SEARCH · Geology Reports

Results for “ESS Open Archive”

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

SourceResearch connectionFrom yearThrough year

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

144 records · Page 7Linked to original sources

USGS2026

CRESCENT earthquake dynamic rupture, earthquake cycle, and tsunami code verification platform

Physics-based simulations are critical for understanding natural hazards. The increasing complexity of numerical codes requires benchmark exercises to verify that different computational methods yield consistent results when solving the same governing equations. Here, we present an open-access web platform designed for the verification of earthquake dynamic rupture, seismic cycle, and tsunami simulations. The platform architecture utilizes a modular, serverless backend on Amazon Web Services (AWS) to provide scalable file processing and visualization. A lightweight static web application provides a secure interface for uploading and managing results, while the browser-based data visualization enables interactive analysis of time series and surface grid data. By using structured JavaScript Object Notation (JSON) text files to define benchmark structures, the system remains fully extensible, allowing the addition of new scenarios without modifying the underlying software logic. The platform hosts the "The Tsunami Problem Versions" (TTPV) 1 & 2, two benchmarks for 3D fully coupled earthquake dynamic rupture and tsunami generation, and provides a framework for earthquake cycle models. This community resource aims to build trust in numerical simulations and facilitate long-term collaborative code verification as modeling software continues to evolve.

Seismica↗

USGS2025

Phase 1 technical implementation plan for the expansion of the ShakeAlert earthquake early warning system to Alaska

Executive Summary The conference report accompanying the fiscal year (FY) 2022 Consolidated Appropriations Act (Public Law 117–103) for the U.S. Department of the Interior and related agencies directed the U.S. Geological Survey (USGS) to “work with the State of Alaska to develop an implementation plan to be completed within two years in order to put ShakeAlert/Earthquake Early Warning in Alaska” (p. 29). Congress included $1 million in the FY 2022 appropriation to conduct this effort. The USGS Earthquake Hazards Program, along with partner organizations, has developed the ShakeAlert earthquake early warning (EEW) system for the West Coast, which currently operates in California, Oregon, and Washington. The purpose of the system and its alert delivery partners is to reduce the impact of earthquakes and save lives and property by delivering ShakeAlert-powered alerts that are transmitted to the public via mass notification technologies, and by providing more detailed data streams to institutional users and commercial service providers to trigger automated, user-specific, protective actions. ShakeAlert was designed in such a way that it could be expanded to other U.S. regions with high earthquake risk, after the build-out of seismic and geodetic networks to support ShakeAlert in a specified region is completed and the necessary funding is secured for long-term operation and maintenance. When an earthquake occurs, seismic waves radiate from the rupturing fault like waves on a pond. It is these waves that people feel as earthquake shaking and that can cause damage to structures. Using networks of ground-motion sensors and sophisticated computer algorithms, ShakeAlert can detect an earthquake seconds after it begins, calculate its location and magnitude, and estimate the resulting intensity of shaking. Early warnings of impending shaking are then sent to people and systems that may experience damaging shaking, allowing them to take appropriate protective actions. Depending on the user’s distance from the earthquake, alerts may be delivered before, during, or after the arrival of strong shaking. There will almost always be a region near the earthquake epicenter where alerts arrive after damaging shaking has begun. The ShakeAlert system updates its ground-motion estimates as an earthquake grows larger. In response to the FY 2022 congressional direction, the USGS worked with the State of Alaska to devise this implementation plan for ShakeAlert expansion to Alaska. The USGS engaged with the Alaska Division of Homeland Security and Emergency Management (DHS&EM) and the Alaska Division of Geological and Geophysical Surveys (DGGS). A cooperative agreement was awarded to the Alaska Earthquake Center (AEC) at the University of Alaska Fairbanks (UAF) for their contributions to the plan and their work coordinating with other networks in Alaska. The USGS engaged with the Alaska Seismic Hazards Safety Commission (ASHSC) throughout the process. The USGS also held a series of Alaska stakeholder engagements. The process of developing the implementation plan was facilitated by contracted staff from Corner Alliance, which is a government consulting firm. This implementation plan describes the details and estimates the costs for a Phase 1 expansion of the ShakeAlert system to Alaska. A geographically limited Phase 1 goal was chosen that covers the highest risk and most populated areas of Alaska. The areas proposed encompass the State’s main population centers and 90 percent of the State’s population. This Phase 1 design is considered very challenging and ambitious from the viewpoint of network operators. The lessons learned if this plan is implemented could be used to consider subsequent phases to expand EEW beyond Phase 1 in Alaska in the future. ShakeAlert is built on the foundation of the sensor networks and data processing infrastructure of the USGS-led Advanced National Seismic System (ANSS). This implementation plan calls for a total of 450 high-quality, real-time EEW-capable ANSS seismic stations in Alaska: 270 new stations, 160 upgraded stations, and 20 existing stations. These seismic station numbers are based on a station spacing of 10 kilometers (km) in urban areas, 20 km in seismic source areas that endanger population centers, and 40 km in other areas. The associated costs also include support for some EEW-capable global navigation satellite system (GNSS) stations, with a focus on improving warnings for large subduction zone earthquakes. For effective EEW, ShakeAlert requires low-latency, high-availability, robust telemetry links to deliver continuous, real-time data from field stations to the data centers. The Alaska data processing hardware infrastructure would follow the general design for fail-safe operation that is used for the ShakeAlert system on the West Coast. The ShakeAlert architecture uses two independent layers: the production layer for earthquake processing and the alert layer to make alerting decisions and serve alerts to users. This implementation plan includes two geographically separated data centers in Alaska, each with two fully independent production and alert layers using the same system design developed for the West Coast. As of March 2024, the ShakeAlert system is at version 3.0.1, with more advanced versions in the development and testing pipeline. ShakeAlert originally used two algorithms to determine the location and magnitude of earthquakes using seismic data. A third algorithm that can calculate very large magnitudes of very large earthquakes with geodetic data was added in March 2024. ShakeAlert publishes several data and alert products to meet the needs of different users. All messages include the location of the earthquake, either as a point or a line, and its magnitude. Ground-shaking estimates are published in two forms, as ground-motion contours and a map grid. Providing adequate warning time for strong shaking (the “target threshold”) requires sending alerts at a threshold lower than that strong shaking level (the “alert threshold”). The thresholds for public alerting in Alaska would be a joint USGS and State decision. To have the greatest benefit, ShakeAlert-powered alerts would be delivered to institutional users and individuals by all practical pathways. The USGS alert layer can support thousands of institutional users and alert redistributors, but the USGS does not have the mission nor the infrastructure and expertise to perform mass notifications to the public or implement automatic actions for end users of the alerts. To meet this need, ShakeAlert recruits private sector “technology enablers” that have the necessary expertise to develop end-user implementations using EEW alerts with the goal of stimulating an EEW industry. Earthquake early warning alerts are useless if people do not know how to respond to them. Although the alert messages include instructions about what to do (drop, cover, and hold on), alerts are more effective if people have been trained in advance. Messages about ShakeAlert’s capabilities, limitations, and benefits could be integrated with existing earthquake education programs, including State-run programs. Therefore, ShakeAlert would coordinate with both public and private partners and stakeholders through various partnerships and agreements to accomplish consistent and ongoing public earthquake hazard education. The estimated capital cost of completing the computing infrastructure and sensor networks for the Phase 1 ShakeAlert expansion to Alaska is approximately $66 million in 2024 dollars. The annual operation and maintenance cost of the completed system is estimated to be $12 million per year in 2024 dollars when fully built out.

Alaska↗

USGS2026

Teach me how to pycap: A high-capacity well decision support tool using analytical solutions in Python

Regulatory agencies in humid temperate environments rely on timely evaluations of streamflow depletion and drawdown to protect aquatic ecosystems and existing water users. Numerical models offer detailed insights, but their complexity and time demands often preclude their practical use in rapid decision-making. We present pycap-dss, an open-source Python package that implements a suite of analytical solutions for estimating streamflow depletion and drawdown. The tool supports superposition of multiple wells and time-varying pumping, enabling cumulative impact assessments in situations with multiple wells and streams. The software is modular and extensible, allowing users to interchange solutions or add new analytical methods. A YAML-based configuration supports batch processing of multiple wells, and an optional AnalysisProject class facilitates integration with regulatory workflows. Rigorous unit and regression testing ensures computational reliability, and continuous integration supports ongoing development. We demonstrate deterministic examples of drawdown where multiple solutions are readily compared and streamflow depletion with multiple wells in the Central Sands region of Wisconsin. We also show the value of Monte Carlo analyses of streamflow depletion in the same Central Sands example, leveraging computational efficiency to evaluate the uncertainty of individual and cumulative streamflow depletion calculations from over 200 high-capacity wells.

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

ARCHI: A new R package for automated imputation of regionally correlated hydrologic records

Missing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in “target” records by linear regression using more complete “reference” records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-acceptance criteria and applying geospatial, correlation, and group-based filters to control reference selection. ARCHI provides additional functions for visualizing results, clustering records with similar correlation structures, evaluating holdout data, and interactive parameterization with an accessible and intuitive graphical user interface (GUI). This methods brief provides an overview of the ARCHI package, modeling guidelines, and benchmarking on two regional groundwater-level datasets from the Central Valley, CA and Long Island, NY. We evaluate ARCHI alongside widely used multivariate imputation software to highlight and contextualize its computational efficiency, imputation accuracy, and model transparency when applied to large, groundwater-level datasets.

California, New York↗

USGS2025

Bedrock geologic map of the Bellows Falls 7.5- x 15-minute quadrangle, Windham and Windsor Counties, Vermont, and Sullivan and Cheshire Counties, New Hampshire

Introduction The bedrock geology of the Bellows Falls 7.5- x 15-minute quadrangle, Vermont and New Hampshire, consists of polydeformed Ordovician to Devonian metasedimentary, metavolcanic, and metaplutonic rocks of the Connecticut Valley trough, Bronson Hill anticlinorium (or Bronson Hill terrane), and the Central Maine terrane. Previous work in this area includes a 1:62,500-scale published map and text (1946), State geologic maps of New Hampshire (1997) and Vermont (2011), and various maps and reports presented largely as parts of field trip guidebooks. A provisional open-file map of the geology of the Vermont part of the Bellows Falls 7.5- x 15-minute quadrangle, completed in 1997, is incorporated and revised on this map based on additional fieldwork. This study recognizes three major structural levels from west to east and from lowest to highest: (1) autochthonous rocks of the Connecticut Valley trough; (2) allochthonous rocks of the New Hampshire sequence and Bronson Hill arc in the Monroe thrust sheet (or nappe), including the Skitchewaug nappe; and (3) allochthonous rocks of the Fall Mountain thrust sheet or nappe.

New Hampshire, Vermont↗

USGS2026

In-situ geochronology using LA-ICP-MS/MS: Application of the Lu-Hf system in carbonate, apatite and fluorite

Geochronology is a fundamental tool for reconstructing earth history and constraining the timing of ore deposit formation. Traditionally, U-rich minerals like zircon, titanite, monazite and apatite have been analysed to determine the timing of mineralization and alteration events. However, not all deposits or alteration assemblages contain these minerals, and the U-Pb system can be disturbed due to open system behaviour. We present a methodology for obtaining in-situ Lu-Hf dates from various minerals to demonstrate the potential for dating ore deposits that lack U-rich minerals. Carbonate, apatite and fluorite minerals from different tectonic settings and geological environments were analysed. We acquired Lu-Hf and REE (rare earth element) data from carbonate minerals from a carbonatite orebody and dike samples from the Mountain Pass REE carbonatite deposit in California, USA. We also tested a ‘double-dating’ method, where U-Th-Pb dates and Lu-Hf dates were obtained from the same analysis of apatite from rocks in the Michigan’s Great Lakes Tectonic Zone. Preliminary results for Lu-Hf dating of fluorite from carbonatite rocks in the Lemitar Mountains of New Mexico are also included. The analytical methodology shows promising results in each of these cases where there is sufficient parent Lu and U and measurable daughter Hf and Pb respectively.

Conference Paper↗

USGS2025

The effects of line simplification on planform geometry

Data on maps should retain accuracy regardless of scale. Yet, as cartographic lines are generalized, there can be impacts on properties such as topology, density, and planform geometry. Here, we investigate the use of the Scale Specific Sinuosity (S3) metric (Stanislawski et al., 2023) to evaluate the effects of line simplification on planform geometry, which is the bends of streams in map view. We employ an open-source Python S3 workflow to characterize the geometry of five diverse stream channels in the United States. The original data are extracted from the U.S. Geological Survey National Hydrography Dataset 1:24,000-scale vector data (U.S. Geological Survey, 2000) (Table 1), and the simplification is done using the Visvalingam and Whyatt method (2017) with a simplification tolerance of 0.5, 1.0, 1.5, and 2.0 km. The S3 analysis is calculated at each level of simplification and S3 derivatives are generated. Derivatives include measures of sinuosity, fractal dimension, and the dominant bend wavelength. The findings show that the change in planform geometry is scale-dependent, though simplification will have little effect on straighter lines. The change becomes more apparent in complex lines as the degree of simplification aligns with the scale of the dominant bend geometries. These logical conclusions are evidence that the S3 is a useful metric for automated characterization of bend geometry regardless of line complexity.

Conference Paper↗

USGS2026

Microtextural characteristics of adularia in banded quartz veins from the Midas low-sulfidation epithermal deposit, Nevada

High-grade ores at the Miocene Midas low-sulfidation epithermal deposit in northern Nevada are confined to crustiform quartz veins containing abundant adularia. Micro-X-ray fluorescence elemental mapping reveals that adularia is a common gangue mineral occurring in colloform bands, bands showing bladed textures, and bands with dendritic terminations. The adularia aggregates have delicate shapes and are comprised of stacked, submillimeter crystals hosted by fine-grained quartz. The textural evidence suggests that the adularia aggregates originally formed within a gel-like, noncrystalline silica matrix, which subsequently transformed into quartz. This indicates that the adularia did not precipitate in open space along the vein walls. Correlative microscopy, involving scanning electron microscopy-based automated mineralogy and optical petrography, demonstrates that bands containing abundant adularia are not the primary host to ore minerals. The ore minerals occur in different bands within the crustiform veins, implying that adularia and ore mineral precipitation did not always occur simultaneously. It is hypothesized here that fluid flow at Midas involved intermittent short-lived events of fluid flashing, causing rapid solute supersaturation in the liquid. During each flashing event, different amounts of vapor were produced along a given vein. Compositional differences between adjacent bands in the crustiform quartz veins may, therefore, be linked to variations in the amount of vapor formed during each flash event.

Nevada↗

USGS2025

Insights into widespread landsliding in southern Appalachia from Hurricane Helene

Between 23 and 27 September 2024, antecedent rain followed by Hurricane Helene produced one of the most damaging weather events in southern Appalachia history. The back-to-back storm events resulted in a maximum cumulative rainfall of 848 mm and hurricane-force wind gusts over 170 km/h in western North Carolina, eastern Tennessee, and southwestern Virginia. The resulting regional flooding, landslides, and tree blowdown caused over 100 fatalities, damaged or destroyed critical infrastructure and thousands of structures, and severed connectivity across the region. Over the next several weeks, a multi-agency landslide response produced a rapid hazard assessment and mapped 2217 landslides, 55% of which damaged infrastructure or property. Orographic uplift enhanced rainfall, resulting in concentrated landsliding along the ~250 km swath of the Blue Ridge escarpment in western North Carolina. Landslides initiated predominantly on windward-facing (southeast-facing) slopes, and localized clustering of initiation points indicated a strong influence of hillslope-scale meteorological and geomorphic factors. Many shallow landslides mobilized into larger, highly mobile, and damaging debris flows that graded into floods. Here, we put our preliminary observations in the context of historical storm-driven landslide events and open new avenues for investigating the nature and extent of landslides and their effects in southern Appalachia and similar environments.

Georgia, North Carolina, South Carolina, Tennessee↗

USGS2026

The United States Magnetotelluric Array and the National Impedance Map

The United States Magnetotelluric Array (USMTArray) data set, collected in the years 2006–2024, consists of more than 1,700 long-period magnetotelluric stations covering the entirety of the contiguous United States on a quasi-regular 70 km grid. Funding across multiple federal agencies was critical to sustaining this effort to its completion. Important components of the project included active guidance and participation from the MT community, the open and timely availability of all data, and the application of consistent instrumentation and robust data processing. Together with parallel advancement in the development of publicly available three-dimensional (3D) inversion codes, the USMTArray has revitalized the US magnetotelluric community and increased the visibility of magnetotellurics within the Earth-science community. Taken as a whole, these data are visualized as the National Impedance Map, which, together with a 3D synthesis conductivity model of the nation, reveals the electrical architecture of the contiguous US. USMTArray data are used by researchers worldwide for fundamental and applied studies, including investigations of continental architecture and evolution, estimation of hazards to critical infrastructure due to geomagnetic storms, and assessment of the nation's undiscovered geothermal and mineral resources. We here review the history and development of the project, discuss the challenges and successes in its execution, present the National Impedance Map and synthesis conductivity model, and highlight the breadth of research stemming from this rich data set.

conterminous United States↗

USGS2025

Geologic map of the Greater Antilles and Virgin Islands

Introduction The geologic map of the Greater Antilles and Virgin Islands is a compilation of information from the literature, integrated to provide a seamless geologic map of the region. This map was prepared to serve as a base map for a mineral resource assessment of the region. Several small-scale regional geologic maps of the region have been prepared in the past. This report supersedes an earlier version of the geologic map of the Greater Antilles and the Virgin Islands that was released as U.S. Geological Survey (USGS) Open-File Report 2019–1036. For this report, the regional geologic overview shown on sheet 1 covers Cuba; the island of Hispaniola, which includes Haiti and Dominican Republic; Jamaica; the Cayman Islands; Puerto Rico; and the U.S. and British Virgin Islands. Sheet 2 shows the geology of Cuba and Cayman Islands, sheet 3 the geology of Jamaica, sheet 4 the geology of Hispaniola, and sheet 5 the geology of Puerto Rico and Virgin Islands. Accompanying the maps are three tables: table 1 lists the map units (in alphabetical order by map-unit label), their ages, and the countries or territories in which they can be found. These are listed in alphabetical order for ease in locating unit names from the symbols on the maps. Table 2 is a list of sources for the geologic map compilation of Puerto Rico. Table 3 shows formations in the Devil’s Race Course Group of Jamaica.

Greater Antilles, Virgin Islands↗

USGS2024

Seismic velocity changes from repetitive seismicity at Mauna Loa prior to and during its 2022 eruption

Mauna Loa’s short-lived eruption from late November to early December 2022 marked the culmination of nearly a decade of elevated seismic activity and geodetic inflation. The volcano has been monitored by a network of permanent, short period and broadband seismometers. I used the continuous waveform data from that network starting in 2012 to generate a catalog of seismicity that enhances the US Geological Survey Hawaiian Volcano Observatory’s public seismic catalog with four times the number of earthquakes, which were then grouped by waveform similarity. Analysis of subtle delays in the timing of arrivals of scattered waves between pairs of earthquakes in this catalog yields a history of small changes in the shallow seismic velocity structure of the volcano. Seismic velocities have been shown at other volcanoes to change during unrest and eruption. My results show a decrease in seismic velocity centered on the summit beginning in September 2022, corresponding to the onset of a vigorous precursory swarm of seismic activity and shallow inflation. During the eruption itself, I observe large changes due likely to dike opening along the northeast rift zone and deflation of the summit reservoir. However, seismic velocity changes associated with non-volcanic sources such as ground shaking from large earthquakes and meteorological influences at seasonal and diurnal time scales are also observed, and these dominate the velocity changes prior to the eruption. Proper accounting of these effects will be a requirement for use in real-time monitoring, and this work serves as a starting point in that endeavor for Mauna Loa.

Hawaii↗

USGS2026

Is satellite-derived bathymetry vertical accuracy dependent on satellite mission and processing method?

This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; and (3) a modified composite band-ratio method for Sentinel-2 (SatBathy) with an initial simplified calibration, followed by a more rigorous linear regression against in situ bathymetry data. All methods were tested in three different areas with different geological and environmental conditions, Cabo Rojo, Puerto Rico; Key West, Florida; and Cocos Lagoon and Achang Flat Reef Preserve, Guam. It is demonstrated that all satellite derived bathymetry (SDB) methods have increased accuracy when the results are aligned with higher-accuracy ICESat-2 ATL24 track bathymetry data using the iterative closest point (ICP). SDB vertical accuracy depends more on location characteristics than the method or optical satellite instrument used. All error metrics considered (mean absolute error, median absolute deviation, and root mean square error) can be less than 5% of the maximum bathymetry depth penetration for at least one method, although not necessarily for the same method for all sites. The SDB error distribution tends to be bimodal irrespective of method, satellite instrument, alignment, site, or maximum bathymetry depth, leading to the potential ineffectiveness of traditional error metrics, such as the root mean square error. However, our analysis demonstrates that performing detrending where possible can achieve an error distribution as close to normality as possible for which error metrics are more diagnostic.

Florida↗

USGS2025

Applying U-Th disequilbrium for dating siliceous sinters

Continental hydrothermal systems are critical avenues for the crustal transport of heat and mass captured for geothermal energy and mineral exploration. Thus, understanding their temporal evolution and longevity is important for resource characterization. Deposits of microlaminated siliceous sinter, common surface expressions of high temperature reservoirs (> 170 °C), have the potential to trace hydrothermal histories. Geothermal reservoirs are often located within uranium-bearing silicic volcanic rock where subsurface fluid-rock interactions extract U into hydrothermal fluids. U incorporated in the surface sinter deposit has the potential to provide a U— Th disequilibrium dating option. We focus on samples from El Tatio geyser field in the Altiplano of northern Chile, the largest geothermal system in the Andes. Our resulting 230 Th/U ages, along with the water and deposit elemental compositions, suggest concentrations of U and Th vary predictably along the sinter apron. While distal facies containing the highest U concentrations (> 50 μg/g) are least affected by detrital Th corrections, they can display suspected open-system behavior. In contrast, more medial facies, where bacterial mats and other porous textures are co mmonly concentrated, have only trace amounts of U (< 0.1 μg/g), which leads to unreliable or geologically improbable dates. Proximal facies tend to date most consistently. By comparing existing 14 C ages with 230 Th/U results, 230 Th/U ages tend to be younger than the 14 C ages, supporting the presence of a 14 C-dead carbon influence. New data confirm that the onset of geothermal activity at El Tatio goes back to the late Pleistocene.

Altiplano, El Tatio geyser field↗

USGS2026

Ductile and brittle Rio Grande Rift deformation in Oligocene granite records a two-stage rift history in southern Colorado

The timing and nature of early deformation in the Rio Grande Rift remains poorly constrained. We present evidence for the earliest structural signature of rift extension in the Sangre de Cristo Range, southern Colorado, based on new geologic mapping, structural analysis, rock magnetic data, and thermochronology. These analyses focus on the ~30.0 Ma granite of Chokecherry Canyon, which hosts discrete low-angle mylonitic shear zones and a distributed, gently SW-dipping protomylonitic fabric. Incremental stretching axes, stretching lineations, and Kmax magnetic lineations plunge gently WSW. Quartz microstructures and crystallographic orientations indicate dominantly coaxial strain in the protomylonite and general shear in the discrete shear zones. Quartz c-axis opening-angle thermometry suggests deformation at ~420–540°C. Thermal modeling of ⁴⁰Ar/³⁹Ar K-feldspar data indicates rapid post magmatic cooling below the brittle–plastic transition, supporting shear-zone formation immediately after emplacement. Slow cooling from ~20–13 Ma was followed by renewed rapid cooling at ~13 Ma, interpreted as the onset of extensional exhumation along the Sangre de Cristo Fault System. These results show that extension in the northern Rio Grande Rift was active by ~30 Ma, earlier than previously recognized. We propose a two-stage model for northern Rio Grande Rift evolution: Stage I (30–23 Ma) records ENE–WSW extension localized in low-angle mylonitic shear zones associated with mid-crustal intrusions; Stage II (≤18 Ma) reflects brittle high-angle normal faulting, focused exhumation, and rift narrowing. Stage I magmatism and deformation along the western range front likely established crustal weaknesses that guided later fault development.

Colorado↗

USGS2026

High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs

We produced a 30-m resolution binational land cover map of Bird Conservation Region 33 (BCR 33) for the U.S. North American Bird Conservation Initiative. The region covers large portions of the Sonoran and Mojave Deserts. The map can support the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program’s recovery planning efforts and constitutes the first known binational land cover dataset spanning sections of the United States–Mexico border and using a consistent classification system for both countries. The mapped region includes 152 distinct land cover classes, covering a total area of 38,421,453 ha (148,345 mi 2 ), of which 13,148,345 ha (52,706 mi 2 ) are located in Mexico and 24,770,640 ha (95,639 mi 2 ) in the United States. We primarily used Landsat 8 (OLI) imagery, supplemented by limited ground surveys from two field campaigns, drone-based aerial data, and existing vegetation classification frameworks from both countries. The classification applied a data-fusion approach integrating 30-m Landsat 8 imagery, decadal phenology metrics from vegetation indices, and a random forest model trained mainly with datasets from a comprehensive national mapping project from the U.S. Geological Survey (USGS) GAP Analysis Project (GAP) and federal wildland fire agencies’ Landscape Fire and Resource Management Planning Tools (LANDFIRE) (GAP/LANDFIRE) [United States side] and the National Institute of Statistics and Geography (INEGI) [Mexico side] as well as land cover maps and opportunistic open-access and field observations. Mapping of the full BCR 33 region was carried out in two phases: 1) Phase I, the prototype map, covered a smaller portion of the transboundary area and identified 31 land cover classes, and 2) Phase II, the full BCR 33 map (refer to Figure 1), which resulted in 152 land cover classes. Using a Random Forest classifier, we achieved an overall prediction accuracy of 92% for the Phase I map and 87% for the Phase II full region map. This slight decrease can be attributed to working on a larger, more complex area with a greater number of land cover classes. No formal validation was conducted, aside from using a subset of the collected field observations and training data to assess model performance during and after training. The training sites were further verified using Google Earth (Google, 2026) imagery. Two undergraduate students who worked for over a year visually inspected imagery and open access public images to confirm each training site during model training using in-house developed, online, visual tools. A portion of this field training data was reserved for model validation, and the corresponding results are to be presented in later sections. The project developed an end-to-end, medium- and fine-resolution remote sensing–based data fusion mapping approach. This effort produced a map (Nagler et al., 2025) and the online tools to support a dynamic, live, online map for visualizing the transboundary vegetation communities in BCR 33. The toolset is currently hosted by the University of Arizona (UofA) Vegetation Index and Phenology (VIP) Lab to support FWS partners (https://vip.arizona.edu/viplab_data_explorer?LCM_BCR33). The online map is designed to allow rapid updates using new training, validation, or correction data, making it dynamic and maintainable. The approach we took established a framework for rapid updating and correction of land cover maps, as the model can be quickly retrained with new field observations, updated training data, or other sources. This enables dynamic mapping and change detection of the region’s vegetation. This framework is an advance in data fusion and crowdsourced mapping of complex, vulnerable regions, providing support to regional stakeholders and the wider user community. This transboundary map can inform the protection, conservation, and restoration of vegetation, habitat, and ecosystems, particularly for threatened and endangered species across the two nations using consistent and harmonized binational mapping systems. Beyond supporting land management decisions and stakeholders in the transboundary desert ecoregions, this BCR 33 mapping effort establishes a foundation for future rapid, low-cost, cross-border land cover mapping that can benefit and advance ecosystem management.

Arizona, Baja California, California, Nevada, Sina↗

USGS2025

The impact of burial diagenesis on soil-formed minerals in paleosols using stable isotopes of phyllosilicates and carbonate clumped isotopes

To understand the effects of burial diagenesis on the stable isotope geochemistry of soil-formed clay and carbonate minerals in paleosols, samples were collected from seven cores, spanning middle- to upper-Pennsylvanian strata of the Illinois Basin, with varied maximum burial depths of 1–3 km. Mixed-layer illite-smectite and kaolinite mixtures give δ 2 H and δ 18 O values of −83 ‰ to −36 ‰ and 11.9 ‰ to 21.1 ‰ (VSMOW), respectively. After carbonates were screened petrographically for diagenetic textures using transmitted light and cathodoluminescence, measured clumped isotope Δ 47 values range from 0.504 to 0.563 ‰ (I-CDES). Resulting mineral formation temperatures for phyllosilicate mineral mixtures are 28 to 66 °C (mean = 47 °C), whereas T(Δ 47 ) estimates for calcites are 36 to 61 °C (mean = 45 °C). Calculated δ 18 O water values from which phyllosilicate minerals and calcites precipitated under isotopic equilibrium ranges from −7.1 to −1.2 ‰ and − 1.4 to +4.9 ‰, respectively. Closed and open-system phyllosilicate-fluid exchange modeling indicates that phyllosilicate alteration occurred in the presence of a low temperature brine or meteoric water and is interpreted to occur in a layer-by-layer illitization transformation. Due to the lack of diagenetic textures and positively correlated T(Δ 47 ) and δ 18 O water , calcites are interpreted to have undergone solid-state bond reordering. Despite low to moderate temperatures (<125 °C) and varying depths of shallow burial (1–3 km), solid-state transformation of phyllosilicates and calcites indicates paleosols had prolonged exposure to burial conditions which has implications for the use of paleosol minerals for paleoenvironmental reconstructions.

Illinois, Indiana, Kentucky↗