USGS Science⌕ Search

SEARCH · USGS Science

Results for “The ISME Journal”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

1,663 records · Page 61Linked to original sources
USGS2025

No evidence for an active margin-spanning megasplay fault at the Cascadia Subduction Zone

It has been previously proposed that a megasplay fault within the Cascadia accretionary wedge, spanning from offshore Vancouver Island to Oregon, has the potential to slip during a future Cascadia subduction zone earthquake. This hypothetical fault has major implications for tsunami size and arrival times and is included in disaster-planning scenarios currently in use in the region. This hypothesis is evaluated in this study using CASIE21 deep-penetrating and U.S. Geological Survey high-resolution seismic reflection profiles. We map changes in wedge structural style and seismic character to identify the inner-outer wedge transition zone where a megasplay fault has been previously hypothesized to exist and evaluate evidence for active faulting within this zone. Our results indicate that there is not an active, through-going megasplay fault in Cascadia, but instead, the structure and activity of faulting at the inner-outer wedge transition zone is highly variable and segmented along strike, consistent with the segmentation of other physical and mechanical properties in Cascadia. Wedge sedimentation, plate dip, and subducting topography are proposed to play a major role in controlling megasplay fault development and evolution. Incorporating updated megasplay fault location, geometry, and activity into modeling of Cascadia earthquakes and tsunamis could help better constrain associated hazards.

British Columbia, Oregon, Washington↗
USGS2024

U.S. Geological Survey Groundwater Climate Response Network—2023

As of October 2023, the U.S. Geological Survey (USGS) operated more than 660 sites across the United States and its territories as part of the Groundwater Climate Response Network (CRN). The CRN is comprised of wells and springs selected to monitor the effects of climate variability, such as droughts, on groundwater levels and spring discharge nationwide. The CRN includes more than 550 locations with realtime data and more than 100 sites with non-real-time data available to the public on the CRN web mapper and the USGS National Water Dashboard.

General Information Product↗
USGS2024

Application of non-stationary shear-wave velocity randomization approach to predict 1D seismic site response and its variability at two downhole array recordings

Accounting for uncertainties in seismic site response is crucial to improving the performance of one-dimensional (1D) ground response analyses (GRAs) at downhole array recording sites. In addition to site effects, uncertainties in 1D-GRAs can also be contributed from the seismic source and/or path. Though often representing not more than one percent of the distance (path) from the source, site conditions are known to have an enormous influence on ground shaking. In this study, we focus on the site shear-wave velocity ( V S ) structure, which is the main ingredient for estimating the variability of site response. As such, V S can manifest aleatory uncertainties related to the effects of small-scale spatial heterogeneities within the near surface, thus V S can substantially modify ground shaking during earthquakes. We apply a novel V S randomization approach to propagate the small-scale heterogeneities of V S to estimate seismic site response within a non-stationary probabilistic framework. The randomization approach generates samples of V S profiles that are used to perform several 1D-GRAs and obtain an averaged site response and related variability. The proposed method is implemented on data recorded at two downhole array sites with different subsurface soil conditions: a soft soil site on Treasure Island (California, United States of America) and a rock outcrop site in Cadarache (South-East France). We show that synthetic surface-to-borehole transfer functions from 1D-GRAs provide an acceptable fit to the empirical transfer functions from low-motion earthquake records and succeed in reproducing most of the site-specific seismic response variability. The remaining mismatch between transfer functions is likely due to insufficient precision on the seismic bedrock and the impedance contrast. The variability in site response is discussed with emphasis on the role of V S small-scale heterogeneities, attenuation, and input motion incidence angle in ground motion variability for the site and soil conditions at both locations.

California↗
USGS2024

Neogene hydrothermal Fe- and Mn-oxide mineralization of Paleozoic continental rocks, Amerasia Basin, Arctic Ocean

Rocks dredged from water depths of 1,605, 2,500, 3,300, and 3,400 m in the Arctic Ocean included Paleozoic continental rocks pervasively mineralized during the Neogene by hydrothermal Fe and Mn oxides. Samples were recovered in three dredge hauls from the Chukchi Borderland and one from Mendeleev Ridge north of Alaska and eastern Siberia, respectively. Many of the rocks were so pervasively altered that the protolith could not be identified, while others had volcanic, plutonic, and metamorphic protoliths. The mineralized rocks were cemented and partly to wholly replaced by the hydrothermal oxides. The Amerasia Basin, where the Chukchi Borderland and Mendeleev Ridge occur, supports a series of faults and fractures that serve as major zones of crustal weakness. We propose that the stratabound hydrothermal deposits formed through the flux of hydrothermal fluids along Paleozoic and Mesozoic faults related to block faulting along a rifted margin during minor episodes of Neogene tectonism and were later exposed at the seafloor through slumping or other gravity processes. Tectonically driven hydrothermal circulation most likely facilitated the pervasive mineralization along fault surfaces via frictional heating, hydrofracturing brecciation, and low- to moderate temperature Fe- and Mn-rich hydrothermal fluids, which mineralized the crushed, altered, and brecciated rocks.

Geochemistry, Geophysics, Geosystems↗
USGS2024

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews↗
USGS2025

The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought

Quantitative understanding is lacking on how the depth of active groundwater circulation in bedrock affects mountain streamflow response to a multi‐year drought. We use an integrated hydrological model to explore the sensitivity of a variety of streamflow metrics to bedrock circulation depth and porosity under a plausible extreme drought scenario lasting up to 5 years. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. With drought, a deeper circulation system with higher drainable porosity more effectively buffers minimum flow and significantly limits perennial stream loss in comparison to a shallow circulation system. Streamflow buffering is accomplished through extensive groundwater storage loss. However, deeper circulation systems experience prolonged recovery from drought in comparison to storage‐limited shallow systems. Research highlights the importance of characterizing the deeper bedrock hydrogeology in mountainous watersheds to better understand and predict drought impacts on stream ecosystem health and water resource sustainability.

Colorado↗
USGS2024

Uncertainty and spatial correlation in station measurements for mb magnitude estimation

The body‐wave magnitude (⁠⁠) is a long‐standing network‐averaged, amplitude‐based magnitude used to estimate the magnitude of seismic sources from teleseismic observations. The U.S. Geological Survey National Earthquake Information Center (NEIC) relies on in its global real‐time earthquake monitoring mission. Although waveform modeling‐based moment magnitudes are the modern standard to characterize earthquake size, is important because (1) in many cases, waveform modeling is not possible (e.g., low signal‐to‐noise events), (2) is applicable over a broad range of magnitudes, ∼M 4–7, and (3) there is a many decades‐long history of estimating magnitudes. We use the NEIC Preliminary Determination of Epicenters earthquake catalog to investigate the uncertainty in NEIC station measurements. We show that measurements are spatially correlated, which can bias event ⁠, and we describe an empirical relation between this spatial correlation and station‐to‐station distance. We further describe an approach to mitigate bias from the spatial correlation. Accounting for the spatial covariance of observations can change the event from −0.15 to 0.07 units (10th to 90th percentile) for smaller events (⁠⁠). These smaller events have the largest standard deviations ranging from 0.05 to 0.15 units (10th to 90th percentile).

The Seismic Record↗
USGS2024

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

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

Oregon, Washington↗
USGS2023

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↗
USGS1999

Streamflow, base flow, and ground-water recharge in the Housatonic River basin, western Massachusetts and parts of eastern New York and northwestern Connecticut

Streamflows for selected flow durations from 1 to 99 percent and the August median streamflows were estimated for 11 long-term streamflow-gaging stations in and near the study area. Estimates of streamflow and associated standard errors were determined for selected flow durations from 50 to 99 percent and the August median streamflows for 21 low-flow partial-record stations and for selected flow durations from 1 to 99 percent and the August median streamflows for two partial-record stations and seven short-term discontinued streamflow-gaging stations. Median streamflows per square mile for the 10-, 50-, and 90-percent flow durations and the August median streamflows were 3.90, 1.01, 0.185, and 0.248 cubic feet per second per square mile. Streamflows per square mile at selected flow-duration discharges between 1 and 99 percent at the 41 stations were related to basin characteristics to explain differences in streamflow characteristics. Basin characteristics included basin elevations, extent of stratified-drift deposits, land use, aspect, and underlying bedrock geology types. Most streamflow differences were positively correlated to basin elevation differences, most likely because precipitation increases with elevation, and to stratified-drift deposits, which allow more precipitation to recharge the ground water and to discharge later than do till and bedrock deposits. Mean base flow was computed from continuous records of daily mean discharge at 11 long-term streamflow-gaging stations in and near the study area. Mean annual base flow ranged from 13.4 to 24.5 inches per year. Minimum annual base flow ranged from 45 to 72 percent of mean annual rates at the 11 long-term stations, and the ratio of base flow to streamflow (base-flow index) ranged from 0.55 to 0.80. Base-flow durations between 1 and 99 percent were calculated from streamflow records at the 11 long-term streamflow-gaging stations. Base flow accounted for 45.5 to 85.0 percent of total annual streamflow at the 1- and 99-percent flow durations. Ground-water-recharge rates were computed from continuous records of daily mean discharge at 11 long-term streamflow-gaging stations in and near the study area. Mean annual ground-water-recharge rates ranged from 17.5 to 22.4 inches per year at 10 of the 11 long-term stations. Mean annual ground-water-recharge rates ranged from 2 to 7 inches per year higher than base flow. Minimum annual ground-water-recharge rates ranged from 48 to 72 percent of mean annual ground-water-recharge rates. Mean annual potential ground-water recharge was estimated from monthly climatological data collected at six climatological stations in and near the study area. Mean potential ground-water recharge ranged from about 17.9 to 28.9 inches per year, with a median value of 22.6 inches per year. This median value compares well to that calculated by use of streamflow records at the 11 streamflow-gaging stations (20.0 inches per year). Streamflows per square mile for the 10-, 50-, and 90-percent flow durations at stations in and near the study area were similar to those computed for other unregulated long-term continuous streamflow-gaging stations in central and eastern Massachusetts. Base-flow and ground-water-recharge rates in the study area compared closely to results from other studies in southeastern Massachusetts and Rhode Island, which were based on the same computational methods.

Connecticut, Massachusetts, New York↗
USGS2025

Detecting hidden sedimentary geothermal systems in the Upper Colorado River Basin

Geothermal resources exist in sedimentary rock where circulation of water for efficient extraction or storage of heat is possible. Except in rare instances where hot water is expressed at the land surface, sedimentary geothermal resources are hidden, so the identification of these systems is optimally accomplished using predictive subsurface modeling. An integrated approach using detailed paleogeographic interpretations, subsurface geologic mapping, and numerical modeling has produced regional geologic and temperature models for the Upper Colorado River Basin, a large watershed in central North America that contains many sedimentary basins. These models identify areas of hidden sedimentary geothermal resource potential in low temperature (<90°C), moderate temperature (90–150°C), and high temperature (>150°C) fairways across the study area. These models incorporate maps of key horizons in outcrop and the subsurface to create a robust structural framework that can be used to target favorable geology for natural or engineered permeability. This framework is populated with lithologies derived from detailed palaeogeographical maps and over 40,000 bottom hole temperature (BHT) values were used to create a calibrated three-dimensional (3D) temperature model across the region. The resulting maps serve as a regional sedimentary geothermal play fairway screening tool for evaluating different grades of sedimentary geothermal resources and for identifying areas of interest where more detailed, prospect-scale studies can be undertaken.

Arizona, Colorado, New Mexico, Utah, Wyoming↗
USGS2008

Late Jurassic igneous rocks in south-central Arizona and north-central Sonora: Magmatic accompaniment of crustal extension

In Middle Jurassic time, the region of south-central Arizona and north-central Sonora was part of a continental magmatic arc. In early Late Jurassic time, this magmatic arc gradually gave way to a extensional regime. Late Jurassic rifting was accompanied by magmatism, typically small in volume relative to the preceding arc phase, and with mildly alkaline compositions. These igneous rocks, the 158- to 146-Ma Ko Vaya Suite, make up much or most of the Comobabi, Artesa, Quijotoa, and Brownell Mountains and Sierra del Cobre. The bimodal Ko Vaya Suite constitutes volcanic to shallow plutonic complexes comprising chiefly trachyandesite and volcanic wacke; monzodiorite; rhyolite porphyry; distinctive, compositionally and texturally heterogeneous, quartzpoor granite (the Ko Vaya Granite); A-type perthite granite and quartz syenite; aphyric intrusive rhyolite; and locally abundant hematite veins. Alteration, particularly potassic alteration, is widespread. Minimally altered samples of granite and quartz syenite have the composition of trachyte or trachydacite and alkali rhyolite. The Ko Vaya Suite is nonconformably overlain by the latest Jurassic Sand Wells Formation, approximately correlative with the Glance Conglomerate, the basal unit of the Bisbee Group. The Ko Vaya Suite is in some ways analogous to the coeval Independence dike swarm of eastern California, but less diffuse. We infer that the Ko Vaya Suite represents especially intense or focused Late Jurassic extensional magmatism.

Arizona, Sonora↗
USGS2026

Investigating the influence of climate and volcanic surface aging on fluvial erosion: A case study of Réunion Island, Indian Ocean

Precipitation is one of the dominant drivers of landscape erosion and evolution; however, the effects of typical rainfall compared with less frequent, high-magnitude precipitation events on erosion remain unclear. Volcanic islands are ideal locations to study such phenomena due to their simple geometries, nontectonic construction, and strong spatiotemporal rainfall gradients. However, spatial variation in surface age, created during their construction, often complicates their degradation histories by introducing temporal changes in erosion rates as drainage networks develop. Réunion Island (western Indian Ocean) presents a clear example of this, with an east–west gradient in both surface age and mean annual precipitation, as well as infrequent cyclones that alter the background rainfall pattern. In this study, we analyze the effects of surface age, average rainfall, and rainfall variability on basin development and fluvial erosion across the island. We calculate basin-averaged values of basin morphology, age, precipitation, river discharge, eroded volumes, and erosion rates, and use these to analyze the dominant drivers of landscape evolution through a series of correlation analyses. Our results indicate a temporal dependence on the influence of precipitation, with young surfaces being dominantly eroded by high-rainfall events and older surfaces eroded by mean annual rainfall patterns. Furthermore, we show that drainage development of shield volcanoes follows similar trends to other volcano types, and suggest that surface permeability and groundwater structure are important controls on runoff-driven erosion on shield volcanoes. These results add new components to the question of how precipitation impacts erosion.

Réunion Island↗
USGS2025

Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition

The U.S. Geological Survey (USGS), Defense Advanced Projects Research Agency (DARPA), NASA Jet Propulsion Laboratory (JPL), and MITRE ran a 12-week machine learning competition aimed at accelerating development of AI tools for critical mineral assessments. The Artificial Intelligence for Critical Mineral Assessment Competition solicited innovative solutions for two challenges: 1) automated georeferencing of historical maps, and 2) automated feature extraction from historical maps. Competitors used a new dataset of historical map images to train, validate, and evaluate their models. Automated georeferencing pipelines attained a median root-mean square error of 1.1 km. Prompt-based extraction (i.e., with user input) of polygons, polylines, and points from geologic maps yielded median F1-scores of 0.77, 0.56, 0.35, respectively. Geologic maps pose numerous challenges for AI workflows because they vary significantly. However, despite its short duration, the competition yielded promising results that have since spurred further innovation in this area and led to the development of new AI tools to semi-automate key, time-consuming parts of the assessment workflow.

Applied Computing and Geosciences↗
USGS2025

Waning greenhouse gas emissions from U.S. Federal lease coal production by the mid-21st century

This study presents estimates of future years (2024–2051) United States Federal lease coal production and the resulting greenhouse gas (GHG) emissions from the combustion, transport, and mining of that fuel. Results from the coal production estimate indicate a decline in production from Federal leases; with known production of 240 million short tons (mtn) in 2023 and a projected decline to 34.0 mtn by 2051, which represents a reduction to 14.2% of the 2023 value. In parallel with this projection, total GHG emissions are estimated to decrease from 402.2 million metric tons of carbon dioxide equivalent (MMT CO 2 eq.) in 2024 to 55.0 MMT CO 2 eq. in 2051, a decline to 13.7% of 2024 emissions estimates. The reductions in coal production and emissions are mainly the result of planned coal combustion power plant closures, with major projected closures in 2037 and 2048. However, GHG emissions estimates for future years can be uncertain as they rely heavily on coal production estimates from operators' public business plans and other publicly available resources. Forward looking plans of this type are subject to significant changes if economic and political factors deviate from current information. Results suggest that average GHG emissions over the time series breakout to 95% end point combustion, 3.7% transportation combustion emissions, and 1.3% fugitive emissions, although there is uncertainty associated with these figures. Uncertainty stemming from production projections, sector distributions, and emissions factors on the future emissions estimates increases with time, ranging from −28% to +48% within the 2024–2051 timeframe.

Alabama, Colorado, Montana, North Dakota, Utah, Wy↗
USGS2026

Wavelet Inversion for SliP (WISP): Open-source earthquake slip modeling software

Models of the spatiotemporal evolution of earthquake slip, termed finite-fault models, are a critical component of rapid earthquake and tsunami response, earthquake forecasting, seismic ground-motion estimates, and studies of earthquake kinematics. Here, we detail a newly released finite-fault modeling software, Wavelet Inversion for SliP (WISP), in use at the U.S. Geological Survey’s National Earthquake Information Center (NEIC) and available to the public. WISP version 1.1.0 allows inversion of teleseismic body and surface waves, as well as local strong-motion, static and dynamic Global Navigation Satellite System, and satellite imagery (e.g., Interferometric Synthetic Aperture Radar) observations on single or multiple planar fault segments. The software is used in NEIC rapid response of earthquakes M w ≥ 7, generally resulting in a published model within the first few hours after the event origin time. The rupture location and dimensions are then used as inputs to downstream products to estimate earthquake shaking, predict loss, and model the likelihood of secondary hazards, namely landslides and liquefaction. WISP is also used in research studies to evaluate the characteristics of complex ruptures including multifault ruptures and earthquake doublets, among others. The WISP version 1.1.0 software release is composed of Python-wrapped FORTRAN code to accomplish the inversion procedure. A simple command line interface facilitates ease of use even for those with only a cursory knowledge of Python scripting. WISP version 1.1.0 includes a Jupyter Notebook tutorial demonstrating use of the software for modeling the 2015 M w 8.3 Illapel, Chile, earthquake. In parallel with the tutorial, we demonstrate the typical usage of the WISP software using the M w 8.3 Illapel earthquake example here.

Seismological Research Letters↗
USGS2026

Hydrothermal manganese-oxide mineralization of a carbonate ooze, Samoan hotspot region, South Pacific Ocean

Low-temperature hydrothermal manganese oxides occur throughout the global oceans. However, the hydrothermal replacement of a carbonate ooze by manganese oxides is described here for the first time. The 24 samples dredged from three locations around the Territory of American Samoa in the South Pacific Ocean include six samples with remnant carbonate and volcaniclastic sediments, which we refer to as “low Mn,” and 18 samples in which the mineralization is pervasive and has replaced most original sediment, termed “high Mn.” The 18 high-Mn samples exhibit a mean Mn content of 51 wt.%. Higher Li and Mn contents and lower Fe contents in the high-Mn samples indicate a hydrothermal origin and distinguish these samples from hydrogenetic ferromanganese crusts. Mn-oxide layers are up to 90 mm thick, some with columns to 44 mm long and 10 mm wide, the magnitude of which has not been described previously. Samples are composed of birnessite and 10 Å phyllomanganate minerals. Textures of the thickest Mn layers indicate mineralization below the seabed from ascending fluids during multiple hydrothermal pulses. Mineralization took place by complete to partial replacement and cementation of foraminiferal sediments intermixed with volcaniclastic sediments in varying amounts. Our results highlight the production of carbonate sediment-hosted hydrothermal Mn oxides from multiple hydrothermal sources within the Samoan volcanic chain. The potential extensive distribution on the regional scale of this newly described mineralization process and unique element enrichments raise questions about its broader distribution globally and potential importance to hydrothermal processes, element mass balance, and seabed mineral resources.

Geochemistry, Geophysics, Geosystems↗
USGS1997

Water-quality assessment of south-central Texas: Occurrence and distribution of volatile organic compounds in surface water and ground water, 1983-94, and implications for future monitoring

The study area of the South-Central Texas study unit of the National Water-Quality Assessment Program comprises the Edwards aquifer in the San Antonio region and its catchment area. The first phase of the assessment includes evaluation of existing water-quality data for surface water and ground water, including volatile organic compounds, to determine the scope of planned monitoring. Most analyses of volatile organic compounds in surface water are from the National Pollutant Discharge Elimination System sites in San Antonio, Texas. Nine volatile organic compounds were detected at the six sites. The three compounds with the most detections at National Pollutant Discharge Elimination System sites are 1,2,4-trimethylbenzene, toluene, and xylene. Analysis of volatile organic compounds in ground water was limited to Edwards aquifer wells. Twenty-eight volatile organic compounds were detected in samples from 89 wells. The five most commonly detected compounds in samples from wells, in descending order, are tetrachloroethene, trichloroethene, bromoform, chloroform, and dibromochloromethane. Detections of volatile organic compounds in surface water and ground water within the South-Central Texas study area are limited to site-specific sources associated with development; therefore, planned monitoring for possible detections of volatile organic compounds as part of the National Water-Quality Assessment Program will emphasize areas of expanding population and development. Monitoring of volatile organic compounds is planned at National Pollutant Discharge Elimination System sites, at basic fixed surface-water sites, and in the ground-water study-unit surveys.

Texas↗