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Article: explore 974 source-linked works published from 1974 to 2026, with original documents and citations.

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Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

Sources: usgs. Collection updated 2026-09-14. Counts describe this index, not the complete source archives.

USGS2023

Slow slip detectability in seafloor pressure records offshore Alaska

In subduction zones worldwide, seafloor pressure data are used to observe tectonic deformation, particularly from megathrust earthquakes and slow slip events (SSEs). However, such measurements are also sensitive to oceanographic circulation-generated pressures over a range of frequencies that conflate with tectonic signals of interest. Using seafloor pressure and temperature data from the Alaska Amphibious Community Seismic Experiment, and sea surface height data from satellite altimetry, we evaluate the efficacy of various seasonal and oceanographic pressure signal proxy corrections and conduct synthetic tests to determine their impact on the timing and amplitude prediction of ramp-like signals typical of SSEs. We find that subtracting out the first mode of the complex empirical orthogonal functions of the pressure records on either the shelf or slope yields signal root-mean-square error (RMS) reductions up to 73% or 80%, respectively. Additional correction with proxies that exploit the depth-dependent spatial coherence of pressure records provides cumulative variance reductions up to 83% and 93%, respectively. Our detectability tests show that the timing and amplitude of synthetic SSE-like ramps can be well constrained for ramp amplitudes ≥4 cm on the shelf and ≥2 cm on the slope, using a fully automated detector. The principal limits on detectability are residual abrupt changes in pressure that occur as part of the transition to and from summer to winter conditions but are not adequately characterized by our seasonal corrections, as well as the inability to properly account for instrumental drift, which is not readily separated from the seasonal signal.

Alaska↗
USGS2023

WellSTIC: A cost-effective sensor for performing point dilution tests to measure groundwater velocity in shallow aquifers

Many individual measurement points are required to characterize groundwater velocity within an aquifer. Groundwater velocity is most commonly measured using a network of >5 cm diameter monitoring wells, which, if not already present at a site, are expensive and labor-intensive to install. Drive-point piezometers—simple, cost-effective wells that can be installed by hand—are a common tool for sampling groundwater in shallow, alluvial aquifers, but most groundwater velocity measurement techniques require equipment that is too large for these narrow (usually <2 cm inside diameter) piezometers. In this technical note, we introduce a low-cost sensor and well packer system (<$90 USD) for performing point dilution tests in narrow piezometers. Field data show that the magnitude of groundwater velocity measured with this technique agrees with velocities computed from natural gradient tracer tests. Additionally, with proper calibration, these sensors can be used to continuously monitor in-well specific conductance, either during inter-well tracer tests with saline tracers or for water quality monitoring. This system is a viable tool for rapid assessment of the magnitude of groundwater velocity in shallow aquifers.

Water Resources Research↗
USGS2023

Cursed? Why one does not simply add new data sets to supervised geothermal machine learning models

Recent advances in machine learning (ML) identifying areas favorable to hydrothermal systems indicate that the resolution of feature data remains a subject of necessary improvement before ML can reliably produce better models. Herein, we consider the value of adding new features or replacing other, low-value features with new input features in existing ML pipelines. Our previous work identified stress and seismicity as having less value than the other feature types (i.e., heat flow, distance to faults, and distance to magmatic activity) for the 2008 USGS hydrothermal energy assessment; hence, a fundamental question regards if the addition of new but partially correlated features will improve resulting models for hydrothermal favorability. Therefore, we add new maps for shear strain rate and dilation strain rate to fit logistic regression and XGBoost models, resulting in new 7-feature models that are compared to the old 5-feature models. Because these new features share a degree of correlation with the original relatively uninformative stress and seismicity features, we also consider replacement of the two lower-value features with the two new features, creating new 5-feature models. Adding the new features improves the predictive skill of the new 7-feature model over that of the old 5-feature model; albeit, that improvement is not statistically significant because the new features are correlated with the old features and, consequently, the new features do not present considerable new information. However, the new 5-feature XGBoost model has a statistically significant increase in predictive skill for known positives over the old 5-feature model at p = 0.06. This improved performance is due to the lower-dimensional feature space of the former than that of the latter. In higher-dimensional feature space, relationships between features and the presence or absence of hydrothermal systems are harder to discern (i.e., the 7-feature model likely suffers from the “curse of dimensionality”).

Geothermal Resources Council Transactions↗
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↗
USGS2022

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

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

Idaho↗
USGS2021

Critical review of mercury methylation and methylmercury demethylation rate constants in aquatic sediments for biogeochemical modeling

Mercury is a toxin that causes neurological impairments in adults, is particularly harmful for fetuses and children, and is deadly in severe cases, making it a worldwide health concern. Methylmercury (MeHg) is the environmentally relevant form of mercury (Hg) because it biomagnifies along the food chain. Methylmercury is mainly produced in aquatic sediments via methylation of inorganic Hg (Hg(II)) and transformed back via demethylation. Because transformation rates determine MeHg concentrations, quantification of methylation and demethylation rates is needed to inform management of MeHg. Published rate constants for Hg(II) methylation ( 𝑘 𝑚 ) and MeHg demethylation ( 𝑘 𝑑 ) vary greatly, stemming partly from differences in experimental methods. We conducted a comprehensive review of rate laws, evaluated published rate constants, and performed biogeochemical simulations to assess variability in reported 𝑘 𝑚 and 𝑘 𝑑 . Based on selected studies employing the same pseudo-first-order rate law and similar experimental methods, we found that 𝑘 𝑚 = 0.04 ± 0.03 d −1 is a reasonable range for wetland sediments. Over a number of environments, maximum 𝑘 𝑑 was smaller at sites without Hg source ( 𝑘 𝑑 = 0.5 d −1 ) than at sites with identified Hg source ( 𝑘 𝑑 = 1.8 d −1 ). Larger variability and higher uncertainty in 𝑘 𝑑 compared to 𝑘 𝑚 highlight the need for more research on MeHg demethylation rates. This critical review: (a) aids the design of future experimental studies of 𝑘 𝑚 and 𝑘 𝑑 ; (b) provides guidance for comparing rate constants from different studies; (c) presents a biogeochemical reaction model to assess rate constants; and (d) informs selection of 𝑘 𝑚 and 𝑘 𝑑 values from the literature for use in model simulations.

Critical Reviews in Environmental Science and Tech↗
USGS2021

Factors influencing distribution of Coccidioides immitis in soil, Washington State, 2016

Coccidioides immitis and Coccidioides posadasii are causative agents of Valley fever, a serious fungal disease endemic to regions with hot, arid climate in the United States, Mexico, and Central and South America. The environmental niche of Coccidioide s spp. is not well defined, and it remains unknown whether these fungi are primarily associated with rodents or grow as saprotrophs in soil. To better understand the environmental reservoir of these pathogens, we used a systematic soil sampling approach, quantitative PCR (qPCR), culture, whole-genome sequencing, and soil chemical analysis to identify factors associated with the presence of C. immitis at a known colonization site in Washington State linked to a human case in 2010. We found that the same strain colonized an area of over 46,000 m 2 and persisted in soil for over 6 years. No association with rodent burrows was observed, as C. immitis DNA was as likely to be detected inside rodent holes as it was in the surrounding soil. In addition, the presence of C. immitis DNA in soil was correlated with elevated levels of boron, calcium, magnesium, sodium, and silicon in soil leachates. We also observed differences in the microbial communities between C. immitis -positive and -negative soils. Our artificial soil inoculation experiments demonstrated that C. immitis can use soil as a sole source of nutrients. Taken together, these results suggest that soil parameters need to be considered when modeling the distribution of this fungus in the environment.

Washington↗
USGS2021

Geology, mineralogy, and cassiterite geochronology of the Ayawilca Zn-Pb-Ag-In-Sn-Cu deposit, Pasco, Peru

The Ayawilca deposit in Pasco, Peru, represents the most significant recent base-metal discovery in the central Andes and one of the largest undeveloped In resources globally. As of 2018, it hosts an 11.7 Mt indicated resource grading 6.9% Zn, 0.16% Pb, 15 g/t Ag, and 84 g/t In, an additional 45.0 Mt inferred resource grading 5.6% Zn, 0.23% Pb, 17 g/t Ag, and 67 g/t In, and a separate Sn-Cu-Ag inferred resource of 14.5 Mt grading 0.63% Sn, 0.21% Cu, and 18 g/t Ag. Newly obtained U–Pb dates for cassiterite by LA-ICP-MS (22.77 ± 0.41 and 23.05 ± 2.06 Ma) assign the Ayawilca deposit to the Miocene polymetallic belt of central Peru. The polymetallic mineralization occurs as up to 70-m-thick mantos hosted by carbonate rocks of the Late Triassic to Early Jurassic Pucará Group, and subordinately, as steeply dipping veins hosted by rocks of the Pucará Group and overlying Cretaceous sandstones-siltstones of the Goyllarisquizga Group. Relicts of a distal retrograde magnesian skarn and cassiterite (stage pre-A) were identified in the deepest mantos. The volumetrically most important mineralization at Ayawilca comprises a low-sulfidation assemblage (stage A) with quartz, pyrrhotite, arsenopyrite, chalcopyrite, Fe-rich sphalerite, and traces of stannite and herzenbergite. Stage A sphalerite records progressive Fe depletion, from 33 to 10 mol% FeS, which is compatible with the observed transition from low- to a subsequent intermediate-sulfidation stage (B) marked by the crystallization of abundant pyrite and marcasite. Finally, during a later intermediate-sulfidation stage (C) sphalerite (up to 11 mol% FeS), galena, native bismuth, Cu-Pb-Ag sulfosalts, siderite, Mn-Fe carbonates, kaolinite, dickite, and sericite were deposited. This paragenetic evolution shows striking similarities with that at the Cerro de Pasco Cordilleran-type polymetallic deposit, even if at Ayawilca stage C did not reach high-sulfidation conditions. The occurrence of an early retrograde skarn assemblage suggests that the manto bodies at Ayawilca formed at the transition between distal skarn and skarn-free (Cordilleran-type) carbonate-replacement mineralization. Mineral assemblages define a T- f S 2 evolutionary path close to the pyrrhotite-pyrite boundary. Buffering of hydrothermal fluids by underlying Devonian carbonaceous phyllites of the Excelsior Group imposed highly reduced conditions during stage A mineralization (log f O 2 < − 30 atm). The low f O 2 favored efficient Sn mobility during stages pre-A and A, in contrast to other known ore deposits in the polymetallic belt of central Peru, in which the occurrence of Sn minerals is minor. Subsequent cooling, progressive sealing of vein walls, and decreasing buffering potential of the host rocks promoted the shift from low- (stage A) to intermediate-sulfidation (stages B and C) states. LA-ICP-MS analyses reveal significant In contents in Fe-rich sphalerite (up to 1.7 wt%), stannite (up to 1908 ppm), and chalcopyrite (up to 1185 ppm). The highest In content was found in stage A sphalerite that precipitated along with chalcopyrite and stannite, thus pointing to the early, low-sulfidation assemblage as prospective for this high-tech metal in similar mineral systems. Indium was likely incorporated into the sphalerite crystal lattice via Cu + + In 3+ ↔ 2 Zn 2+ and (Sn, Ge) 4+ + (Ga, In) 3+ + (Cu + Ag) + ↔ 4 Zn 2+ coupled substitutions. Indium incorporation mechanisms into the stannite and chalcopyrite crystal lattices remain unclear.

Pasco↗
USGS2021

Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System

The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ET a ), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ET a , and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ET a yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ET a estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and −7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (r ah ) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agriculture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period.

Urucuia Aquifer System↗
USGS2018

Diatomite

No abstract available.

Mining Engineering↗
USGS2018

Gypsum

No abstract available.

Mining Engineering↗
USGS2018

Ball clay

No abstract available.

Mining Engineering↗
USGS2018

Fire clay

No abstract available.

Mining Engineering↗
USGS2018

Bromine

No abstract available.

Mining Engineering↗
USGS2018

Bismuth

No abstract available.

Mining Engineering↗
Compare source metadata on this page
WorkPublishedSource identifierSource
Slow slip detectability in seafloor pressure records offshore Alaska2023-02-1970262605usgs
WellSTIC: A cost-effective sensor for performing point dilution tests to measure groundwater velocity in shallow aquifers2023-01-1770279694usgs
Cursed? Why one does not simply add new data sets to supervised geothermal machine learning models202370251029usgs
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 Predictions202370251035usgs
U-Pb scheelite ages of tungsten and antimony mineralization in the Stibnite-Yellow Pine district, central Idaho2022-05-2570232606usgs
Critical review of mercury methylation and methylmercury demethylation rate constants in aquatic sediments for biogeochemical modeling2021-12-2370279519usgs
Factors influencing distribution of Coccidioides immitis in soil, Washington State, 20162021-11-0370280080usgs
Geology, mineralogy, and cassiterite geochronology of the Ayawilca Zn-Pb-Ag-In-Sn-Cu deposit, Pasco, Peru2021-09-1370280095usgs
Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System2021-02-0170269697usgs
Diatomite201870279120usgs
Gypsum201870279121usgs
Ball clay201870279126usgs
Fire clay201870279127usgs
Dimension stone201870279128usgs
Bromine201870279129usgs
Mining review201870279130usgs
Bismuth201870279132usgs
Industrial sand and gravel201870279141usgs

These are bibliographic comparisons, not experimental rankings. Follow the original document for methods and conditions.