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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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USGS1976

Transport and dispersion of fluorescent tracer particles for the dune-bed condition, Atrisco Feeder Canal near Bernalillo, New Mexico

A tracer technique in which mineral particles were coated with fluorescent dyes was used to study the rates of transport and dispersion of sediment particles`of various diameters and specific gravities for a dune-bed condition in an alluvial channel. The experiment was conducted in the Atrisco Feeder Canal near Bernalillo, N. Mex., between May 1 and July 14, 1967. A continuous point source of tracers, approximated by injections at 5- or 10-minute intervals, was maintained for 7 days so that the steady-dilution procedure could be used to calculate the transport rate of bed material. After termination of the injection process, the spatial-integration procedure was used to follow the movement of the tracers downstream and to calculate the transport rate. Samples of the bed material in transport and the accompanying tracers moving along the surface of the dune bed were obtained periodically throughout the study with the "dustpan" sampler especially designed for fluorescent tracer studies. In addition, the spatial distributions of the tracers in the dune bed were determined three times during the study by core sampling.

New Mexico↗
USGS2025

Total uncertainty quantification in inverse solutions with deep learning surrogate models

We propose an approximate Bayesian method for quantifying the total uncertainty in inverse partial differential equation (PDE) solutions obtained with machine learning surrogate models, including operator learning models. The proposed method accounts for uncertainty in the observations, PDE, and surrogate models. First, we use the surrogate model to formulate a minimization problem in the reduced space for the maximum a posteriori (MAP) inverse solution. Then, we randomize the MAP objective function and obtain samples of the posterior distribution by minimizing different realizations of the objective function. We test the proposed framework by comparing it with the iterative ensemble smoother and deep ensembling methods for a nonlinear diffusion equation with an unknown space-dependent diffusion coefficient. Among other applications, this equation describes the flow of groundwater in an unconfined aquifer. Depending on the training dataset and ensemble sizes, the proposed method provides similar or more descriptive posteriors of the parameters and states than the iterative ensemble smoother method. Deep ensembling underestimates uncertainty and provides less-informative posteriors than the other two methods. Our results show that, despite inherent uncertainty, surrogate models can be used for parameter and state estimation as an alternative to the inverse methods relying on (more accurate) numerical PDE solvers.

Journal of Computational Physics↗
USGS2025

High-precision earthquake catalog for Minto Flats fault zone, central Alaska, reveals complex and conjugate faulting

The Minto Flats fault zone (MFFZ) in central Alaska is a left‐lateral strike‐slip fault system situated between the continental‐scale right‐lateral Denali and Kaltag‐Tintina faults. The MFFZ has the potential to generate magnitude 7 earthquakes, and it hosted a magnitude 6 earthquake in 1995. It has also produced exotic events, such as very‐low‐frequency earthquakes and nucleation signals. We use network‐matched filtering and relative earthquake relocation techniques to derive a detailed catalog of earthquake locations for the MFFZ. The catalog spans from August 2014 to December 2019, a time period including 13 temporary seismic stations in the region. Our results provide the most complete catalog for the MFFZ and include deeper events, clusters of shallow seismicity, and a complex and segmented fault structure not observed in the original regional catalog. We document right‐lateral strike‐slip faulting, conjugate to the main northeast‐striking left‐lateral faults of the MFFZ. Below Nenana basin, the relocated seismicity reveals northwest‐dipping left‐lateral faults, supporting the inference that deep crustal active faulting is associated with recent basin deformation.

Alaska↗
USGS1999

Estimation of magnitude and frequency of floods for streams in Puerto Rico: New empirical models

Flood-peak discharges and frequencies are presented for 57 gaged sites in Puerto Rico for recurrence intervals ranging from 2 to 500 years. The log-Pearson Type III distribution, the methodology recommended by the United States Interagency Committee on Water Data, was used to determine the magnitude and frequency of floods at the gaged sites having 10 to 43 years of record. A technique is presented for estimating flood-peak discharges at recurrence intervals ranging from 2 to 500 years for unregulated streams in Puerto Rico with contributing drainage areas ranging from 0.83 to 208 square miles. Loglinear multiple regression analyses, using climatic and basin characteristics and peak-discharge data from the 57 gaged sites, were used to construct regression equations to transfer the magnitude and frequency information from gaged to ungaged sites. The equations have contributing drainage area, depth-to-rock, and mean annual rainfall as the basin and climatic characteristics in estimating flood peak discharges. Examples are given to show a step-by-step procedure in calculating a 100-year flood at a gaged site, an ungaged site, a site near a gaged location, and a site between two gaged sites.

Puerto Rico↗
USGS2025

Measurement of in situ-produced cosmogenic nuclides in fine-grained quartz from shale

In situ -produced 10 Be in quartz is widely used to constrain exposure ages and denudation rates, traditionally measured in sand-sized grains. Here we report a new method for isolating fine-grained quartz from shale and demonstrate its reliability for grain sizes down to single microns. Sequential dissolution tests and analyses of grain size separates show that meteoric 10 Be is eliminated and that recoil losses during cosmogenic nuclide production are balanced by implantation from other mineral grains. High-temperature combustion leads to diffusion of meteoric 10 Be into fine-grained quartz and must be used with caution.

Nuclear Instruments and Methods in Physics Researc↗
USGS2026

Lunar analog study using portable gamma-ray neutron detector: Radiochemical mapping of silicic-to-basaltic volcanic terrains in the San Francisco Volcanic Field, Arizona

Studying lunar silicic volcanism provides key insights into the Moon’s crustal evolution, magmatic processes, and volcanic history. The Lunar-VISE (Lunar Vulkan Imaging and Spectroscopy Explorer) mission will investigate the Gruithuisen domes, a unique lunar region hypothesized to have formed through silicic volcanism. Using instruments on a Firefly Aerospace lander and a Honeybee Robotics rover, Lunar-VISE will analyze mineralogy, geochemistry, and surface properties to determine the origin and evolution of the domes, with a gamma-ray and neutron spectrometer (LV-GRNS) among its payload instruments. In preparation for this mission, we conducted preliminary fieldwork using a handheld gamma-ray neutron detector with NaI(Tl) and Cs LiYCl :Ce (CLYC) scintillators. This study focuses on various rhyolitic and basaltic volcanic centers in the San Francisco Volcanic Field (SFVF) near Flagstaff, Arizona—specifically Sugarloaf Peak, Bonito Lava Flow, and Robinson Mountain, a region containing several well-characterized lunar analog sites. The SFVF was selected for this study due to its broad compositional diversity spanning basaltic to rhyolitic compositions, its well-preserved volcanic morphologies, and its extensive use in previous NASA field campaigns and astronaut training exercises, making it an ideal terrestrial laboratory for testing planetary exploration techniques. We measured natural radioactivity, specifically from potassium (K), thorium (Th), and uranium (U) and other elements in their decay chains, which serve as diagnostic tracers of magmatic differentiation and crustal evolution processes, to assess geochemical variability across compositionally diverse terrains. The detector’s sensitivity was assessed across varying concentration levels. Regionally averaged concentrations of radioisotopes were determined by gamma-ray spectroscopy at selected sites. Our measurements reveal significant compositional variations between sites, with Sugarloaf Peak (rhyolitic, silicic dome) exhibiting the highest average radioisotope concentrations (K: 3.29 ± 0.24 wt%, U: 14.35 ± 1.81 ppm, Th: 27.14 ± 2.43 ppm), while Bonito Lava Flow (K: 1.13 ± 0.08 wt%, U: 3.86 ± 0.52 ppm, Th: 7.52 ± 1.02 ppm), and Robinson Mountain (K: 1.46 ± 0.16 wt%, U: 5.59 ± 1.06 ppm, Th: 11.75 ± 1.98 ppm) show lower concentrations aligned with basaltic compositions. Gamma-ray fluxes were elevated by a factor of approximately three to four at Sugarloaf Peak relative to nearby basaltic terrains, consistent with expected geochemical differentiation patterns. Furthermore, at meter scales, proximity to geological features significantly affects measurements. Th concentrations adjacent to a cliff face at Sugarloaf’s base were 28% higher than values measured 3-5 meters away from the same feature, demonstrating localized compositional heterogeneity. Analysis of station-by-station measurements reveals that K, U, and Th concentrations show an elevation trend at Robinson Mountain and generally higher concentrations near Sugarloaf Peak’s summit, suggesting progressive magmatic differentiation and/or the presence of more evolved lithologies at higher elevations. By mapping these elements on Earth using GRNS, we aim to optimize measurement techniques for Lunar-VISE and similar rover-borne missions by demonstrating the applicability of portable GRNS instruments for planetary surface exploration in an analog environment.

Arizona↗
USGS2026

A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping

Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD. LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics. Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60% overall agreement, compared to 71.1 ± 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ± 1.22% agreement, compared to 84.6 ± 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

Remote Sensing of Environment↗
USGS2024

Perspectives on transportable array Alaska background noise levels

Background seismic noise fundamentally sets a lower bound on our ability to record signals arising from earthquakes. The background noise spectrum at a station is a combination of cultural noise, ocean-generated microseism noise, intrinsic instrument self-noise, and the sensitivity of the instrument to nonseismic noise sources. The USArray-Transportable Array Alaska deployed 195 stations across Alaska and parts of Canada (Yukon, British Columbia, and Northwest Territories). These stations were all installed using similar techniques and made use of instruments with similar self-noise levels. As such, this network provides an opportunity to look at how geographic location influences seismic background. Using these broadband stations, we report background noise levels from 0.2 to 75 s period in six discrete bands. By constructing “noise maps,” we depict both spatial and temporal changes in the background noise field. Using these maps, combined with targeted analysis, we infer sources and contributing factors to noise levels in these different period bands. These include cultural noise, the formation of sea ice, seasonal changes in permafrost and wave activity in the Gulf of Alaska, and magnetic field variability. We use this study as an opportunity to review several previous studies examining seismic noise in Arctic regions.

Book chapter↗
USGS2026

Detecting snow avalanche activity using infrasound: Hooker Valley, New Zealand

Snow avalanches pose considerable hazards to people and infrastructure in alpine environments. Traditional avalanche monitoring relies on meteorological data and visual observations, which can be limited in scope and timeliness. Infrasound offers a promising complementary monitoring tool by detecting the low-frequency sound waves generated by avalanches. Here, we present infrasound and camera observations during a 50-day field campaign in the Hooker Valley of Aoraki/Mount Cook National Park, New Zealand. Our study detected seven avalanches with the cameras, whereas the infrasound system identified only one of these events, which was the largest and occurred under conditions that likely favoured infrasound propagation. The infrasound system recorded numerous other events not captured by the cameras, indicating the benefit of further investigation to determine their sources. These findings highlight the potential of infrasound technology for detecting avalanches and providing broad spatial coverage, capturing events in areas not monitored by cameras, while also showcasing limitations in infrasound capabilities. The limited detection of smaller avalanches underscores the opportunity for further research to enhance detection capabilities and understand environmental influences such as snow cover and wind noise. Overall, this study emphasises the utility of multidisciplinary monitoring techniques to improve avalanche detection in alpine environments.

Hooker Valley↗
USGS2025

An unexplained tsunami: Was there megathrust slip during the 2020 Mw7.6 Sand Point, Alaska, earthquake?

On October 19, 2020, the M w 7.6 Sand Point earthquake struck south of the Shumagin Islands in Alaska. Moment tensors indicate the earthquake was primarily strike-slip, yet the event produced an enigmatic tsunami that was larger and more widespread than expected for an earthquake of that magnitude and mechanism. Using a suite of hydrodynamic, seismic, and geodetic modeling techniques, we explore plausible causes of the tsunami. We find that strike-slip models consistent with the moment tensor orientation cannot produce the observed tsunami. Hydrodynamic inversion of sea surface deformation from deep ocean and tide gauge data suggest seafloor deformation more closely matches a megathrust, rather than a strike-slip, source. Static slip inversions, using sea level and Global Navigation Satellite System data, allow for a portion of co-seismic megathrust slip that can explain tsunamigenesis. Combining all available geophysical datasets to model the kinematic rupture, we show that considerable, relatively slow, megathrust slip is allowable in the Shumagin segment, concurrent with strike-slip faulting. We hypothesize that the slow megathrust rupture does not contribute much seismic radiation allowing it to previously go unnoticed with traditional seismic monitoring.

Alaska↗
USGS2026

Opportunities for the U.S. Geological Survey’s National Seismic Hazard Model to improve seismic risk assessment of critical infrastructure.

As fragility and risk modeling techniques and computational capabilities evolve, complemented by moving toward more routine and systematic seismic risk assessment of all buildings and critical infrastructure, the authors pose a few critical questions to investigate how the U.S. Geological Survey (USGS) National Seismic Hazard Models (NSHMs) can be used and enhanced further to serve such issues. In this paper, we use three examples from multiple sectors to (1) identify the role of USGS NSHMs in evaluating seismic risks to critical infrastructure, (2) quantify potential impacts from NSHM enhancements (i.e., [i] hazard curves for the vertical component of ground motion, [ii] stochastic event sets, and [iii] maps of probabilistic ground failure hazards), and (3) clarify the feasibility of relevant NSHM improvements. We illuminate that NSHMs are commonly used in location-specific performance assessments, whereas earthquake effects on critical infrastructure can be widespread across large geospatial regions. Further, we found that without the NSHM extensions considered here, risk can be severely underestimated, e.g., neglecting ground failure hazards can underestimate regional loss by a factor of two or more. Although many challenges remain, we developed example prototypes to clarify the feasibility of the NSHM extensions, which can facilitate improved management of risks to critical infrastructure.

Earthquake Spectra Journal↗
USGS2025

Methods for estimating selected low-flow statistics at gaged and ungaged stream sites in Massachusetts

The U.S. Geological Survey, in cooperation with the Massachusetts Department of Conservation and Recreation, Office of Water Resources, computed selected at-site streamflow statistics at U.S. Geological Survey streamgages in and near Massachusetts and developed regional regression equations for estimating selected streamflows at ungaged stream sites in Massachusetts. Two sets of regional regression equations were developed: (1) the “mainland” equations, for mainland Massachusetts excluding the area covered by the second set, and (2) the “southeastern” equations, for the Plymouth-Carver-Kingston-Duxbury aquifer area in southeastern Massachusetts and for Cape Cod. The regression equations and at-site statistics may be used by Federal, State, and local water managers in addressing water-resources issues relevant in Massachusetts. Regional regression analyses for the mainland equations were developed to estimate the following 27 streamflow statistics: 99-, 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, and 50-percent flow durations; monthly June, July, August, and September 90- and 50-percent flow durations; February, June, and August median of the monthly means; harmonic mean; and medians of the following annual low-flow frequency statistics: 7-day; 7-day, 2-year; 7-day, 10-year; 30-day, 2-year; and 30-day, 10-year. The analyses used 81 streamgages with minimal to no regulations in and near Massachusetts. The regression analyses determined that four basin characteristics—drainage area, combined hydrologic soils A and B, streamflow variability index, and annual mean temperature—were the only significant explanatory variables for the different mainland equations. Regional regression equations were developed for the Plymouth-Carver-Kingston-Duxbury aquifer area in southeastern Massachusetts and Cape Cod, because surface-water drainage areas and groundwater contributing areas do not always coincide in this area of the State. The regression analyses to estimate 10 flow durations from the 99th to 50th percentiles used 18 streamflow sites with some occasional minor regulations—because there are few unregulated streams in southeastern Massachusetts. The analyses determined that groundwater contributing area and storage (combined water bodies and wetlands) were the only significant explanatory variables in the southeastern equations.

Massachusetts↗
USGS2024

Methodology for inclusion of produced and stored carbon dioxide in the U.S. Geological Survey Federal lands greenhouse gas inventory

The U.S. Geological Survey (USGS) has developed two new carbon dioxide (CO2) emissions and sequestration accounting methods for use in future reports. The first method is a Federal lease-produced CO2 emissions calculation for an update of the report, “Federal Lands Greenhouse Gas Emissions and Sequestration in the United States.” The methodology to incorporate Federal lease CO2 production emissions into the updated report relies on CO2 sales royalty data from the Office of Natural Resources Revenue (ONRR). The end usage points for the gas include enhanced oil recovery with CO2 (CO2-EOR), food and beverage, and chemical production. CO2-EOR is the main end point for natural CO2 production in the United States; it accounted for 94% of usage in 2022 [1]. Federal lands emissions from this sector are estimated at 460 metric tons of CO2 in 2022, a very small amount relative to most other Federal lands emissions sector estimates. The second new method, planned for a separate report, is a calculation of the geologic storage of CO2 on Federal lands. The second method estimates the CO2 stored under Federal surface lands and documents Federal climate change mitigation efforts. Currently, there is no storage of CO2 at an industrial level on Federal lands, however multiple proposals and projects are planned. This method was developed on non-Federal lands datasets in an effort to prepare for when these activities on Federal lands will require accounting. National estimates for CO2 geologic storage using this method, but without a Federal lands filtering step, totaled 8.0 million metric tons (Mt) in 2022. The two methods described here are new benchmark methods in a collection of accounting procedures to document the current state of greenhouse gas emissions and their storage on Federal lands. These benchmarks can then be used to measure any subsequent changes in emissions from or carbon storage beneath Federal lands. While the magnitude of the values is currently non-existent to small, emissions mitigation goals established by decision makers indicate that these values will grow, and their documentation will take on greater value and use.

continental United States↗
USGS2026

Validating antler- and movement-based estimates of caribou reproduction using video camera collars

Wildlife agencies invest substantial resources in monitoring ungulate reproductive rates given ongoing concerns about population trends and their implications for management. For barren-ground caribou ( Rangifer tarandus ) in the North American Arctic, data on parturition and neonate survival have traditionally been collected using visual observations of antler retention and calf presence from small, fixed-wing aircraft. However, these surveys are weather-dependent, expensive, and dangerous, motivating the development of movement-based approaches that infer reproductive events from global positioning system (GPS) location data. We had the unique opportunity to use video camera collars to validate both antler- and movement-based methods for estimating reproduction in barren-ground caribou, using data from the Porcupine (2018–2023) and Western Arctic (2021, 2023) herds in Alaska, USA, and Yukon, Canada. For the movement-based approaches, we assessed individual-based and population-based methods, which both inferred parturition following a sharp decline in movement and inferred calf loss following a sharp increase in movement. We investigated these approaches using rarified 1-, 2-, 4-, and 8-hour fix intervals. Based on video collar observations, we found that antler retention at the onset of the calving period was highly accurate for predicting parturition (96% accuracy) but became unreliable as calving progressed, as approximately 80% of parturient females shed their antlers during the calving period. In contrast, both movement methods performed marginally for estimating caribou parturition (~70% accuracy), with estimates from the individual-based method exhibiting greater bias than those from the population-based method. The individual-based method also poorly predicted neonate survival (≤49% accuracy for detecting parturition and calf fate for the Porcupine Herd). Video data revealed that inaccuracies with the movement methods often occurred when caribou bedded during storms, rested after traversing mountainous terrain, lost a calf shortly after birth, or increased movement to evade insects. These results have important implications for management agencies using antler- and movement-based methods to estimate caribou reproduction, particularly when informing assessments of population decline or recovery. Our results demonstrate the utility of video collar data for validating reproductive monitoring approaches and provide insights into how these approaches can be improved.

Alaska, Yukon↗
USGS2024

Using the horizontal-to-vertical spectral ratio method to estimate thickness of the Barry Arm landslide, Prince William Sound, Alaska

Conducting detailed investigations of large landslides is difficult, especially in the subsurface, largely due to environmental factors such as steep slopes, difficult access, and numerous objective hazards. These factors have made it challenging to accurately estimate the depth to the failure surface of the Barry Arm landslide, a large (roughly 10 8 cubic meters), deep-seated bedrock landslide in Prince William Sound, Alaska, recognized in 2019. The landslide has exhibited accelerated movement in recent years and poses a potential tsunamigenic hazard if rapid failure occurs. Failure surface depth, equivalent to landslide thickness, is a necessary metric for landslide-volume calculations and associated tsunami wave models. In this report, we used seismic noise recorded by a seismometer located on the Barry Arm landslide in Alaska to calculate the horizontal-to-vertical spectral ratio (HVSR) to investigate the site fundamental frequency ( f 0 ) and depth of the failure surface. To ensure that observed peak frequencies in the spectral ratio were related to the underlying stratigraphy (and not caused by other noise sources like nearby glaciers, topographic resonance, weather, or human activities), we also calculated HVSRs using earthquake signals, HVSRs at other seismic stations within a 2.5-kilometer radius, and a standard spectral ratio between the landslide station and other sites. We observed multiple peaks in the landslide HVSR curves at 1.5 hertz (Hz), 4–5 Hz, and 7–11 Hz. The frequencies of these peaks were consistent at the landslide site through time and across methods and were dissimilar to those identified at other seismic stations in the area, making it unlikely the peaks were caused by local noise. Directional HVSRs calculated at 15-degree intervals showed amplification of the higher frequency peaks in the direction parallel to slip, indicating two-dimensional site effects. We used the distinct frequency peaks in the seismic record to develop a 4-layer conceptual model of the landslide wherein the top of the deepest layer represents the primary failure surface, or the boundary between damaged (mobile) and undamaged material. We inverted Rayleigh wave ellipticity curves within this 4-layer configuration with constraints on S-wave velocity and layer thickness based on analogous material properties identified in the literature. This was necessary absent any site-specific subsurface S-wave velocity data. The best-fitting models indicate a mean slope-normal depth to the failure surface of 188 (±9) meters (m), with additional stratigraphic boundaries at 4 and 20 m below ground surface, potentially representing layered motion. These results agree with and improve upon ranges estimated by previous studies and can support future modeling and assessment efforts at Barry Arm.

Alaska↗
USGS2026

Modeling legacy nitrogen transport under instantaneous, steady-state, and transient groundwater flow conditions

In hydrologic settings where groundwater discharge contributes substantially to surface waters, legacy nitrogen in groundwater can confound surface water nitrogen loads estimated exclusively from current terrestrial sources. Additionally, legacy nitrogen in groundwater can contribute to lagged responses to nitrogen management efforts. Some methods of estimating groundwater contributions to surface water nitrogen loads account for legacy nitrogen, while others do not. The resulting differences are rarely quantified. We used a numerical modeling framework to compare three methods of estimating time-varying annual groundwater nitrogen loads to surface water receptors on eastern Long Island, New York. The instantaneous load method used steady-state contributing areas and includes no temporal groundwater lag. The second method used numerical simulations of nitrogen loads under steady-state flow, which captures groundwater transport lags but omits the annual variability in transient hydrologic stresses. The third method numerically simulated both transient groundwater flow and nitrogen transport to explicitly capture the effects of legacy nitrogen in groundwater. Depending on antecedent nitrogen and hydrologic conditions, historical nitrogen loads estimated from the numerical simulations were sometimes similar (<10% difference) and other times substantially different (±100%) from the instantaneous load estimates. Additionally, simulated future surface water nitrogen loads responded asymptotically over several decades following reductions in terrestrial nitrogen sources, further highlighting the effect of groundwater transport lag times. The comparison of the three methods, quantification of historical interannual variability, and prediction of lagged responses to nitrogen source reductions provide important context for decision makers using estimated groundwater nitrogen loads to help evaluate nitrogen management efficacy.

New York↗
USGS2024

Site response in the Walnut Creek–Concord region of the San Francisco Bay, California: Ground motion amplification in a fault-bounded basin

Thirty‐seven portable accelerometers were deployed in the eastern San Francisco Bay communities of Walnut Creek and Concord to study site response in a fault‐bounded, urban, sedimentary basin. Local earthquakes were recorded for a period of two years from 2017 to 2019 resulting in 101 well‐recorded events. Site response is estimated by two methods: the reference site spectral ratio method and a source‐site spectral inversion method. The reference site spectral ratio method allows investigation of the variability of site amplification with source azimuth and frequency. The source‐site spectral inversion method yields the best least‐squares fit to site response for a database of ground‐motion records. Both methods show substantial amplification in the Walnut Creek–Concord basin below 2 Hz indicating strong surface‐wave development. Greater amplification is seen for sources aligned along the long axis of the basin. Inversion using close‐in sources at short distances yields lower amplification at longer periods than the entire data set due to reduced surface‐wave generation for steeper angles of incidence. Inversion of site response spectra for shallow shear‐wave velocity using a global search algorithm yields V S30 values consistent with generalized mapping results based on geology and topography but with greater variability due to local site variations. 3D finite‐element modeling shows greater amplification in the Walnut Creek–Concord basin with a basin‐edge effect likely contributing to higher ground motions. Topography is also seen to lead to increased scattering and shadowing effects.

California↗
USGS2025

Review and synthesis of the applications of machine learning to coalbed methane recovery

Over the last 30 years, a substantial literature has evolved on the use of machine learning (ML) to assess, predict, and improve the efficiency of coalbed methane (CBM) recovery. In the United States, the production of CBM declined as shale gas production matured, but CBM continues to be an important energy resource in other parts of the world. ML applications that have the potential to improve CBM reservoir management and production forecasts, and to increase exploration and operational efficiency, are still of significant interest. The integration of geostatistical techniques into the CBM ML applications has been largely absent but represents an opportunity for improvement. The literature demonstrates the widespread interest in, and applicability of, ML algorithms applied to CBM problems, and that they continue to result in improvements in predictive performance. However, (1) much of the research is more academic than operational, (2) many results are based on simulations, or small or proprietary datasets, (3) ML performance information can be inconsistent and sometimes entirely omitted, (4) most methodologies are unique to the specific CBM situation and likely not generalizable, (5) no standard data repositories are available to directly compare the performance of competing algorithms, and (6) the spatial component is often omitted. Finally, relatively new ML protocols involving causality analysis and reinforced learning, as well as hybrid workflows combining both supervised and unsupervised learning, are anticipated to dominate the future investigations. Integration of geostatistical and geospatial analysis with ML should enhance performance.

Book chapter↗