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USGS2025

Estimation of dynamic geologic CO2 storage resources in the Illinois Basin, including effects of brine extraction, anisotropy, and hydrogeologic heterogeneity

Since the vast majority of carbon dioxide (CO 2 ) storage resources in the United States are in deep saline aquifers, optimizing the use of these saline storage resources could be crucial for efficient development of geologic CO 2 storage (GCS) resources and basin- or larger-scale deployment of GCS in the country. Maximum CO 2 injection rates can be enhanced by extracting brine from the CO 2 storage unit. However, disposal of the extracted brine is both a technological and economic challenge. The lowest-cost option would likely be reinjection of the extracted brine into another formation above or below the CO 2 storage unit. Therefore, it is important to estimate brine injectivity as it will constrain the potential to increase CO 2 injectivity at an injection site that has access to multiple geologic storage units where either CO 2 or brine can be injected. Using a simulation-optimization framework, coupled with a non-isothermal, multiphase CO 2 -water-salt equation-of-state module, we developed a computationally efficient method for evaluating optimization of simultaneous CO 2 injection, brine extraction, and brine (re)injection at hypothetical injection sites deployed across a geologic basin. The Illinois basin is ideal for testing our methodology because it contains multiple geologic storage units with seals in between them to isolate injection of CO 2 in one unit from interfering with the injection of either brine or CO 2 in another unit above or below it. In addition, we investigated the relative effects of variation in key geologic parameters as well as two reservoir structures (hydrogeologic heterogeneity/anisotropy and homogeneity/isotropy) on CO 2 injectivities and enhancement of CO 2 injectivity through extracting brine. Results suggest that permeability, depth, and especially thickness of the storage unit could be the most influential parameters determining CO 2 injectivity. They also suggest that only injecting CO 2 into the storage unit with the greatest injectivity, enhancing that unit’s injectivity by extracting brine, and disposing of the produced brine in other suitable units could maximize total CO 2 injectivity in limited regions of the basin. At the majority of simulated injection sites, however, we found that injecting CO 2 into all of the accessible and suitable storage units was more likely to maximize the CO 2 storage resource.

Illinois, Kentucky, Indiana↗

USGS2024

Mitigating climate change by abating coal mine methane: A critical review of status and opportunities

Methane has a short atmospheric lifetime compared to carbon dioxide (CO 2 ), ∼decade versus ∼centuries, but it has a much higher global warming potential (GWP), highlighting how reducing methane emissions can slow the rate of climate change. When considering the contribution of greenhouse gas (GHG) emissions to current global warming (2010–2019) relative to the industrial revolution (1850–1900) levels, methane contributes 0.5 °C or ∼ a third of the total. The most recent post-2023 global estimates of methane emissions by bottom-up (BU) and top-down (TD) approaches for the coal mining sector are in the range of ∼41 ± 3 Tg yr −1 and 33 ± 5 Tg yr −1 , respectively. This divergence, notwithstanding overlapping confidence intervals, is a result of differences between applied TD global inversion models and BU emission inventories. Further research can help to better refine emissions from the various contributing coal mine methane (CMM) emissions sources. The coal mining sector accounts for over 10 % of global anthropogenic methane emissions. The contribution of CMM emissions to the global budget have increased since 2000, although upward and downward regional trends have been observed.

International Journal of Coal Geology↗

USGS2025

Simulation of the impacts of projected climate change on groundwater resources in the urban, semiarid Yucaipa Valley watershed, southern California using an integrated hydrologic model

Managing water resources in semiarid watersheds is challenging due to limited supply and uncertain future climate conditions. This paper examines the impact of future climate changes on an urban watershed in southern California using an integrated hydrologic model. GSFLOW modeling software is used to simulate the nonlinear relationships between climate trends and precipitation partitioning into ET, runoff, and subsurface storage. Four global circulation models (GCMs), each with two greenhouse-gas scenarios, RCP45 and RCP85 are used to project future climate conditions. GCMs include the CanESM2, CNRM-CM5, HadGEM2-ES, and MIROC5 models. The model's simulated hydrologic conditions are compared with historical data to assess changes in water budgets and groundwater supply. Results indicate decreased groundwater storage in most scenarios due to increased natural evapotranspiration, vegetation consumptive use, and streamflow out of the watershed. Only scenarios with substantially increased future precipitation show increased groundwater storage. The study also highlights increased future aridity despite the rise in precipitation and large precipitation events forecast by GCMs, which increase the risk of urban floods and decrease stream leakage and water available to vegetation.

California↗

USGS2025

Four-band image mosaic of the Colorado River Corridor downstream of Glen Canyon Dam in Arizona, derived from the May 2021 airborne image acquisition

In May 2021, the U.S. Geological Survey’s Grand Canyon Monitoring and Research Center acquired airborne multispectral high-resolution data for the Colorado River in the Grand Canyon, Arizona. The image data, which consist of four spectral bands (red, band 1; green, band 2; blue, band 3; and near infrared, band 4) with a ground resolution of 20 centimeters, are available as 16-bit unsigned-integer GeoTIFF files in Sankey and others (2024) (available online at https://doi.org/10.5066/P9BBGN6G ). The image files are projected in the State Plane Coordinate System, using the central Arizona zone (202) with the North American Datum of 1983 National Adjustment of 2011. The assessed spatial accuracy for these data is based on 47 ground-control points that were independent from the ground-control points used by the contractor for aerotriangulation and is reported at the 95-percent confidence level as 0.514 meter (m) and a root mean square error of 0.297 m. The intended uses of this dataset are primarily in support of scientific research and monitoring applications. Examples of these applications include high-resolution spatial and temporal change detection of the river channel, geomorphic landforms, riparian vegetation, and backwater and nearshore habitat, as well as other ecosystem-wide mapping. These imagery data also serve as reference material for field science mission planning, as base data for field data collection including community science activities, and as a highly detailed guide for technical boat operation during science activities such as reconnaissance for nighttime missions and navigating rapids during low flows.

Arizona, Nevada, Utah↗

USGS2024

Evaluation of an open earthquake early warning system in Mexico, and laboratory tests of their sensors

Microelectromechanical system (MEMS) accelerometers are useful for seismological and engineering applications because of their ability to record unsaturated large seismic signals. Recent advances in MEMS technologies enable the design of instruments with improved capabilities that also allow the recording of small signals. As a result, MEMS can be useful across a broad dynamic range and for both major earthquakes and smaller magnitude events. Here, we analyze the performance of a MEMS‐based accelerometer with the capability of real‐time data transmission. We discuss the self‐noise level, dynamic range, and sensitivity, along with the timing precision and data transmission latency, and compare these parameters to other MEMS‐based instruments. We also summarize observations from a regional network deployed in southern Mexico over a period of 3+ yr for the purpose of earthquake early warning. In addition to the sensor evaluation, we present a large, openly available data set of strong motion data from the Mexican network that comprises continuous ground‐motion records from 24 accelerometers in the period between 2017 and 2022.

Seismological Research Letters↗

USGS2026

On-demand global Landsat evapotranspiration product: Development, evaluation, and dissemination

Global actual evapotranspiration (ET) is one of the essential climate variables needed to understand and manage the relationships among food, energy, and water resources. The U.S. Geological Survey Earth Resources Observation and Science (EROS) Center launched a provisional ET product in 2020, offering on-demand, field-scale global coverage derived from Landsat data through the EROS Science Processing Architecture (ESPA) platform. The ESPA interface provides ET data for cloud-free Landsat overpasses starting in 1982 with Landsat 4 through the current Landsat 9. The ET data are delivered as a Provisional Level-3 Science product created using the Operational Simplified Surface Energy Balance (SSEBop) model. Landsat surface temperature and reference ET are the main model drivers along with vegetation index and net radiation for model parameterization. A large volume of Landsat-based ET orders (e.g., over 1,200,000 images from June 2020 through December 2025) around the world indicate increasing awareness and application of the ET data. The ESPA platform enables land and water resource managers and researchers to access a first-order ET product without requiring advanced knowledge of remote sensing technology or evapotranspiration modeling. We present the methodology and workflow of the on-demand Landsat ET product and its performance evaluations over diverse hydro-climatic settings. The product can help estimate field-scale consumptive water use and thus quickly and consistently assess historical water use, allocation, and budget to inform water management under changing environments. Future ET data aggregated to monthly and seasonal time scales are expected to enhance integration with decision-making tools and procedures.

Remote Sensing of Environment↗

USGS2025

Constraining landslide frequency across the United States to inform county-level risk reduction

Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often , which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal Emergency Management Agency's National Risk Index (NRI) requires landslide frequency estimates to inform expected annual loss estimates. We present county-level landslide frequency (landslides per area per year) estimates for the 50 US states. We applied Bayesian negative binomial regression to estimate both the expected (average) reported landslide frequency and full distribution of annual landslide counts for each county. We compared a suite of models that used combinations of landslide-susceptible area, probability of potentially triggering earthquakes, frequency of potentially triggering precipitation, and ecological region as predictors. We trained our models with landslide inventory data from counties with the most comprehensive records available nationwide and used zero-inflated negative binomial distributions as an incompleteness model to correct for temporal reporting gaps. We selected a preferred frequency model to inform the NRI based on information criteria and physically plausible parameter estimates. The model showed that average annual reported landslide frequencies vary by 5 orders of magnitude across US counties, ranging from 0.002 (0.00015–0.05) landslides 1000 km −2 yr −1 in Kusilvak Census Area, Alaska, to 29 (19–46) landslides 1000 km −2 yr −1 in Lake County, California, reflecting the country's strong variations in landslide susceptibility, earthquake probability, and other factors for which ecological region serves as a proxy. Counties with estimated frequencies in the top 20 % of all counties are predominately along the West Coast of the continental United States, in mountainous regions of the Pacific Northwest and Intermountain West, in locally steep or earthquake-prone regions of the Midwest and Southeast, along the Appalachians, in southern and southeastern Alaska, and on some Hawaiian islands. By examining the number of landslides predicted in 99th percentile years for each county, we identified that 26 % of US counties likely have potential for widespread landsliding with more than 10 landslides 1000 km −2 yr −1 , even when such large events have not been reported in the training data for that county. Overall, our results better represent the range of possible landslide frequencies and spatial variations than previous national-scale estimates reported in the NRI, and our approach can inform other risk-reduction and loss-mitigation efforts across the United States and globally.

Natural Hazards and Earth System Sciences↗

USGS2026

Bedrock geologic map of the Eagle Lake quadrangle, Essex County, New York

The bedrock geology of the 7.5-minute Eagle Lake quadrangle, Essex County, New York, consists of deformed and metamorphosed Mesoproterozoic gneisses of the Adirondack Highlands unconformably overlain by weakly deformed lower Paleozoic sedimentary rocks of the Champlain Valley. The Mesoproterozoic rocks occur on the eastern edge of the Adirondack Highlands and represent an extension of the Grenville Province of Laurentia. Granulite facies Mesoproterozoic paragneiss, marble, and amphibolite hosted the emplacement of an anorthosite-mangerite-charnockite-granite (AMCG) suite, now exposed mostly as orthogneiss, at approximately 1.18–1.15 giga-annum (Ga, billion years before present). The earliest of four phases of deformation (D1) predated AMCG magmatism and is characterized by gneissosity, rarely preserved F1 isoclinal folds, and migmatite in the paragneiss host rocks. A sample of hornblende quartz syenite from the AMCG suite, collected from an abandoned railroad cut on Old Furnace Road, yielded a U-Pb zircon age of 1,149±10 million years before present. D2 deformation produced a composite penetrative gneissosity, migmatite, and isoclinal F2 folds. Towards the end of D2, felsic magmatism (including the regionally extensive Lyon Mountain Granite Gneiss, abbreviated “LMG”) spread by penetrative migration as semiconcordant alkali feldspar granite sheets subparallel to S2 into the previously deformed lithologies. The LMG crystallized at approximately 1.15 to 1.14 Ga and displays synkinematic F2 folds thus constraining the time of D2 deformation. Exhumation of the Marcy anorthosite began during D3 along a mylonitic extensional detachment, as a type of core complex. Protracted D3 produced F3 folds exhibited in regional domes and basins, such as the Hammondville antiform, reactivation of the S2 foliation, partial melting, metamorphism, metasomatism, iron ore remobilization, and intrusion of magnetite-bearing pegmatite both as layer-parallel sills and crosscutting dikes. D4 created NE- and NW-trending boudinage, local high-grade ductile shear zones, and crosscutting granitic pegmatite dikes. Kilometer (km)-scale lineaments readily observed in lidar data are Ediacaran mafic dikes and Phanerozoic brittle faults. Lower Paleozoic rocks are part of the Early Cambrian to Late Ordovician great American carbonate bank on the ancient margin of Laurentia. The Potsdam Sandstone preserves the Cambrian stratigraphy in outliers above the Great Unconformity. The Paleozoic rocks are weakly folded and block faulted. Parts of the quadrangle are covered by undifferentiated glacial deposits, but much of the quadrangle contains only a variably thick, veneer of unmapped glacial till over significant areas of exposed bedrock. The map also shows waste rock piles and locations of historical mining operations. This study was undertaken to improve our understanding of the bedrock geology in the Adirondack Highlands, establish a modern framework for 1:24,000-scale bedrock geologic mapping in the Adirondack Mountains, and provide a modern context for historical mines. This Scientific Investigations Map of the Eagle Lake 7.5-minute quadrangle consists of a map sheet, an explanatory pamphlet, and a geographic information system database that includes bedrock geologic units, faults, outcrops, and structural geologic information. The map sheet includes a bedrock geologic map, a correlation of map units, a description of map units, an explanation of map symbols, and two cross sections. The explanatory pamphlet includes a discussion of the geology.

New York↗

USGS2025

Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes

As the northern high-latitude permafrost zone experiences accelerated warming, permafrost has become vulnerable to widespread thaw. Simultaneously, wildfire activity across northern boreal forest and Arctic/subarctic tundra regions impacts permafrost stability through the combustion of insulating organic matter, vegetation, and post-fire changes in albedo. Efforts to synthesis the impacts of wildfire on permafrost are limited and are typically reliant on antecedent pre-fire conditions. To address this, we created the FireALT dataset by soliciting data contributions that included thaw depth measurements, site conditions, and fire event details with paired measurements at environmentally comparable burned and unburned sites. The solicitation resulted in 52 466 thaw depth measurements from 18 contributors across North America and Russia. Because thaw depths were taken at various times throughout the thawing season, we also estimated end-of-season active layer thickness (ALT) for each measurement using a modified version of the Stefan equation. Here, we describe our methods for collecting and quality-checking the data, estimating ALT, the data structure, strengths and limitations, and future research opportunities. The final dataset includes 48 669 ALT estimates with 32 attributes across 9446 plots and 157 burned–unburned pairs spanning Canada, Russia, and the United States. The data span fire events from 1900 to 2022 with measurements collected from 2001 to 2023. The time since fire ranges from 0 to 114 years. The FireALT dataset addresses a key challenge: the ability to assess impacts of wildfire on ALT when measurements are taken at various times throughout the thaw season depending on the time of field campaigns (typically June through August) by estimating ALT at the end-of-season maximum. This dataset can be used to address understudied research areas, particularly algorithm development, calibration, and validation for evolving process-based models as well as extrapolating across space and time, which could elucidate permafrost–wildfire interactions under accelerated warming across the high-northern-latitude permafrost zone. The FireALT dataset is available through the Arctic Data Center ( https://doi.org/10.18739/A2RN3092P , Talucci et al., 2024).

Arctic↗

USGS2025

Performance analysis of oil recovery and CO2 retention in a greenfield residual oil zone: CO2-EOR in Tall Cotton Field (Permian Basin, West Texas, USA)

Residual oil zones (ROZs) can offer significant oil resources via enhanced oil recovery (EOR) as well as subsurface carbon dioxide (CO 2 ) retention during injection. If injected CO 2 is anthropogenic, the ROZs can offer a substantial geologic storage potential. The ROZs below the oil/water contact (OWC) of main pay zones (MPZ) in conventional reservoirs or brownfields, are more commonly developed for CO 2 injection and oil production and reported in the literature. However, CO 2 -EOR in greenfield ROZs, reservoirs without a MPZ present, have rarely been developed for CO 2 -EOR operation. The Tall Cotton Field of West Texas, Permian Basin, which started production in 2015 (Phase 1) and expanded in 2017 (Phase 2) from the San Andres Limestone, is one of the first examples of greenfield ROZs developed for EOR by injecting CO 2 . This paper analyses EOR and CO 2 retention performance of Tall Cotton Field using allocated injection and production data from inverted 5-spot well patterns of Phase-1 and -2 developments. Production and injection data allocated to each of the 28 identified patterns (nine 20-acre patterns for Phase-1, three 20-acre and sixteen 10-acre patterns for Phase-2) were analyzed for historical and forecasted oil recovery using ratio-trend decline analysis, and for CO 2 retention performance of the patterns. The allocated data were further used to calculate injected reservoir pore volume and void replacement ratios (VRR) for the analysis period. Quantitative results indicated that oil recovery factors of the 5-spot patterns varied between 4–10 %, and 5–30 % between the end of injection and the forecast periods, respectively. Storage of CO 2 , on the other hand, increased to a mean value of ∼7130 MMscf per pattern in Phase-1 and to a mean storage of 3700 MMscf per pattern in Phase-2 until the end of injection, followed by a decline after the end of injection and into the forecast period. Resulting CO 2 utilization factors ∼6–50 Mscf/bbl were estimated at the end of injection. Overall, presented results suggested that developing greenfield ROZs for CO 2 -EOR can be as promising as brownfield ROZs and mature MPZs for EOR and underground storage of injected CO 2 . For Tall Cotton Field, results suggest that Phase-2 patterns generally outperformed Phase-1 for oil recovery factors, while Phase-1 performed better in CO 2 retention performance metrics. This is the first study in the literature that reports a detailed CO 2 -EOR performance analysis of a greenfield ROZ in the Permian Basin, which can potentially allow for comparison with MPZs and brownfield ROZs.

Texas↗

USGS2025

Special issue "Effects of surface geology on seismic motion (ESG): General state-of-research"

To understand and predict the behaviors of strong ground motions incurred during devastating earthquakes, studies focused on the “effects of surface geology (ESG) on seismic motions” (following tradition, herein contracted as: “ESG”) have progressed steadily in the last three decades. These ESG studies typically involve in situ measurements of the wavefield and commonly apply both analytical and computational approaches. Concurrently, improvements in ESG related research can be readily attributed to the proliferation of openly accessible strong motion data, as well as advances in computational power. Nevertheless, there remain significant shortfalls in our understanding of the epistemic and aleatory uncertainties associated with the ESG as demonstrated by phenomena from recent deadly earthquake-related site effects. Thus, investigations toward addressing these deficiencies should be underscored in future earthquake related disaster mitigation efforts. This special issue of Earth, Planets, and Space (EPS) is dedicated to the ongoing efforts by the International Association of Seismology and Physics of the Earth’s Interior (IASPEI) and the International Association of Earthquake Engineering (IAEE) that champion activities promoting studies related to the ESG.

Earth, Planets and Space↗

USGS2026

Production of mineral commodities and geospatial map of the mineral industries and related infrastructure of China

As part of the U.S. Geological Survey’s (USGS) mission to distribute global mineral information and analyze supply chains, this study provides a comprehensive review of the global significance of China’s mineral production and capacity in 2023. Of 77 mineral commodities in the USGS dataset, China produced 74 and was the world’s first-ranked producer for 39 of the 74. Compared to the high share of global mineral production, including up to 98 percent of global gallium production, the country’s share of global mineral reserves was relatively small, ranging from 20 percent (zinc ore) to 52 percent (tungsten ore). China’s imports of metal ores, slag, and ash accounted for 64 percent of global imports of such commodities by value. The country’s exports of base metals and articles of base metal accounted for 17 percent of the global exports. To help nongeographic information system users assess the spatial distribution of mineral mines, processing facilities, and ports for trades in China, this study created a geospatial (also called “georeferenced”) portable document format (GeoPDF) map. In addition, the GeoPDF contains mineral resource tracts (such as antimony, copper, potash, coal, and oil and gas), exploration sites, and energy infrastructure based on the preexisting USGS data.

Open-File Report↗

USGS2026

Precipitation-based flood-inundation maps for the East Fork Little Blue River and tributaries at Lee’s Summit, Missouri, 2024

The U.S. Geological Survey, in cooperation with the City of Lee’s Summit, Missouri, assessed flooding of the East Fork Little Blue River and tributaries for varying precipitation magnitudes and durations, varying antecedent runoff conditions, and projected climate-change conditions. The precipitation scenarios were used to develop a library of flood-inundation maps for a 2.95-mile reach of the East Fork Little Blue River and tributaries within the city. A two-dimensional U.S. Army Corps of Engineers Hydrologic Engineering Center–River Analysis System (HEC–RAS; ver. 6.5) rain-on-grid model was calibrated to selected runoff events representing a range of antecedent runoff conditions and hydrologic responses. Lowest adjacent grades for structures within the nearby study area were incorporated into the terrain, and depth grids and water-surface elevation grids were developed for the study area. Simulated velocities at selected bridge locations were also developed from the model. The model was calibrated using water-surface elevation data collected from water-level loggers (pressure transducers) and streamflow measurements and water-surface elevation measurements made at a reference point during runoff events. The calibrated HEC–RAS model was used to simulate streamflows from design rainfall events of 15-minute to 24-hour durations and ranging from a 100- to 0.1-percent annual exceedance probability (1-year to 1,000-year recurrence intervals). Flood-inundation maps were produced for depths at a reference location of 3 to 16 feet, or a depth exceeding the 0.1-percent annual exceedance probability interval precipitation. The results of each precipitation duration-frequency value were represented by a 1-foot-increment inundation map based on the generated peak streamflow from that rainfall event and the corresponding water-surface elevation at the East Fork Little Blue River reference location. Within the HEC–RAS model, 240 scenarios were developed from the design rainfall events with each of 3 antecedent conditions. Additional scenarios were created to simulate the effects of projected precipitation scenarios on the 100-year recurrence interval, 24-hour storm and the 100-year recurrence interval, 6-hour storm. All simulation results were assigned to a flood-inundation map condition based on the generated peak flow and corresponding water-surface elevation at the East Fork Little Blue River reference location. The flood-inundation maps are shown on a web mapping application made available to the public through the City of Lee’s Summit (hyperlink will be added when available). The flood-inundation maps are tied to real-time precipitation data obtained from the Automated Surface Observing System weather station at the Lee’s Summit Municipal Airport, accessible at https://mesonet.agron.iastate.edu/request/download.phtml?network=MO_ASOS . The availability of these maps, along with information regarding observed rainfall, could help provide emergency management personnel and residents with information that is critical for flood-response activities, such as evacuations and road closures, and for postflood recovery efforts.

Missouri↗

USGS2024

Monitoring and assessment of urban stormwater best management practices at selected Chicago public schools in Chicago, Illinois, from September 1, 2016, to July 1, 2017

The Space to Grow program helps transform aging and neglected schoolyards of Chicago Public Schools into outdoor community spaces with the goal of promoting health and learning while addressing neighborhood flooding issues. Virgil I. Grissom Elementary School and Donald L. Morrill Math and Science School were selected in 2014 for schoolyard upgrades and the installation of various green infrastructure (GI) improvements. The U.S. Geological Survey installed sensors to measure precipitation, groundwater levels, and stormwater runoff volumes from September 1, 2016, to July 1, 2017. At Virgil I. Grissom Elementary School, about 933,000 gallons of water fell on the schoolyard during the monitoring period. No discharge was recorded coming from the GI sewer lines, but backflow indicated water was flowing from the sewer line draining the impervious running track into the combined manhole structure and backwards into the GI retention basins (as designed). This design allowed for a 100-percent capture rate. Native soil at Virgil I. Grissom Elementary School also was conducive to rapid infiltration. Soil borings at Virgil I. Grissom Elementary School indicated about 10.5 feet (ft) of fine sand overlying silty clay to a depth of at least 16 ft. At Donald L. Morrill Math and Science School, about 1,120,000 gallons of water fell on the schoolyard during the monitoring period. About 72.5 precent of this water was discharged into the sewer system, and the other 27.5 percent was captured by the GI. Unlike Virgil I. Grissom Elementary School, the soil profile at Donald L. Morrill Math and Science School consisted of about 5 ft of clay loam overlying stiff blue clay to a depth of at least 12 ft. The sewer line coming from the GI under the football field was at the bottom of the reservoir. This design seemed to allow water to flow out of the line before being absorbed by the retention basin.

Illinois↗

USGS2025

Prospectivity mapping for geologic hydrogen

Geologic, or naturally occurring, hydrogen has the potential to become a new, low-carbon, primary energy resource. Often referred to as “white” or “gold” hydrogen, this gas occurs naturally in the Earth’s subsurface, similar to petroleum resources. However, unlike petroleum, which releases carbon dioxide when burned, burning hydrogen only produces water as a byproduct. Exploration for geologic hydrogen remains in an early stage and discoveries of high concentrations of subsurface hydrogen are still relatively rare. To facilitate research and exploration for this potential resource, this report presents the first publicly available prospectivity map of geologic hydrogen accumulations in the conterminous United States. Prospective regions are those regions in which all major components necessary for a hydrogen accumulation likely are present—a source of sufficient hydrogen generation, porous reservoirs for storage, and seals to prevent leakage. The midcontinent region of the United States and the central California coast are revealed as having high prospectivity. This analysis also identifies previously unrecognized prospective regions that may be favorable due to long distance lateral migration of subsurface hydrogen, such as the offshore eastern seaboard of the United States, and can provide a linkage between surface observations of hydrogen degassing and far-field source regions. The methodology developed to create this map is expandable and flexible and may be adapted to incorporate new concepts in the hydrogen system and for application to other regions of the world.

conterminous United States↗

USGS2025

Estimated ultimate recovery (EUR) Prediction for Eagle Ford Shale using integrated datasets and artificial neural networks

The estimated ultimate recovery (EUR) is an important parameter for forecasting oil and gas production and informing decisions regarding field development strategies. In this study, we combined site-specific geologic, completion, and operational parameters with the predictive capabilities of machine learning (ML) models to predict EURs of the wells for the Eagle Ford Marl Continuous Oil Assessment Unit. We developed an extensive dataset of wells that have produced from the lower and upper Eagle Ford Shale intervals and reduced the model complexity using principal component analysis. We tested the ML models and estimated the sensitivities of ML-predicted EURs to changes in the values of different input variables. The results of applying the optimized ML model to the Eagle Ford suggest that the approach developed in this study could be promising. The ML estimates of the EURs fit the DCA-based values with an R 2 ~ 0.9 and a mean absolute error of ~36 × 10 3 bbl. In the lower Eagle Ford Shale, the EUR estimates were found to be most sensitive to changes in porosity, net thickness of the interval, clay volume, and the API gravity of the oil; and that in the upper Eagle Ford Shale they were most sensitive to changes in the total organic carbon and water saturation, which suggests that it could be important to consider these parameters in assessing these intervals or close analogs.

Louisiana, Mississippi, Texas↗

USGS2025

Review and synthesis of the applications of machine learning to coalbed methane recovery

Over the last 30 years, a substantial literature has evolved on the use of machine learning (ML) to assess, predict, and improve the efficiency of coalbed methane (CBM) recovery. In the United States, the production of CBM declined as shale gas production matured, but CBM continues to be an important energy resource in other parts of the world. ML applications that have the potential to improve CBM reservoir management and production forecasts, and to increase exploration and operational efficiency, are still of significant interest. The integration of geostatistical techniques into the CBM ML applications has been largely absent but represents an opportunity for improvement. The literature demonstrates the widespread interest in, and applicability of, ML algorithms applied to CBM problems, and that they continue to result in improvements in predictive performance. However, (1) much of the research is more academic than operational, (2) many results are based on simulations, or small or proprietary datasets, (3) ML performance information can be inconsistent and sometimes entirely omitted, (4) most methodologies are unique to the specific CBM situation and likely not generalizable, (5) no standard data repositories are available to directly compare the performance of competing algorithms, and (6) the spatial component is often omitted. Finally, relatively new ML protocols involving causality analysis and reinforced learning, as well as hybrid workflows combining both supervised and unsupervised learning, are anticipated to dominate the future investigations. Integration of geostatistical and geospatial analysis with ML should enhance performance.

Book chapter↗

USGS2025

Critical minerals in mine waste

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

Fact Sheet↗