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USGS2025

Introduction to the special section on improving measurements of earthquake source parameters

Earthquake source parameters such as magnitude, seismic moment, source dimension, stress drop, and radiated energy are fundamental to understanding earthquake physics, and are also key ingredients in earthquake ground‐motion modeling, rupture simulation, and statistical seismology. However, the uncertainties in these parameters estimated from the radiated seismic wavefield are large due to variability in approaches, including site and attenuation characterizations, and so estimates for an individual earthquake made by different studies can vary greatly. Estimating spectral source parameters remains a popular topic, due to a combination of their intrinsic importance and their apparent ease of measurement, but also a controversial one, due to many sources of variability and large uncertainty. The available methods coupled with necessary parameter choices and assumptions in the analysis make it challenging for researchers to apply methods or understand the reliability in results or reported source parameters. This Special Section on Improving Measurements of Earthquake Source Parameters showcasing comparisons between methods and studies seeks to alleviate some of these difficulties to help the community identify the important components and trade‐offs of decomposing recorded seismograms into their source, path, and site components.

Bulletin of the Seismological Society of America↗
USGS2000

Quality assurance and analysis of water levels in wells on Pahute Mesa and vicinity, Nevada Test Site, Nye County, Nevada

Periodic and continual water-level data from 1963 to 1998 were compiled and quality assured for 65 observation wells on Pahute Mesa and vicinity, Nye County, Nevada. As part of the quality assurance of all water levels, ancillary data pertinent to computing hydraulic heads in wells were compiled and analyzed. Quality-assured water levels that were not necessarily in error but which did not represent static heads in the regional aquifer system, or required some other qualification, were flagged. Water levels flagged include those recovering from recent pumping or well construction, water levels affected by nuclear tests, and measurements affected by borehole deviations. A cursory examination of about 30 wells with available water-level and down-hole temperature data indicate that water levels in most wells on Pahute Mesa would not be significantly affected by temperature if corrected to 95 degrees Fahrenheit. Wells with large corrections (greater than 10 feet) are those with long water columns (greater than 1,500 feet of water above the assumed point of inflow) in combination with mean water-column temperatures exceeding 105 degrees Fahrenheit. Water-level fluctuations in wells on Pahute Mesa are caused by several factors including infiltration of precipitation, barometric pressure, Earth tides, ground-water pumpage, and seismic events caused by tectonic activity and underground nuclear testing. No observed water-level fluctuations were attributed to a naturally occurring earthquake. The magnitude and duration of changes in water levels caused by nuclear tests are affected by the test size and the distance from a well to the test. Identifying water levels that might be affected by past nuclear tests is difficult because pre-testing water-level data are sparse. Hydrologically significant trends were found in 13 of 25 wells with multiple years of water-level record. The largest change in water levels (1,029 feet in 25 years) occurred in well U-19v PS 1D as a result of the Almendro nuclear test. Likely explanations for trends in most of the wells are either changes in precipitation patterns that affect recharge rates to the ground-water system, pumping effects from water-supply well U-20 WW, or a combination of these two factors.

Nevada↗
USGS2026

The U.S. Geological Survey 2025 Puerto Rico and U.S. Virgin Islands time-independent earthquake rupture forecast

We present the 2025 U.S. Geological Survey Puerto Rico and U.S. Virgin Islands (PRVI) time‐independent earthquake rupture forecast (ERF), developed for the 2025 update to the National Seismic Hazard Model (NSHM) for PRVI. The updated ERF improves upon a prior model from 2003, including an expanded fault inventory with slip‐rate estimates, updated seismicity catalogs, and refined subduction zone geometries and deformation models. It applies the fault‐system inversion methodology to solve for rates of ruptures on modeled faults, adapted from the 2023 NSHM (NSHM23) for the western United States, including the first application of the inversion to model rates on a U.S. subduction interface. Off‐fault and intraslab seismicity are constrained by observed seismicity and use updated methods developed for NSHM23. Uncertainties in model components are substantial, and the ERF represents epistemic uncertainties through a comprehensive logic tree consisting of 1.7 billion logic‐tree branches combined across all sources.

Puerto Rico, U.S. Virgin Islands↗
USGS1993

Geochemistry of and radioactivity in ground water of the Highland Rim and Central Basin aquifer systems, Hickman and Maury counties, Tennessee

A reconnaissance of the geochemistry of and radioactivity in ground water from the Highland Rim and Central Basin aquifer systems in Hickman and Maury Counties, Tennessee, was conducted in 1989. Water in both aquifer systems typically is of the calcium or calcium magnesium bicarbonate type, but concentrations of calcium, magnesium, sodium, potassium, chloride, and sulfate are greater in water of the Central Basin system; differences in the concentrations are statistically significant. Dissolution of calcite, magnesium-calcite, dolomite, and gypsum are the primary geochemical processes controlling ground-water chemistry in both aquifer systems. Saturation-state calculations using the computer code WATEQF indicated that ground water from the Central Basin system is more saturated with respect to calcite, dolomite, and gypsum than water from the Highland Rim system. Geochemical environments within each aquifer system are somewhat different with respect to dissolution of magnesium-bearing minerals. Water samples from the Highland Rim system had a fairly constant calcium to magnesium molar ratio, implying congruent dissolution of magnesium-bearing minerals, whereas water samples from the Central Basin system had highly variable ratios, implying either incongruent dissolution or heterogeneity in soluble constituents of the aquifer matrix. Concentrations of radionuclides in water were low and not greatly different between aquifer systems. Median gross alpha activities were 0.54 picocuries per liter in water from each system; median gross beta activities were 1.1 and 2.3 picocuries per liter in water from the Highland Rim and Central Basin systems, respectively. Radon-222 concentrations were 559 and 422 picocuries per liter, respectively. Concentrations of gross alpha and radium in all samples were substantially less than Tennessee’s maximum permissible levels for community water-supply systems. The data indicated no relations between concentrations of dissolved radionuclides (uranium, radium-226, radium-228, radon-222, gross alpha, and gross beta) and any key indicators of water chemistry, except in water from the Highland Rim system, in which radon-222 was moderately related to pH and weakly related to dissolved magnesium. The only relation among radiochemical constituents indicated by the data was between radium-226 and gross alpha activity; this relation was indicated for water from both aquifer systems.

Tennessee↗
USGS2024

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

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

International Journal of Coal Geology↗
USGS2026

Extreme precipitation variability and soil texture controls on water-table response

Extreme precipitation events (EPEs), a key class of hydrometeorological extremes, are intensifying globally under climate change; however, their effects on water-table dynamics across varying soil textures remain poorly understood. To better understand the impacts of EPEs, we conducted one-dimensional modeling to evaluate water-table response time, displacement, recession time, and total recharge under EPEs of 0.20 m, 0.40 m, and 0.60 m amounts, applied over 1-, 7-, and 20-day durations across twelve soil textures. The results show that coarse soils (i.e., sand) respond within days, while fine soils (i.e., clay) may take over 200 days. Water-table displacement ranged from 0.30 to 1.64 m and increased with EPE magnitude. The time it took for water tables to recede ranged from 1.2 to 3.0 years. A first-order estimate of total possible recharge, calculated from porosity and displacement, ranged from 17% (clay) to 97% (sand), averaging ~63% across soil textures. These findings highlight that recharge is primarily governed by EPE magnitude and soil properties, not event duration. This modeling effort provides new insight into how soil texture modulates groundwater response to extreme precipitation, informing future water budget and resilience assessments.

Water↗
USGS2025

Preventing overfitting when using tree-based methods for mapping hydrothermal favorability

Ensemble tree-based algorithms are robust tools for estimating sparsely distributed resources with non-linear dependencies (e.g., hydrothermal systems). These algorithms naturally accommodate the threshold conditions necessary to enable and support hydrothermal systems (e.g., having sufficient heat and permeability) and are simpler than many other non-linear machine learning strategies (e.g., artificial neural networks), which is an advantage when working with few labeled examples from which to learn. In previous work, we used eXtreme Gradient Boosting (XGBoost) to produce regional prediction and uncertainty maps of hydrothermal favorability; however, recent studies suggest that, even when properly applied, XGBoost has some risk of overfitting when there are few labeled examples from which to learn. To evaluate overfitting when constructing hydrothermal favorability maps with tree-based methods, we compare XGBoost with Extremely Randomized Trees (ExtraTrees), another ensemble tree-based algorithm that has the potential to underfit when using few labeled examples. We hold all other modeling parameters constant, resulting in two contrasting favorability maps of conventional geothermal resources for the Great Basin. Our results indicate that ExtraTrees demonstrably reduces overfitting compared with XGBoost. After considering overall performance, we conclude that ExtraTrees provides a more suitable modeling approach than XGBoost for the purposes of conventional hydrothermal resource assessments.

Conference Paper↗
USGS2026

New constraints on northeast Seattle basin structure from converted seismic waves

The Seattle basin is a deep sedimentary basin in the Seattle–Bellevue, Washington metropolitan area within the Puget Lowland of Washington State. We determine the structure of a portion of the basin and the underlying basement using analysis of P waves converted from direct S incident from below. A deep local crustal event beneath Monroe, about 35 km northeast of Seattle, was recorded by a 100‐station nodal array deployed in 2019. The event produced a variety of coherent seismic phases, including converted waves from the sediment—basement boundary, internal structure within the basin, and additional crustal discontinuities. Using observed Sp converted waves, we apply an adjoint‐based full waveform inversion (FWI) method to determine the amplitude and extent of seismic discontinuities at depth. We find the strongest source of converted waves for this event lies ∼6 to 7 km depth below northern Lake Washington, interpreted to be the local depth to basement rock. The newly imaged shallow basement structure may be part of a deformation zone associated with the Siletzia eastern boundary. Our results highlight the utility of converted seismic waves recorded by a dense array, combined with an FWI method, to illuminate crustal structure.

Washington↗
USGS2025

Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model

Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the state of Alabama in the southeast of the United States. By integrating dynamic data such as streamflow and groundwater levels with static catchment attributes, the machine learning model identifies primary drivers of nitrate fluctuations, offering detailed insights into the complex interactions affecting multi-year nitrate concentrations in natural aquatic systems. In addition, a novel LSTM-based approach utilizes synthetic surface water nitrate data to predict groundwater nitrate levels, helping to address monitoring gaps in aquifers connected to these rivers. This method reveals potential correlations between surface water and groundwater nitrate dynamics, which is particularly meaningful given the lack of water quality observations in many aquifers. Field applications further show that, while the LSTM model effectively captures seasonal trends, limitations in representing extreme nitrate events suggest areas for further refinement. These findings contribute to data-driven water quality management, enhancing understanding of nitrate behavior in interconnected water systems.

Alabama↗
USGS2025

Hydrogeology and groundwater quality in the Snake River alluvial aquifer at Jackson Hole Airport, Wyoming, 2011–20

The Snake River alluvial aquifer underlying the Jackson Hole Airport (JHA) in northwest Wyoming is an important source of water used for domestic, commercial, and irrigation purposes by the airport and nearby residents. The U.S. Geological Survey, in response to previously identified water-quality concerns in the area, monitored and evaluated changes in hydrogeologic characteristics and groundwater-quality conditions of the alluvial aquifer during 2011–20. During that period, the Jackson Hole Airport made several changes that potentially improved water quality at and downgradient from the airport. Well, water level, and hydrogeologic data were collected from the alluvial aquifer to identify hydrogeologic characteristic and groundwater quality changes. Additionally, results of statistical tests were applied to water-quality results to evaluate trends in selected physical properties and constituent concentrations with time. The trends of those data show that water quality did improve overall during the study period compared to previously collected data. Presumably, these trends are in response to the changes in the aircraft deicing/anti-icing fluid (ADAF) formulation used by the JHA, the many JHA infrastructure improvements made during 2011–20, the degradation of existing ADAFs in subsurface soils and groundwater, or some combination of these possibilities.

Wyoming↗
USGS2026

Rapid earthquake magnitude classification via P-wave strains from borehole strainmeters and Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) offers a promising approach for earthquake early warning (EEW) in settings where seismic networks are costly to maintain. By repurposing fiber-optic cables as dense strainmeter arrays, DAS enables real-time earthquake detection wherever those fibers are accessible. However, poor azimuthal coverage and challenges in estimating magnitude from strain measurements remain key hurdles in applying for earthquake monitoring. Here, we develop a machine learning method to distinguish large (M≥5.4) earthquakes from smaller ones within the first 4 seconds of a strain waveform after a P-wave arrival without determining location. Using ensemble decision tree models trained on borehole strainmeter data (3.5≤M≤7.1) and tested on onshore DAS waveforms (including the 2024 M7 Offshore Cape Mendocino earthquake), we find that low-frequency (0.2–0.5 Hz) continuous wavelet transform coefficients are the strongest predictors of magnitude, in addition to strain amplitude. Both DAS and borehole strainmeters effectively capture long-period strain signals, making these findings valuable for EEW systems. Our method shows high precision compared to the real-time EEW system, ShakeAlert®, supporting the position that DAS is a viable technology for earthquake monitoring and magnitude classification.

California↗
USGS2026

Testing characteristic magnitude distributions in modern PSHA models

The characteristic magnitude distribution hypothesis predicts a higher rate of large earthquakes than a Gutenberg–Richter extrapolation of the small‐earthquake rate would imply. Characteristic magnitude distributions have been commonly applied to faults in probabilistic seismic hazard analysis (PSHA), and in modern models they can emerge from the way short‐term seismicity constraints are combined with long‐term geologic and geodetic constraints. We test the characteristic magnitude distribution hypothesis by comparing the fault‐based magnitude distributions from the 2023 update to the National Seismic Hazard Model (NSHM23) in the Western United States with observed seismicity over the past 93 yr. We find that observed magnitude distributions fall outside the model‐predicted confidence bounds in regions where NSHM23 produces characteristic magnitude distributions: in these regions, the model predicts higher rates of large earthquakes than are observed. An analysis of the earlier California model (Uniform California Earthquake Rupture Forecast, version 3) also reveals discrepancies between the modeled and observed magnitude distributions. In addition, we find that observed magnitude distributions near modeled faults are not significantly different from those in background regions. These results challenge the prevalence of characteristic magnitude distributions in fault‐based seismic hazard models and call for a reassessment of how disparate data sets are integrated in PSHA.

western United States↗
USGS2026

Variability and consistency in wildfire susceptibility: Insights from a national compilation

Background Wildfire risk in the United States is rising and remains a land management priority. The quantitative wildfire risk assessment (QWRA) framework integrates fuels, topography, weather and values at risk to estimate the potential change in value from wildfire. Within this, response functions (RFs) represent how values respond to fire intensity. These are often based on expert judgment, but variation across assessments is unclear. Aims This study uses data from the US Geological Survey (USGS) Wildfire Hazard and Risk Assessment Clearinghouse to characterize consistency and variation across categories and contexts. Methods We applied descriptive statistics to summarize RFs, using tables, box-and-whisker plots and heat maps stratified by highly valued resource or asset (HVRA) category and spatial scale. Key results RFs and value definitions vary, especially for ecosystem-related resources. Some functions, such as for buildings in the wildland–urban interface (WUI), translate well across contexts, while others require more input. Conclusions Some functions are broadly transferable, while others need customization. This analysis provides references and starting points for improvement to RFs in QWRAs. Interpretations Expanding the clearinghouse and dataset and building more transparency in expert elicitation can build trust among communities, agencies and end-users, and can support efficient use of limited resources to mitigate wildfire risk.

International Journal of Wildland Fire↗
USGS2024

Classification of lakebed geologic substrate in autonomously collected benthic imagery using machine learning

Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images.

Michigan, Wisconsin↗
USGS2025

A method to obtain remotely sensed grain size distributions from nonplanar granular deposits

Constraining the grain size distribution of granular deposits with complex surfaces is difficult with existing approaches. Field and laboratory techniques are time consuming and limited by the maximum grain size that laboratories can accommodate. In this study, we present a new method to identify the coarse fraction of the grain size distribution at a debris-flow fan deposit surveyed with terrestrial laser scanning (TLS) in Glenwood Canyon, Colorado, USA. This method is a novel grain segmentation algorithm developed for application to point cloud data of deposits with complex surfaces and angular grains ranging in size from centimeters to a meter. This approach combines an existing random forest machine learning method with a novel iterative clustering algorithm. We compared the grain size distribution from our algorithm with a Wolman pebble count conducted in the field, and found a root mean squared error of less than 2 cm from the 5th to 95th percentile of the grain size distribution of grains ranging from cobble to boulder sized (6.3–78 cm in our application). Finally, we compared our new algorithm with an existing open-source grain segregation algorithm, and our method outperformed the selected alternative when applied to the debris-flow deposit point cloud.

Colorado↗
USGS2025

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↗
USGS2025

Geochemical processes related to mined, milled, or natural metal deposits in a rapidly changing global environment

The demand for metals and raw materials, such as nickel and copper, has been projected to expand in the coming decades, driven by the global energy transition, the need for green technologies, and expanding infrastructure. Consequently, the increasing extraction and production of mining waste can have adverse impacts on surrounding environments and human health. The aim of this thematic collection is to fill critical knowledge gaps in the present-day cycles of metal(loid)s from source to larger sinks, and the effect of environmental management, anthropogenic development, and climate change. Altogether, the studies have been conducted in different natural settings around the world and comprise investigations in laterites, a soil-medicinal plant system, watersheds, and banded iron formations, among others. The geochemical applications in tracing mineralization, its secondary products, and/or potential impact on the immediate environment are highly diverse with applied tools ranging from isotope tracers to major and trace element systematics. Particularly the use of rare earth elements, their patterns and anomalies are methods employed by several studies in this collection. We summarize the findings to offer a potential future direction for the use of geochemical tracing techniques in resource exploration in the context of climate change and environmental challenges.

Geochemistry: Exploration, Environment, Analysis↗
USGS2026

Structural evolution of iron coordination in proteins across Earth’s oxygenation history

Protein metal-binding sites support essential biological functions shaped by protein fold, subunit interactions, and cofactor chemistry. Because these sites encode both biochemical function and environmental constraint, they offer a route to connect protein evolution with changes in Earth’s surface environment through time. Of particular interest is iron (Fe), the most widely used metal in biology and a cofactor central to both anaerobic and aerobic metabolism. Here, we systematically compare the immediate chemical environments of functionally essential Fe-binding sites in three-dimensional protein structures to test whether Fe coordination spheres differ across oxygen contexts. Using a curated dataset of experimentally determined structures, we identify a clear shift in the local chemistry of Fe-binding environments from anaerobic to aerobic proteins. Aerobic Fe sites are significantly more hydrophilic than anaerobic ones, and amino-acid composition analyses show reduced cysteine use in aerobic Fe-binding neighborhoods. These patterns suggest that as Earth’s surface environments became more oxygenated, proteins retained Fe as a core redox metal while reconfiguring local coordination chemistry in ways less vulnerable to oxidative damage. More broadly, this study introduces and applies the Coordination Sphere Analysis and Comparison (CSAC) workflow, an open and archived Python workflow for extracting local metal-binding environments from structure datasets, providing a framework for linking metalloprotein structure to evolutionary and geobiological transitions across Earth history.

Discover Life↗