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1,748 records · Page 43Linked to original sources
USGS2025

Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs

Oil and gas reservoirs represent suitable containers to sequester carbon dioxide (CO 2 ) in a supercritical state because they are accessible, reservoir properties are known, and they previously contained stored buoyant fluids. However, planners must quantify the relative magnitude of the CO 2 storage resource in these reservoirs to formulate a comprehensive strategy for CO 2 mitigation. Even reconnaissance-type estimates of CO 2 storage resources of known oil and gas reservoirs may require complicated calculations involving 1) estimates of recoverable oil and gas, 2) reservoir properties (depth, temperature, pressure, etc.), and 3) the physical qualities of the retained fluids. We demonstrate the application of machine learning (ML) algorithms to bypass these computations to yield more rapid estimates of CO 2 storage resources in reservoirs capable of hosting CO 2 in a supercritical state. ML algorithms are computationally efficient because they do not impose the strong assumptions on the data-generating process that standard statistical or engineering procedures require. Further, ML algorithms can capture highly complex, particularly nonlinear, relationships among predictor variables. We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. The proposed methods and models provide an effective and novel way to more rapidly and directly determine the subsurface CO 2 storage capacity of oil and gas reservoirs around the world, information that operators, researchers, and policymakers alike require to meet energy transition and decarbonization goals.

Frontiers in Enviornmental Science↗
USGS2025

Slow rupture, long rise times, and multi-fault geometry: The 2020 M6.4 southwestern Puerto Rico mainshock

The M 6.4 mainshock of the southwestern Puerto Rico seismic sequence on 7 January 2020, was one of the most impactful modern earthquakes in the northeastern Caribbean. Due to its offshore location and complex aftershock distribution, its source kinematics remain poorly constrained. This active sequence illuminated a complex set of previously unrecognized structures that indicate multiple causative faults may have slipped during its rupture. Here, we utilize seismic and geodetic observations to enhance model resolution, estimate the finite slip of the mainshock, and test a multi-segment, geologically realistic fault geometry. Our refined model finds a lower rupture velocity and longer rise times than typical for an event of this magnitude. This indicates a slow-evolving rupture process that resembles characteristics of a tsunami earthquake. Although this normal/strike-slip faulting event was not tsunamigenic, these qualities, if pervasive for this region, have important implications for future seismic monitoring and hazards in southwestern Puerto Rico.

Puerto Rico↗
USGS2025

Increased flood exposure in the Pacific Northwest following earthquake-driven subsidence and sea-level rise

Climate-driven sea-level rise is increasing the frequency of coastal flooding worldwide, exacerbated locally by factors like land subsidence from groundwater and resource extraction. However, a process rarely considered in future sea-level rise scenarios is sudden (over minutes) land subsidence associated with great (>M8) earthquakes, which can exceed 1 m. Along the Washington, Oregon, and northern California coasts, the next great Cascadia subduction zone earthquake could cause up to 2 m of sudden coastal subsidence, dramatically raising sea level, expanding floodplains, and increasing the flood risk to local communities. Here, we quantify the potential expansion of the 1% floodplain (i.e., the area with an annual flood risk of 1%) under low (~0.5 m), medium (~1 m), and high (~2 m) earthquake-driven subsidence scenarios at 24 Cascadia estuaries. If a great earthquake occurred today, floodplains could expand by 90 km 2 (low), 160 km 2 (medium), or 300 km 2 (high subsidence), more than doubling the flooding exposure of residents, structures, and roads under the high subsidence scenario. By 2100, when climate-driven sea-level rise will compound the hazard, a great earthquake could expand floodplains by 170 km 2 (low), 240 km 2 (medium), or 370 km 2 (high subsidence), more than tripling the flooding exposure of residents, structures, and roads under the high subsidence scenario compared to the 2023 floodplain. Our findings can support decision-makers and coastal communities along the Cascadia subduction zone as they prepare for compound hazards from the earthquake cycle and climate-driven sea-level rise and provide critical insights for tectonically active coastlines globally.

California, Oregon, Washington↗
USGS2025

Bølling-Allerød productivity in the subarctic Pacific driven by seasonal upwelling

The Bølling-Allerød deglacial event is marked by high diatom productivity and opal deposition throughout the subarctic Pacific. This opal could either constitute a strengthened biological pump and thus carbon sequestration, or a weakened biological pump and release of marine-sequestered CO 2 to the atmosphere. We quantify silicic acid supply at IODP Site U1340 in the Bering Sea using biogenic opal and δ 30 Si of Coscinodiscus , a diatom genus. These records, along with diatom environmental indicators, suggest the Bølling-Allerød had high silicic acid availability related to a shift from stratification to seasonal upwelling dynamics. We thus propose the primary cause of the high productivity event was increased macronutrient supply from vertical exchange that injected old, nutrient-rich, CO 2 -rich waters into the surface. Enhanced CO 2 release from the subarctic Pacific may help explain critical intervals of CO 2 rise that occur at the onsets of the Bølling-Allerød and PreBoreal.

Geophysical Research Letters↗
USGS2025

Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole

We use a high-resolution digital elevation model and a numerical thermal model to produce a variety of inputs for a water-ice prospectivity model for the Volatiles Investigating Polar Exploration Rover (VIPER) landing site. These input data are maps of topography, surface slope, surface aspect, surface curvature, maximum temperature, depth to ice stability, permanently shadowed regions (PSRs), distance to PSRs, and PSR density. This model predicts where water ice is most likely within the top meter of regolith, assuming plausible relationships between ice concentration and the various inputs. The model is designed to be adjusted in near-real time as data are collected during the VIPER mission. As such, it is a tool for both analyzing data from the mission as well as planning operations. Since the current model, at this point, relies only on orbital remote sensing, the final version will also be a tool to extrapolate the VIPER mission results across the lunar poles.

Planetary Science Journal↗
USGS2025

Broadband stochastic simulation of earthquake ground motions with multiple strong phases with an application to the 2023 Kahramanmaraş, Turkey (Türkiye), earthquake

Stochastic ground motion simulation models are often less accurate at lower frequencies than at higher frequencies when fitting recorded data unless supplemented by a deterministic forward directivity velocity pulse model. Moreover, time-modulated stochastic models, which adjust ground motion amplitudes over time, typically use functions that fail to capture multiple strong-motion phases. The February 2023 Turkey (Türkiye) earthquake exhibited diverse recordings, including near-fault and far-field motions with pulse-like and non-pulse-like characteristics, along with single and multiple strong-motion phases. To better represent such a diverse set of recordings, this study enhances a fully non-stationary site-based stochastic model without combining it with a deterministic model. Improvements include a new band-pass filter with upper- and lower-frequency limits, which refines the representation of the low-frequency content. Moreover, a time-modulating function that can represent energy arrival in multiple strong phases is introduced. The reference model’s parameters are identified by fitting to the energy content, zero-level crossings, and cumulative counts of positive-minima and negative-maxima of a target accelerogram. This fitting procedure is modified to address the increased number of parameters. These improvements broaden the reference model’s applicability while preserving its simplicity, a key aspect appealing to engineering practitioners. The improved model’s applicability is demonstrated by simulating a dataset from the February 2023 Türkiye earthquake, and the accuracy is tested using a pulse-like Next Generation Attenuation Relationships for Western United States dataset. Validations are performed based on total energy, zero-level crossings, Fourier amplitude spectrum, elastic response spectra, and peak ground motion parameters. Validations are performed schematically in the time and frequency domains and quantitatively using goodness-of-fit scores, various validation-metrics errors, and inter-period correlations. Overall, the improved stochastic model can effectively simulate a set of diverse ground motion recordings, including near-fault pulse-like records, records with multiple strong phases, and far-field motions across a broad frequency range.

Kahramanmaraş↗
USGS2025

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

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

Alaska↗
USGS2025

Flow heterogeneity controls dissolution dynamics in topologically complex rocks

Rock dissolution is a common subsurface geochemical reaction affecting pore space properties, crucial for reservoir stimulation, carbon storage, and geothermal energy. Predictive models for dissolution remain limited due to incomplete understanding of the mechanisms involved. We examine the influence of flow, transport, and reaction regimes on mineral dissolution using 29 time-resolved data from 3D rocks. We find that initial pore structure significantly influences the dissolution pattern, with reaction rates up to two orders of magnitude lower than batch conditions, given solute and fluid-solid boundary constraints. Flow unevenness determines the location and rate of dissolution. We propose two models describing expected dissolution patterns and effective reaction rates based on dimensionless metrics for flow, transport, and reaction. Finally, we analyze feedback between evolving flow and pore structure to understand conditions that regulate/reinforce dissolution hotspots. Our findings underscore the major impact of flow arrangement on reaction-front propagation and provide a foundation for controlling dissolution hotspots.

Geophysical Research Letters↗
USGS2025

Statistical approaches for modeling correlated grade and tonnage distributions and applications for mineral resource assessments

Correlations between grade and tonnage exist in mineral resource data compiled from published reports, but they are not always addressed during quantitative assessment of undiscovered mineral resources. Failure to account for correlated grade and tonnage distributions can result in geologically unrealistic assessment results. Current software tools simulate univariate ore tonnage and multivariate resource grades of undiscovered deposits independently. As a result, analysts are forced to rely on ad-hoc solutions to minimize the correlation issues by: 1) creating subsets of data with restricted criteria; 2) truncating grade and tonnage distributions; and 3) testing model robustness using exploratory data analysis. While these methods represent pragmatic solutions, the statistical solutions presented here provide additional options to address real correlations in grade and tonnage data used for mineral resource assessments. We present a modified version of the MapMark4 package in R that introduces two alternatives for modeling grade and tonnage distributions, consisting of a multivariate solution that accounts for correlations between ore tonnage and metal grades and an empirical solution that utilizes simple random sampling with replacement to reproduce coupled grades and tonnages from the input data. We present simulations for contained ore and metal for three case studies representing tungsten skarn, komatiite-hosted nickel, and sediment-hosted carbonate amagmatic zinc-lead (Mississippi Valley-type) deposits. Employing the methods presented here yields quantitative mineral resource assessment results that more closely reflect the empirical distributions of grades and tonnages observed in nature and expands the applicability of these tools for ongoing critical mineral resource assessments.

Applied Computing and Geosciences↗
USGS2025

National population exposure and evacuation potential in the United States to earthquake-generated tsunami threats

Previous efforts to characterize tsunami threats to people have focused primarily on individual scenarios in specific areas but have not recognized multiple scenarios across an entire country. This study addresses this gap by quantifying population exposure and evacuation potential in the United States to 102 earthquake-related, tsunami-hazard zones, including 92 local scenarios, 8 distant scenarios, and 2 probabilistic products. Geospatial path-distance modeling quantified evacuation potential and the influence of departure delays. We focused on residents to support other national, multi-hazard risk analyses. Millions of residents are in distant-tsunami zones, and hundreds of thousands of residents are in local-tsunami zones. In 41 scenarios, there is at least one resident that may have insufficient time to evacuate before wave arrival. Tens of thousands of residents may have insufficient time to evacuate from local tsunamis that impact the U.S. Pacific Northwest or Puerto Rican coastlines. The largest improvements in evacuation potential may come from reducing departure delays in some areas but may involve vertical-evacuation structures or changing land use in other areas.

International Journal of Disaster Risk Reduction↗
USGS2025

Hydrogeologic framework and considerations for drilling and grouting of closed-loop geothermal bores in the Erie-Ontario Lowlands and Allegheny Plateau of New York State

The hydrogeologic framework at closed-loop geothermal sites in the Erie-Ontario Lowlands and Allegheny Plateau of central and western New York is the result of the complex interaction of bedrock geology, glacial geology, and groundwater hydrology, and the occurrence of petroleum and gas. Considerations for closed-loop geothermal bore installation include the thickness and character of glacial deposits, bedrock solubility and depth to competent rock, karst development, the distribution of highly permeable zones and their hydraulic heads, and the presence of saline water, gas, and oil. The hydrogeology of the Erie-Ontario Lowlands and Allegheny Plateau poses challenges to closed-loop geothermal bore drilling and casing; managing drill cuttings, discharge water, and gas; and grouting. The potential to encounter severe challenges typically increases with bore depth. This report highlights hydrogeologic considerations for closed-loop geothermal bore installation in New York’s Erie-Ontario Lowlands and Allegheny Plateau to help guide the efficient and safe development of geothermal resources in the regions.

New York↗
USGS2025

Simulated effects of future water availability and protected species habitat in a perennial wetland, Santa Barbara County, California

This study evaluates the potential water availability in Barka Slough and the effects of changing hydrological conditions on the aquatic habitat of five protected species. Barka Slough is a historically perennial wetland at the downstream western end of the San Antonio Creek Valley watershed (SACVW). A previously published hydrologic model of the SACVW for 1948–2018 was extended to include 2019–2021 and then modified to simulate the future years of 2022–2051. Two models simulating the future years of 2022–2051 were constructed, each with different climate inputs: (1) a repeated historical climate and (2) a 2070-centered Drier Extreme Warming climate (2070 DEW). The model with the 2070 DEW climate had warmer temperatures and an increase in average annual precipitation driven by larger, albeit more infrequent, precipitation events than the model with the historical climate. Simulated groundwater pumpage resulted in cumulative groundwater storage depletion and groundwater-level decline in Barka Slough in both future models. The simulations indicate that Barka Slough may transition from a perennial to an ephemeral wetland. Streamflow, stream disconnection, and depth to groundwater are key habitat metrics for federally listed species in Barka Slough. Future seasonal conditions for each metric are more likely to affect federally listed species’ habitats under 2070 DEW climatic conditions. Future seasonal streamflow volume may negatively impact unarmored threespine stickleback ( Gasterosteus aculeatus williamsoni ) and tidewater goby ( Eucyclogobis newberryi) habitats. Future seasonal stream disconnection may negatively impact the unarmored threespine stickleback habitat. Future groundwater-level decline may negatively impact Gambel’s watercress ( Nasturtium gambelii ) and La Graciosa thistle ( Cirsium scariosum var. loncholepis ) habitats and could influence the ability to use Barka Slough as a restoration or reintroduction site for these species. Results from this study can be used to inform water management decisions to sustain future groundwater availability in the SACVW.

California↗
USGS2025

Comparative properties of saponitic fault gouge and serpentinite muds cored from mud volcanoes of the Mariana subduction zone

We obtained 12 core samples for physical and chemical characterization from three serpentinite mud volcanoes (Yinazao, Asùt Tesoru, and Fantangisña) located on the forearc of the Mariana subduction system, that were drilled during International Ocean Discovery Program Expedition 366. Two samples from the Fantangisña mud volcano are interpreted to be clay-rich fault gouges derived from the subduction channel. Their bulk compositions are intermediate between the serpentinites and oceanic basalts. The oceanic crustal materials in the gouges have been thoroughly metasomatized and the serpentinites extensively altered to the trioctahedral, Mg-rich smectite clays saponite and corrensite. The only relict phases in clasts of crustal rock are accessory Ti- and P-bearing minerals. The two fault gouge samples have lower frictional strengths ( μ < 0.2) than the serpentinites ( μ = 0.2–0.4), and their measured permeabilities are also somewhat lower. Their physical and compositional properties correspond to saponitic gouges from other faults that juxtapose serpentinite against crustal rocks, in particular gouges from the two creeping traces of the San Andreas Fault recovered in the core from the San Andreas Fault Observatory at Depth. The décollement beneath Fantangisña mud volcano is thus expected to be very weak and likely characterized by stable slip.

Geophysics, Geochemistry, Geosystems↗
USGS2025

Groundwater budget for the surficial aquifer surrounding Lake Nokomis, Minneapolis, Minnesota

During prolonged periods of above-average precipitation, rising groundwater levels have the potential to cause damage to and interfere with underground infrastructure and building foundations. To understand the relations between precipitation and groundwater in the vicinity of Lake Nokomis, the U.S. Geological Survey, in collaboration with the University of Minnesota, quantified five components of the groundwater budget: groundwater recharge, change in surficial aquifer storage, surficial aquifer groundwater discharge to Lake Nokomis, groundwater evapotranspiration, and groundwater discharge to underlying bedrock aquifers. Field data, geologic records, and empirical calculation methods were used to quantify groundwater budget components for April 2023 through April 2024. Lake water budget data indicate that Lake Nokomis is a flowthrough system during periods with no outflow through the weir, with groundwater inputs equal to outputs. Roughly 40 percent of precipitation that fell in the study area was added to the surficial aquifer as recharge. Uncertainty in the vertical hydraulic conductivity resulted in wide-ranging estimates (spanning three orders of magnitude) of water discharging from the surficial aquifer to the underlying bedrock aquifer. Drought conditions persisted for the duration of this study and were not representative of the conditions that motivated this study. This study is a start towards understanding relations between precipitation, Lake Nokomis levels, and groundwater levels that could affect local underground infrastructure.

Minnesota↗
USGS2025

Latitudinal gradients of snow contamination in the Rocky Mountains associated with anthropogenic sources

Seasonal snow is an important source of drinking water and recreation, and for agriculture in the Rocky Mountain region. Monitoring snow-water quality can inform on the effects to the albedo and energy balance of the snowpack, and the sources of natural and anthropogenic aerosol and gases. This study analyzed metals in the seasonal snowpack from water year (WY) 2018 for 49 sites. Calcium, lanthanum, and cerium concentrations support the importance of mineral dust to the southern Rocky Mountains. Mercury (Hg), zinc (Zn), and cadmium (Cd) concentrations showed a similar spatial pattern to mineral dust, whereas antimony (Sb) concentrations were highest in the northern Rocky Mountains. To assess the relative contributions from dust versus anthropogenic contaminant sources, enrichment factors (EF) were calculated, with values above 10 indicating anthropogenic contamination. For Cd, Hg, Sb, and Zn, EF values exceeded 10 at northern sites. These observations were compared to spatial trends of EF values of Hg from WY2009 to WY2018, regional monitoring networks, and back trajectory analyses. The agreement between these datasets revealed temporally consistent contaminant sources and/or transport processes to the northern Rocky Mountains snowpack. Sources include current and historical mining and smelting in the region. Strategies to limit the emissions of these metals to the Northern Rockies could benefit from focusing on remediation of contaminated sites, and continued monitoring and mitigation of active mining and smelting.

Colorado , Idaho, Montana, New Mexico, Utah, Wyomi↗
USGS2025

A framework for understanding the effects of subsurface agricultural drainage on downstream flows

Understanding controls on streamflow volume and magnitude is important to water resource management applications, such as critical water and transportation structure design and floodplain mapping. Changes in land use and agricultural practices, such as subsurface agricultural drainage, may be contributing to changes in streamflow characteristics. Subsurface agricultural drainage, also known as tile drainage, is the practice of installing drains in the subsurface of agricultural fields to improve productivity. Because of the complex interactions between subsurface drainage systems, precipitation, local soil conditions, and land management practices, it is difficult to determine how subsurface agricultural drainage affects downstream flow. Previously developed subsurface agricultural drainage conceptual models under dry, saturated, and winter conditions are summarized, and current literature on the effects of subsurface agricultural drainage on downstream flows, focusing on peak flow, non-event flow, and total flow to develop frameworks for discussing these systems is compiled. The effects that subsurface drainage has on hydrologic systems are expected to vary by site and are seasonally based on system design, soil type, moisture conditions, precipitation characteristics, and land conditions. Subsurface drainage can affect the magnitude of peak flow by converting surface runoff from a storm event to subsurface runoff. By increasing hydrologic connectivity of a catchment, subsurface drainage can increase non-event flow or the flow between two storm events, typically dependent on lateral flow through the subsurface and groundwater. Theoretically, by diverting water from groundwater recharge or by reducing water available for evapotranspiration, subsurface drainage may increase the total volume of flow. Precipitation changes may increase infiltration, excess overland flow, and flood risk regardless of the presence or absence of subsurface drainage.

Illinois, Iowa, Michigan, Minnesota, Missouri, Mon↗
USGS2025

Gaps in water quality modeling of hydrologic systems

This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations of management alternatives to support ecosystem health and human beneficial use of water resources. It covers surface water and groundwater quality modeling, dealing with a broad suite of physical, biogeochemical, and anthropogenic drivers. Modeling capabilities for six constituents (or constituent categories) are explored: water temperature, salinity, nutrients, sediment, geogenic constituents, and contaminants of emerging concern. Each constituent was followed through the coupled atmospheric-hydrologic-human system, with prominent modeling gaps described for a diverse array of relevant inputs, processes, and human activities. Commonly identified modeling gaps primarily fall under three types: (1) model gaps, (2) data gaps, and (3) process understanding gaps. In addition to potential solutions for addressing specific individual modeling limitations, some broad approaches (e.g., enhanced data collection and compilation, machine learning, reduced-complexity modeling) are discussed as ways forward for tackling multiple gaps. This gap analysis establishes a framework of diverse approaches that may support improved process representation, scale, and accuracy of models for a wide range of water quality issues.

Water↗