Archive of sidescan-sonar data collected off the North Carolina coast during USGS cruise ATSV99045, 7-27 October, 1999
No abstract available.
North Carolina↗
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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.
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Microbial ecology of permafrost soils: Populations, processes, and perspectives
Permafrost microbial research has flourished in the past decades, due in part to improvements in sampling and molecular techniques, but also the increased focus on the permafrost greenhouse gas feedback to climate change and other ecological processes in high latitude and alpine permafrost soils. Permafrost microorganisms are adapted to these extreme environments and remain active at low temperatures and when resources are limited. They are also an important component of global elemental cycles as they regulate organic matter turnover and greenhouse gas production, particularly as permafrost thaws. Here we review the permafrost microbiology literature coupled with an exploration of its historical aspects, with a particular focus on a new understanding advanced by molecular biology techniques. We further identify knowledge gaps and ways forward to improve our understanding of microbial contributions to ecosystem biogeochemistry of permafrost-affected systems.
Permafrost and Periglacial Processes↗
Inset groundwater-flow models for the Cache and Grand Prairie Critical Groundwater Areas, northeastern Arkansas
The water resources in the Mississippi alluvial plain, located in parts of Missouri, Kentucky, Tennessee, Mississippi, Louisiana, and Arkansas, supports a multibillion-dollar agricultural industry that relies heavily on pumping of groundwater for irrigation of crops and aquaculture. The primary source of groundwater for agricultural-related pumping is the Mississippi River Valley alluvial aquifer, which has declined in storage for decades; secondary groundwater sources include the middle Claiborne aquifer and Wilcox aquifer system. Two areas in northeastern Arkansas that lie within the Mississippi alluvial plain, part of the Cache and Grand Prairie regions, have been designated as Critical Groundwater Areas owing to decades of groundwater declines that resulted from past and current water use. The multidisciplinary Mississippi Alluvial Plain project, led by the U.S. Geological Survey, and funded by their Water Availability and Use Science Program, included objectives to develop numerical groundwater models in focus regions, including the part of the Cache and Grand Prairie regions of northeastern Arkansas. Two inset models were developed using the child model capabilities of MODFLOW 6, the U.S. Geological Survey’s Modular Hydrologic Model simulation software. Both models, called the Cache model and Grand Prairie model, simulated the groundwater system and surface-water/groundwater interactions for the Mississippi River Valley alluvial aquifer and underlying Tertiary-age aquifers and confining units to the Midway confining unit. Each model was spatially discretized into 500-meter x 500-meter orthogonal cells on a grid with 5-meter constant-thickness vertical layers that represented the Mississippi River Valley alluvial aquifer and increasing thickness layers for the aquifers and confining units below the alluvial aquifer. The Cache and Grand Prairie models were calibrated with the PEST++ iterative ensemble smoother Version 5 and employed high dimensional parameterization schemes of 13,740 and 30,436 parameters, respectively. The Cache mean absolute residual for groundwater-level observations within each model domain for the priority well was 1.58 meters. Grand Prairie mean absolute residuals for the alluvial aquifer and middle Claiborne aquifer groundwater-level observations were 2.71 and 10.78 meters, respectively. The groundwater budgets for the Cache and Grand Prairie models were characterized by substantial outflows to irrigation wells, which constituted about 52 and 54 percent of all outflows, with the primary source of water to those wells being releases from unconfined aquifer storage.
Arkansas↗
Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions
Selecting negative training sites is an important challenge to resolve when utilizing machine learning (ML) for predicting hydrothermal resource favorability because ideal models would discriminate between hydrothermal systems (positives) and all types of locations without hydrothermal systems (negatives). The Nevada Machine Learning project (NVML) fit an artificial neural network to identify areas favorable for hydrothermal systems by selecting 62 negative sites where the research team had confidence that no hydrothermal resource exists. Herein, we compare the implications of the expert selection of negatives (i.e., the NVML strategy) with a random sample strategy, where it is assumed that areas outside the favorable structural ellipses defined by NVML are negative. Because hydrothermal systems are sparse, it is highly probable that, in the absence of a favorable geological structure, hydrothermal favorability is low. We compare three training strategies: 1) the positive and negative labeled examples from NVML; 2) the positive examples from NVML with randomly selected negatives in equal frequency as NVML; and 3) the positive examples from NVML with randomly selected negatives reflecting the expected natural distribution of hydrothermal systems relative to the total area. We apply these training strategies to the NVML feature data (input data) using two ML algorithms (XGBoost and logistic regression) to create six favorability maps for hydrothermal resources. When accounting for the expected natural distribution of hydrothermal systems, we find that XGBoost performs better than the NVML neural network and its negatives. Model validation was less reliable using F1 scores, a common performance metric, than comparing probability estimates at known positives, likely because of the extreme natural class imbalance and the lack of negatively labeled sites. This work demonstrates that expert selection of negatives for training in NVML likely imparted modeling bias. Accounting for the sparsity of hydrothermal systems and all the types of locations without hydrothermal systems allows us to create better models for predicting hydrothermal resource favorability.
Geothermal Resources Council Transactions↗
Factors influencing landslide occurrence in low-relief formerly glaciated landscapes: Landslide inventory and susceptibility analysis in Minnesota, USA
In landscapes recently impacted by continental glaciation, landslides may occur where topographic relief has been generated by the drainage of glacial lakes and ensuing post-glacial fluvial network development into unconsolidated glacially derived sediments and exhumed bedrock. To investigate linkages among environmental variables, post-glacial landscape development, and landslides, we created a landslide inventory of nearly 10,000 landslides in five regions of the formerly glaciated low-relief state of Minnesota, USA. Multivariate logistic regression indicates the importance of slope angle, lithology, and the development of stream valleys to landslide distribution. Areas underlain by fine-grained glaciolacustrine and nearshore deposits that are incised by streams are particularly prone to shallow (<1-2 m depth) landslides. Landslides also occur in a wide range of glacial and fluvial deposits, and as rockfall in layered Paleozoic sedimentary rocks in central and southern Minnesota and Precambrian igneous and sedimentary rocks in northeastern Minnesota. Although no more than 1-2% of the studied regions are susceptible to landslides, they can pose risk to life and safety, damage infrastructure, and impact water quality. The combination of recently generated low-relief steep slopes, extensive unconsolidated sediments, and layered sedimentary bedrock make this formerly glaciated landscape more susceptible to landslides than current national-scale models indicate.
Minnesota↗
Enhancing mineral systems exploration through geochronology, thermochronology, and isotope analysis: USGS Geochron and USGS Isotope databases
A mineral systems approach to mineral exploration provides a comprehensive framework for understanding ore deposit formation by examining the geodynamic, magmatic, hydrothermal, and sedimentary processes responsible for mineralization, alteration, and remobilization of economic mineral deposits. Temporal and thermal constraints on ore genesis are crucial for refining mineral system models and guiding predictive exploration strategies. Geochronology and thermochronology offer invaluable insights into the timing and thermal evolution of ore-forming processes, whereas isotopic analyses provide critical information on the source and geochemical history of ore-forming fluids. Combining these methodologies have proven highly effective for mineral exploration in regions like Australia, however, their combined application has been limited in the United States. To apply these tools to mineral systems-based exploration, the U.S. Geological Survey (USGS) has developed two products: (1) The USGS Geochron Database, and (2) the USGS Isotope Database. These databases provide centralized repositories of geo/thermochronological dates and data (Geochron Database) and both radiogenic and stable isotope data (Isotope Database) generated by the USGS and partners over the past decades. Integrating these datasets together and with traditional exploration approaches provides the mineral exploration community with powerful tools for determining the temporal and thermal histories of ore systems and identifying metallogenic source provinces.
Continental United States↗
A global database of soil microbial phospholipid fatty acids and enzyme activities
Soil microbes drive ecosystem function and play a critical role in how ecosystems respond to global change. Research surrounding soil microbial communities has rapidly increased in recent decades, and substantial data relating to phospholipid fatty acids (PLFAs) and potential enzyme activity have been collected and analysed. However, studies have mostly been restricted to local and regional scales, and their accuracy and usefulness are limited by the extent of accessible data. Here we aim to improve data availability by collating a global database of soil PLFA and potential enzyme activity measurements from 12,258 georeferenced samples located across all continents, 5.1% of which have not previously been published. The database contains data relating to 113 PLFAs and 26 enzyme activities, and includes metadata such as sampling date, sample depth, and soil pH, total carbon, and total nitrogen. This database will help researchers in conducting both global- and local-scale studies to better understand soil microbial biomass and function.
Scientific Data↗
USGS Geochron—A database of geochronological and thermochronological dates and data—Technical documentation
Geochronological and thermochronological data are essential for constraining the timing and rates of geological processes, supporting geologic mapping, natural hazard assessment, and resource exploration. The U.S. Geological Survey (USGS) Geochron Database is a centralized, relational database that integrates USGS and State geological survey data in accordance with the National Geologic Mapping Act of 1992 and its reauthorizations. This report documents the structure and development of the database, including its standardized schema, controlled vocabularies, and protocols for compiling legacy and newly published data. The database is designed to adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and currently (2026) includes data from a wide range of geochronological and thermochronological methods. Public access is provided through the USGS Geochron Database Explorer, a browser-based geographic information system (GIS) interface, and through versioned data releases available on ScienceBase, which provide sample-level summary data and detailed analytical information. The USGS Geochron Database is structured to be extensible, supporting the inclusion of additional methods and data types as they are compiled. This format ensures long-term utility and aligns with the Mapping Act’s directive to create a national archive of geochronological information that adds interpretive value to geologic map data. Its design reflects a commitment to data transparency, scientific reproducibility, and national geoscience priorities.
Data Report↗
Collaborative drought science planning in the Colorado River Basin
The U.S. Geological Survey (USGS) is using collaborative, interdisciplinary planning to develop data and tools needed to optimize the management of water resources and land use by resource management agencies during an ongoing, multidecadal drought in the Colorado River Basin. The USGS Actionable and Strategic Integrated Science and Technology team works to build relationships with resource management agencies and other stakeholders who can benefit from the use of USGS data and products. In 2023, the Actionable and Strategic Integrated Science and Technology team hosted a series of collaborative workshops to bring together representatives of resource management agencies and other stakeholders (any person or entity with interests in a resource or location) with USGS program managers, scientists, and multidisciplinary subject matter experts to codevelop concepts for interdisciplinary drought science and technology projects to address pressing needs related to drought in the Colorado River Basin. Workshop participants identified current and recent scientific data that could be shared through a centralized online data portal. Workshop participants also identified drought science and technology needs and developed project concepts to address those science needs. Participants categorized project concepts based on their potential to develop short-, mid-, and long-term drought science data and tools, provide for the spatial or temporal expansion of ongoing USGS science projects, and address high-priority science needs. Participants developed nine project concepts: (1) understanding shifting ecohydrologic baselines, (2) San Juan River Basin synthesis, (3) incorporating dynamic land cover into hydrologic models, (4) aridification compared to drought, (5) surface water-groundwater interactions, (6) cascading effects of drought on dust, (7) cascading effects of drought on water availability, (8) cascading effects of drought on socioeconomic factors, and (9) the value of water in the Colorado River Basin. This report provides an overview of the 2023 Codesign Workshop Series, synthesized outcomes from workshop materials and discussions, and science project concepts that emerged from the collaborative meetings that will continue to be refined into science project proposals through codevelopment processes. This report also highlights lessons learned and next steps needed to receive feedback and testing of the USGS Science Collaboration Portal, continue collaboration to develop detailed specifics and steps for short-term wins, develop interdisciplinary project proposals, and implement science planning and studies.
Arizona, California, Colorado, Nevada, New Mexico,↗
Inference of pattern-based geological CO2 sequestration and oil recovery potential in a commingled main pay and residual oil zone CO2-EOR flood
Several detailed studies have shown that residual oil zones (ROZs) can present significant resources for additional hydrocarbon recovery as well as subsurface carbon dioxide (CO 2 ) sequestration via enhanced oil recovery by injecting CO 2 (CO 2 -EOR). Field development strategies included new wells drilled dedicated to main pay zones (MPZ) and ROZs, or existing wells in MPZs deepened to ROZs for commingled injection-production using different well patterns. The latter presented a challenge when discerning the injection and production from each of the zones, and for subsequent quantification of CO 2 sequestration and EOR potential from different patterns and from the field. In this paper, an innovative method for analyzing commingled injections and productions from MPZs and ROZs, with application to pattern-based data from four staggered line drive patterns in Wasson Field's Denver Unit, Texas, USA, was developed. Decline curve and ratio-trend methods were used as means of history-matching and forecasting. Cumulative production-time and cumulative production-rate data for oil, gas, and water, as well as water-oil ratio (WOR) and gas-oil ratio (GOR), were analyzed along with injection data for time intervals covering major injection events in MPZ, or MPZ and ROZ combined. A combined analysis enabled inference of allocation of fluids into different zones during WAG (water alternating gas) injection and thereby estimation of CO 2 storage, utilization, and retention in different zones as a function of total injection. Results show that ROZs generally present higher CO 2 sequestration potential compared to MPZs, and a comparable incremental oil recovery factor of ∼20%, on average. Results based on ratio analysis further show that while the WOR trend of the pattern production is mostly dominated and controlled by ROZ, GOR is controlled by both intervals. Although the method relying on decline curves and the approach used in zonal fluid allocations are subject to their limitations, this study presents a practical and innovative well-pattern-based method to infer and forecast CO 2 sequestration and oil recovery quantities and fluid ratios from MPZs and ROZs in commingled operations and highlight the added potential offered by ROZs.
Texas↗
topoBuilder quick start guide
TopoBuilder is a public web application from the National Geospatial Program that enables anyone to create customized digital U.S. Geological Survey (USGS) topographic maps, called OnDemand Topos, with the best available, most up-to-date data from The National Map (nationalmap.gov). OnDemand Topos can be made at different scales or quadrangles and can cover anywhere within the United States and its territories.
Fact Sheet↗
Core microbiomes as a potential fingerprinting method of Western USA dust sources
Introduction: Changing frequency and intensity of dust emissions impacts ecosystems and human health. Dust carries microbes, nutrients, heavy metals, and other materials that may change environmental biogeochemistry at deposition sites. Identifying dust sources provides key information on where and when mitigation strategies should be employed. However, commonly used geochemical or isotopic tracers are often not capable of distinguishing between geographic regions. Methods: We explored whether soil bacterial communities may provide distinct fingerprints of dust sources in the western United States. We identified bacterial core communities of dust from ten locations monitored by the National Wind Erosion Research Network (NWERN) with varied land use (cropland, rangeland, and playa), and compared communities to location, soil, and regional characteristics. Samples were collected monthly from Modified Wilson and Cooke (MWAC) samplers, composited by season (spring, summer, and fall), and analyzed using 16S rRNA sequencing. Results: We found distinct bacterial core communities that reflected dust source characteristics. In order of importance, precipitation levels ( p = 0.0001), location ( p = 0.0001), soil texture ( p = 0.0001), seasonality ( p = 0.0001), and elevation (p = 0.0002) were correlated with bacterial community composition. Discussion: Distinct bacterial core communities were associated with site characteristics such as biocrusts, playas, and military base proximity. Our results suggest that the use of core microbiomes may offer a fingerprinting method to identify dust source regions.
Colorado, Nevada, New Mexico, North Dakota, Oklaho↗
Navigating uncertainty and competing objectives: Spring chinook salmon recovery in the upper Willamette river
Globally, anadromous fish populations are threatened with extinction due to multiple factors, including river impoundments that block migration, widespread alteration of physical habitat, temperature, and flow regimes, commercial and recreational harvest, changes in biological communities, and long-term climate trends. In this synthesis, we describe how physical and biological factors, policy and law, and public resource allocation affect management of spring-run Chinook Salmon ( Oncorhynchus tshawytscha ) in the Upper Willamette River (UWR), Oregon, USA. Efforts to restore salmon populations often involve trade-offs, for example, water management decisions must account for flood control, agricultural and municipal water use, and management of other species. UWR Chinook Salmon recovery efforts are also governed by multiple laws, policies, and action agencies that in some cases have incongruent objectives. Competing and conflicting objectives, combined with uncertainty regarding the outcomes of proposed management actions, create significant challenges for decision makers. Resource limitations, regulatory constraints, and incongruent objectives have created challenges for managers in the UWR, but opportunities exist for improved adaptive management and collaboration, which could build trust and engagement among stakeholders and ultimately support the successful implementation of decision tools. A suite of decision support tools that have been used in the Willamette River basin and elsewhere across the range of Pacific salmon have the potential to enhance decision processes. These tools can be effective when decision makers and stakeholders commit to long-term, collaborative utilization of the tools for decision making, and when available resources and regulatory environments are conducive to implementation and adaptive refinement of both the decision-support tools and management plans. The general biological and institutional principles discussed for UWR Chinook Salmon in this work apply more generally to populations of anadromous fishes across their range, especially to populations in highly regulated river systems.
Oregon↗
The Europa Imaging System (EIS) investigation
The Europa Imaging System (EIS) consists of a Narrow-Angle Camera (NAC) and a Wide-Angle Camera (WAC) that are designed to work together to address high-priority science objectives regarding Europa’s geology, composition, and the nature of its ice shell. EIS accommodates variable geometry and illumination during rapid, low-altitude flybys with both framing and pushbroom imaging capability using rapid-readout, 8-megapixel (4k × 2k) detectors. Color observations are acquired using pushbroom imaging with up to six broadband filters. The data processing units (DPUs) perform digital time delay integration (TDI) to enhance signal-to-noise ratios and use readout strategies to measure and correct spacecraft jitter. The NAC has a 2.3° × 1.2° field of view (FOV) with a 10-μrad instantaneous FOV (IFOV), thus achieving 0.5-m pixel scale over a swath that is 2 km wide and several km long from a range of 50 km. The NAC is mounted on a 2-axis gimbal, ±30° cross- and along-track, that enables independent targeting and near-global (≥90%) mapping of Europa at ≤100-m pixel scale (to date, only ∼15% of Europa has been imaged at ≤900 m/pixel), as well as stereo imaging from as close as 50-km altitude to generate digital terrain models (DTMs) with ≤4-m ground sample distance (GSD) and ≤0.5-m vertical precision. The NAC will also perform observations at long range to search for potential erupting plumes, achieving 10-km pixel scale at a distance of one million kilometers. The WAC has a 48° × 24° FOV with a 218-μrad IFOV, achieving 11-m pixel scale at the center of a 44-km-wide swath from a range of 50 km, and generating DTMs with 32-m GSD and ≤4-m vertical precision. The WAC is designed to acquire three-line pushbroom stereo and color swaths along flyby ground-tracks.
Space Science Reviews↗
Onset of aftershocks: Constraints on the Rate-and-State model
Aftershock rates typically decay with time t after the mainshock according to the Omori–Utsu law, R (t)=K(c+t) −p , with parameters K , c , and p . The rate‐and‐state (RS) model, which is currently the most popular physics‐based seismicity model, also predicts an Omori–Utsu decay with p = 1 and a c ‐value that depends on the size of the coseismic stress change. Because the mainshock‐induced stresses strongly vary in space, the c ‐value should vary accordingly. Short‐time aftershock incompleteness (STAI) in earthquake catalogs has prevented a detailed test of this prediction so far, but the newly developed a ‐positive method for reconstructing the true earthquake rate now allows its testing. Using previously published slip models, we calculate the coseismic stress changes for the six largest mainshocks in Southern California in recent decades and estimate the maximum shear as a scalar proxy of the coseismic stress tensor. Aftershock rates reconstructed for events in different stress ranges show that the rates follow a power law with p = 1 independent of stress with no clear sign of a c ‐value. The onset of the power‐law decay is abrupt and more delayed in areas with smaller stress changes. The observations do not necessarily contradict the RS model, as STAI limits the resolution for early aftershocks, and the RS model can reproduce the observations for specific Aσ values. However, the observations lead to strong constraints, namely Aσ <10 kPa and a power‐law decay of the background rate with distance to the fault, with exponent 2.7.
Seismological Research Letters↗
Light-dependent activity of deepwater sculpin (Myoxocephalus thompsonii) across substrates with and without predation risk
Light availability strongly influences predator–prey interactions in deepwater ecosystems, where visual constraints shape both foraging success and prey behavior. The behavioral response of deepwater sculpin ( Myoxocephalus thompsonii ) to siscowet lake trout ( Salvelinus namaycush siscowet ) was studied under ecologically relevant light intensities spanning several orders of magnitude typical of daytime downwelling light in the 20–100 m depth range of Lake Superior. Trials were conducted over varying substrates (gravel, sand, and black fabric). Deepwater sculpin showed a significant preference for gravel over sand and black fabric. In the absence of siscowet, sculpin movement frequency increased as light intensity decreased. Sculpin reaction distance to siscowet was influenced by both light and substrate. Reaction distance was shortest at low light intensities, peaked at intermediate light intensities (3.05 × 10⁹ to < 6.0 × 10⁹ photons m⁻2 s⁻1), and declined again at the highest light intensities tested. In the presence of siscowet, sculpin activity was suppressed at the upper end of the light range (≥ 6.0 × 10⁹ photons m⁻2 s⁻1). The greatest increase in movement occurred between 6.0 × 10⁹ and 3.05 × 10⁹ photons m⁻2 s⁻1, which corresponded to the range where siscowet prey capture declined, suggesting sculpin exploit this low-light window to move with reduced risk. Reduced activity in the presence of predators is common among cryptic species, and our findings suggest that sculpin restrict movement at higher light levels to avoid detection by siscowet.
Wisconsin↗
Surface variable‐based machine learning for scalable arsenic prediction in undersampled areas
In the United States, private wells are not federally regulated, and many households do not test for Arsenic (As). Chronic exposure is linked with multiple health outcomes, and risk can change sharply over short distances and with well depth. Coarse maps or sparse sampling often miss exceedances. Most existing models operate at ∼1 km resolution and use groundwater chemistry or detailed geologic logs, which limits their use in undersampled areas where improved guidance is most needed. We overcome these limitations by developing a machine learning model for Minnesota, USA, that predicts As exposure risk using only surficial variables from remote sensing and global data sets. Variables related to surface water hydrology and geomorphology are selected based on mechanistic links that control redox conditions and As mobilization. Local training was essential, and surficial geology variables that are more sensitive to local conditions were needed to maximize model accuracy. The resulting complete model was sufficiently sensitive to generate accurate and detailed risk maps and depth profiles of As concentrations above the 10 μg/L maximum contaminant level. Accuracy depended on local training data density. We identified a training data density of 0.07 wells/km 2 as a practical target for stable county-level performance. Maps of exceedance probabilities highlight priority areas for testing that are particularly important in rural communities that have received less sampling. These results support public health action by guiding where to install wells and where to test them, how much new sampling is needed, and where treatment outreach is most urgent.
Minnesota↗
Assessment of undiscovered conventional oil and gas resources in the Norphlet Formation, U.S. Gulf Coast region, 2023
Using a geology-based assessment methodology, the U.S. Geological Survey (USGS) estimated undiscovered, technically recoverable mean resources of 16 million barrels of oil and 348 billion cubic feet of gas in conventional reservoirs of the Norphlet Formation in the U.S. Gulf Coast region.
Alabama, Florida, Mississippi↗