameters (e.g. hydraulic and storage parameters), the reliability of the CRR is improved and the IHM is better suited for decision support modelling, the two key objectives of this work. To this end, the remotely sensed MODIS-ET product was incorporated into the calibration process for complementing traditional hydraulic head and streamflow observations. A total of approximately 150,000 observations guided the calibration of a 13-year MODFLOW 6 IHM simulation of the Sardon catchment (Spain) with daily stress periods. The model input uncertainty was represented by grid-cell-scale parameterization, yielding approximately 500,000 unknown input parameters to be conditioned. The calibration was carried out through an iterative ensemble smoother. Incorporating the MODIS-ET data improved the CRR implementation, and reduced uncertainties associated with other model parameters. Additionally, it significantly reduced the uncertainty associated with net recharge, a critical flux for water management that cannot be directly measured and rather is commonly estimated by IHM simulations.

Sardon catchment↗

USGS2025

To heal or not to heal?: 2. The moment-recurrence time behavior of repeating earthquakes in the 2011 Prague, Oklahoma aftershock sequence is consistent with laboratory healing rates

The timing and failure conditions of an earthquake are governed by the interplay between fault reloading and restrengthening. The moment-recurrence time behavior of repeating earthquakes can give observational estimates of fault healing rates; however, it is difficult to link these observed healing rates to laboratory studies of frictional healing in part because of uncertainty in lithology. Here, we study the 2011 Prague earthquake sequence, which includes repeating earthquakes in the Arbuckle group and the granitic basement, and compare them to laboratory experiments on samples of the Arbuckle and Troy granite (representative of the basement rock) (Okamoto et al., 2025, https://doi.org/10.1029/2024JB030573 ). We find three spatially distinct groups of repeating earthquakes with different moment-recurrence behavior: (a) constant moment-recurrence time in the Arbuckle group, (b) scattered moment-recurrence time at the intersection of the foreshock-mainshock fault in the granitic basement, and (c) moment-predictable behavior outside of the foreshock-mainshock fault intersection also in the granitic basement. Our observation of stagnant healing for repeating sequences in the Arbuckle group is consistent with laboratory observations of low healing rates for moderately high pore fluid pressures in Arbuckle samples. For the moment-predictable group, the source radius that is required in order to match healing rates is consistent with source radius estimations when taking into account reasonable attenuation of the P- pulse width. Overall, we observe diverse healing behaviors in the seismic families that are consistent with laboratory healing rates, providing seismic evidence that contact-scale frictional mechanisms are relevant to large-scale earthquake dynamics.

Oklahoma↗

USGS2025

Megafaunal community structure on ferromanganese and phosphorite hardgrounds in the Southern California Borderland

The Southern California Borderland (SCB) is a topographically complex region on the active continental margin that hosts varied hardground habitats, including ferromanganese (FeMn) crusts and phosphorites, marine minerals being considered for resource extraction. The SCB is influenced by seasonal upwelling and terrestrial inputs, and has a well-defined oxygen-minimum zone (OMZ). We analyzed megafaunal community composition, density, and diversity across 41 ROV video transects at 10 SCB hard substrate sites spanning depth (378–2765 m), temperature (1.79–7.96 °C), and oxygen (3.91–105.67 μM) gradients, with varying rock types (FeMn crusts, phosphorites, other rocks). We counted 32,426 individuals representing 146 unique taxa over a total area of 21,935 m 2 . Echinodermata contributed 48 % of the total abundance; Cnidaria 24 %; Porifera 13 %; Annelida 6 %; Arthropoda 6 %; Chordata 2 %; Mollusca, Foraminifera, and Hemichordata <1 %. Megafauna communities showed clear heterogeneity, with density, diversity, and community composition varying among transects within sites and across sites. Rock type recorded no influence on megafaunal density, but habitats that included FeMn crusts had higher diversity and distinct taxonomic composition. Location, depth, and oxygen were the most important variables explaining variance among megafaunal communities, with distinct communities formed at deeper depths and within the OMZ. This study provides baseline information about the megafauna inhabiting SCB hardgrounds, particularly those associated with minerals considered for their resource potential. It extends existing documentation of such communities to greater depths, reveals increased representation of sponges and corals below 1000 m, and highlights the variability within and among deep-sea megafaunal communities, offering context for informed policy decisions.

Southern California borderland↗

USGS2025

Groundwater response to managed aquifer recharge at the Southeast Houghton Artificial Recharge Project in Tucson, Arizona

Managed aquifer recharge is a widespread practice for storing water in the subsurface as groundwater. At a managed aquifer recharge facility in southern Arizona, groundwater-level and repeat microgravity data were collected to monitor aquifer response. These data were used to inform parameter identification for an unsaturated-zone flow model used to simulate the recharge process. The facility, the Southeast Houghton Artificial Recharge Project (SHARP), consists of 3 surface basins (about 27,600 square meters [6.8 acres] total surface area) where recycled water is distributed in recharge cycles lasting several months, with dry periods in between. During the study period, December 2020–December 2022, Tucson Water (the City of Tucson’s water utility) reported 6.56×10 6 cubic meters of water (5,320 acre-feet) recharged. Monitoring included groundwater-level observations at 3 monitoring wells and repeat microgravity measurements at as many as 22 locations (some stations were destroyed between surveys). Six gravity surveys were carried out using absolute- and relative-gravity meters. Large gravity increases, more than 250 microgals, were observed during the first repeat survey, 3.5 months after the start of recharge, but only in the immediate vicinity of the recharge basins. Data show that water moved downward to the water table, and storage changes in the unsaturated zone away from the facility were likely minimal. Gravity decreased at stations more than 1 kilometer from the facility, consistent with regional groundwater-level changes. Groundwater-level increases in wells adjacent to the recharge basins began 2 months after the second repeat gravity survey, and 5.5 months after recharge began. Unsaturated-zone flow modeling was carried out using software that simulates water movement and parameter estimation. Model calibration was carried out by minimizing an objective function calculated from the differences between simulated and observed groundwater levels, and between simulated and observed repeat microgravity data. Including repeat microgravity data in the objective function reduced the uncertainty in estimated parameter values for saturated hydraulic conductivity and saturated water content. Modeling indicated that the unsaturated zone between the recharge basins and the water table does not become saturated even after 685 days of simulated infiltration. This gradual wetting may account for increasing infiltration rates over time, as hydraulic conductivity increases with increasing water content. Unsaturated-zone water content decreased rapidly between recharge cycles. Model-simulated groundwater mounding extended about 1 kilometer from the center of SHARP after the 685-day period following the onset of recharge.

Arizona↗

USGS2022

U-Pb scheelite ages of tungsten and antimony mineralization in the Stibnite-Yellow Pine district, central Idaho

The Stibnite-Yellow Pine district contains the largest antimony resource in the United States, as well as significant gold, and is a historic producer of tungsten. Application of in situ laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) direct dating of scheelite from two Au-Sb-W ore deposits, Yellow Pine and Hangar Flats, yielded an older group of U-Pb ages in the range of 60.0 ± 2.8 to 57.0 ± 1.1 Ma and a younger U-Pb age for scheelite intergrown with stibnite of 47.4 ± 1.1 Ma. These in situ analyses were calibrated by isotope dilution-thermal ionization mass spectrometry (ID-TIMS) U-Pb lower intercept ages of two coarsely crystalline scheelite samples that yielded ages of 57.52 ± 0.22 and 56.62 ± 0.16 Ma. Scheelite of the latter age is of sufficient quality to serve as a primary reference material for LA-ICP-MS scheelite U-Pb geochronology. The group of older U-Pb scheelite ages agrees with 40 Ar/ 39 Ar ages of 56.9 ± 1.2 to 56.38 ± 0.54 Ma on adularia from Yellow Pine and Hangar Flats, whereas the younger U-Pb scheelite age is similar to an 40 Ar/ 39 Ar age of 46.00 ± 0.40 Ma on adularia from an epithermal gold-silver deposit in the adjacent Thunder Mountain caldera. Our results indicate that the main stage of tungsten mineralization occurred at ca. 57 Ma, whereas the main stage of antimony mineralization occurred at ca. 47 Ma—thereby providing first-time age constraints for antimony and tungsten mineralization in the Stibnite-Yellow Pine district.

Idaho↗

USGS2026

Assessment of extreme subsurface hydrologic conditions captured during atmospheric river storms in the San Francisco Bay area (California, USA) with applications to shallow landslide early warning

An increase in soil pore water pressure is the typical trigger for the majority of landslides caused by rainfall. Atmospheric river storms, common to the west coast of North America during the winter season, can deliver landslide triggering rainfall resulting in severe impacts to coastal communities. Using a network of hydrologic monitoring stations situated within landslide-prone terrain in the San Francisco Bay area of California (USA), we assess the meteorologic conditions and resulting hydrologic and landslide response resulting from eight consecutive storm events that caused thousands of shallow landslides during the winter of 2022–2023. We find disparate hydrological responses and resultant degrees of observed landsliding ranging from < 1 landslide/km2 to 18 landslides/km2 at the monitoring sites that reflect the interplay and differences between rainfall delivery, subsurface hydrological characteristics, and geotechnical properties at each site. Antecedent soil moisture from both early season rainfall and the first storm in the sequence played a critical role in setting up some hillslopes for failure. Subsequent storms then generated elevated pore water pressures for several hours with associated landsliding. However, we find that the occurrence of widespread landsliding required not only sufficient pore water pressure magnitude in susceptible hillslopes, but also full and prolonged effective soil saturation throughout hillslope profiles. Landslides may still occur at lower values and durations of effective saturation but are likely to be less extensive regionally. We present these findings within the context of research directions and improvements to landslide early warning systems first suggested by researchers 40 years ago.

California↗

USGS2024

Agricultural tile drains increase the susceptibility of streams to longer and more intense streamflow droughts

Streamflow droughts are receiving increased attention worldwide due to their impact on the environment and economy. One region of concern is the Midwestern United States, whose agricultural productivity depends on subsurface pipes known as tile drains to improve trafficability and soil conditions for crop growth. Tile drains accomplish this by rapidly transporting surplus soil moisture and shallow groundwater from fields, resulting in reduced watershed storage. However, no work has previously examined the connection between tile drainage and streamflow drought. Here, we pose the question: does the extent of watershed-level tile drainage lead to an increased susceptibly and magnitude of streamflow droughts? To answer this, we use daily streamflow data for 122 watersheds throughout the Midwestern United States to quantify streamflow drought duration, frequency, and intensity. Using spatial multiple regression models, we find that agricultural tile drainage generates statistically significant ( p < 0.05) increases in streamflow drought duration and intensity while significantly reducing drought frequency. The magnitude of the effect of tile drainage on streamflow drought characteristics is similar to that of water table depth and precipitation seasonality, both of which are known to influence streamflow droughts. Furthermore, projected changes in regional precipitation characteristics will likely drive the installation of additional tile drainage. We find that for each 10% increase in tile-drained watershed area, streamflow drought duration and intensity increase by 0.03 d and 12%, respectively, while frequency decreases by 0.10 events/year. Such increases in tile drainage may lead to more severe streamflow droughts and have a detrimental effect on the socio-environmental usage of streams throughout the Midwest.

Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio↗

USGS2026

Competition between invasive round goby and native slimy sculpin in a coldwater laboratory setting

Slimy sculpin ( Cottus cognatus ) abundance has substantially declined in many regions of the Laurentian Great Lakes coincident with the arrival and range expansions of invasive round goby ( Neogobius melanostomus ). Previous studies have shown that round goby can outcompete native mottled sculpin ( C. bairdii ) for shelter and negatively affect native spoonhead sculpin ( C. ricei ) growth, but direct behavioral interactions of slimy sculpin and round goby have not been observed. We conducted a laboratory experiment in which a pair of slimy sculpin were placed in an aquarium that contained an artificial spawning shelter and one round goby (treatment, n = 10) or no other fish (control, n = 10) for at least 30 days at temperatures reflective of their potential offshore overlap. We measured slimy sculpin egg deposition, aggressive interactions, artificial shelter occupancy, growth, and survival. Slimy sculpin were less likely to occupy shelters and experienced increased intraspecific competition when a round goby was present. In contrast, round goby rarely chased slimy sculpin, slimy sculpin were more likely to chase round goby, and slimy sculpin growth, egg deposition, and survival were not significantly different between groups. Differences in density between treatments and the relative size of fish could confound our observations. However, the dominant slimy sculpin occupied the shelter at similar rates between experimental groups if the round goby was smaller. Our results indicate that larger round goby overlapping with slimy sculpin could affect slimy sculpin shelter and space use, but the Great Lakes environment may create more stress for slimy sculpin than we observed.

Lake Michigan, Lake Superior↗

USGS2025

40Ar/39Ar geochronology supporting mineral resources research at USGS Denver

The 40 Ar/ 39 Ar geochronology method is used to date potassium-bearing rocks and minerals, based on the decay of 40 K to 40 Ar, which provides important temporal constraints for geological events. The USGS Denver Argon Geochronology Laboratory dates samples from a variety of projects, mainly in the USGS Mineral Resource Program and the National Cooperative Geologic Mapping Program, facilitating in-depth research into the timing of geological events and processes. Recent applications of this method include geochronological studies in the Yellow Pine district, revealing mineralization ages that range from 51 to 70 Ma, and investigations into the Bear Lodge alkaline complex, providing insights into the timing of REE carbonatite formation. Additionally, studies at Alunite Ridge highlight the method's potential for understanding concealed mineral deposits. Ongoing projects continue to leverage 40 Ar/ 39 Ar data to enhance understanding of geologic frameworks and mineral resources, underpinning its importance in modern geochronology and mineral resource assessment.

Conference Paper↗