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937 records · Page 27Linked to original sources
USGS2024

Long-term communication of aftershock forecasts: The Canterbury earthquake sequence in New Zealand

On 14 February 2016, a magnitude (M)5.7 earthquake struck in Christchurch New Zealand (Aotearoa in the Maori language). The shaking caused damage to historic facades, power outages, cliff collapses, rock falls, and liquefaction but no reported injuries or fatalities. This Valentine’s Day earthquake was an aftershock in the Canterbury earthquake sequence (CES), which began on 4 September 2010 with the M7.1 Darfield Earthquake and included the destructive and fatal M6.2 Christchurch aftershock on 22 February 2011. This study, eight months after the Valentine’s Day earthquake and six years after the initiation of the CES, is the first to explore long-term aftershock forecast information and communication needs. The exploratory study also aimed to gather feedback on aftershock scenarios, an alternative form for communicating the forecast. The qualitative study involved workshops with emergency managers, public health officials, and members of the public in Christchurch. Key findings for long-term communication throughout an earthquake sequence include: 1. divergent earthquake experiences affect aftershock communication response and information needs; 2. understanding aftershock sequence behavior is foundational to sense-making when large aftershocks occur; 3. strategic earthquake sequence updates from the trusted science agency and local agencies could serve as important reminders for earthquake preparedness; 4. communication of aftershock forecast uncertainty could aid with both the credibility of the information and living with uncertainty, and 5. inclusion of impact information and preparedness advice into aftershock forecast scenarios could provide links to actionable information. The paper derives implications for research and practice of long-term communications during an aftershock sequence.

Christchurch↗
USGS2024

Developing international standards and guidelines for disseminating and validating simulated ground motions

We are leading an effort to develop international standards and guidelines for curating, disseminating, and validating simulated ground-motion data. This effort is organized as a working group within the Consortium of Organizations for Strong Motion Observation Systems (COSMOS). In 2022 we held online workshops on 7 and 8 June and 20 October. The first workshop focused on curating and disseminating simulated ground-motion data. The second workshop focused on validating simulated ground-motions for engineering applications. About 100 people participated in each of the workshops with strong representation from North America, Europe, and western Asia. In the coming year, we intend to form a technical committee to draft the international guidelines and standards while continuing to engage stakeholders from across the globe through online and in-person meetings. The key points from the workshops include: (1) Numerous groups are generating simulated earthquake ground motions and making them openly available; however there is very little coordination among groups to provide consistent interfaces for searching and retrieving data; (2) Standardizing interfaces for metadata and data access should consider agile approaches that can adapt to changing capabilities and user needs while building upon existing efforts; (3) A distributed architecture is preferred by most participants to allow institutions to host and manage their own data while broadcasting their holdings to a combined catalog; (4) Validation of ground-motion simulations applies to the entire workflow for simulating earthquake ground-motions, including the rupture model, seismic velocity model, and seismic wave propagation software; (5) Metrics to evaluate the validation are application dependent; more research is needed to tie structural response characteristics to ground-motion characteristics; and (6) Validation results should provide a clear, transparent, and quantitative assessment of the simulated ground motions.

Conference Paper↗
USGS2026

Evaluating harvest liberalization strategies on population dynamics of southern latitude temperate-breeding Canada geese

The recovery of the Canada goose ( Branta canadensis ) is one of North America's greatest conservation success stories. Today, continental abundance of temperate-breeding Canada geese (those breeding in southern Canada and lower 48 states in the United States) greatly exceeds historical levels. As a result of increased abundance, human–goose conflicts have also increased, ranging from private and agricultural property damage to human health concerns. Managers have primarily attempted to lower Canada goose populations using hunter harvest via liberalized hunting regulations (increased bag limits and total hunting days). To evaluate the effectiveness of harvest strategies for temperate-breeding Canada geese, managers need a better understanding of how liberalized hunting regulations affect population dynamics. We estimated survival and harvest probabilities, abundance, and recovery distribution of temperate-breeding Canada geese banded in Arkansas, USA, during 2005–2020. We found that adult harvest probabilities declined overall during the study and adult survival probabilities increased. Annual abundances of juvenile geese (≤1 year old) declined during the study, whereas there was no detectable trend in abundance of the adult population over time. Most Canada geese breeding in Arkansas were shot in Arkansas, indicating potential for population dynamics to be influenced largely by state-specific harvest regulations. However, our results suggest that harvest liberalization as a management tool may have limited capacity to further influence population dynamics of Canada geese. Declines in productivity and recruitment likely had a greater influence on the similar observed declines in juvenile and adult abundances, respectively, during our study. More research is needed to better understand the ecological mechanism affecting the population dynamics of Canada geese in both urban and rural environments.

Arkansas↗
USGS2025

Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques

Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.

Alaska, Florida, Massachusetts, Washington↗
USGS2024

Colored shaded relief bathymetry and acoustic backscatter of Ozette Lake, Washington

Offshore of the Pacific Northwest of the United States is the Cascadia Subduction Zone, a 1,000-kilometer-long tectonic boundary defined by a large fault, called a megathrust, that extends from the Mendocino Junction off northern California to the Nootka Fracture Zone off Vancouver Island, Canada (U.S. Geological Survey, 2023). The Juan de Fuca and Gorda oceanic plates to the west of this boundary subduct under the North America continental plate to the east. Several other smaller faults that cut through the North America plate crust also affect the region. Although their effects upon Ozette Lake are uncertain, geological evidence for past earthquakes, such as underwater landslides, may be found in Pacific Northwest lakes. Underwater landslides caused by past earthquakes should be well preserved in these relatively undisturbed lake environments. The floor of Ozette Lake, Washington, located along the Pacific coast of the United States, west of the Puget Sound region and about 140 kilometers east of the megathrust was mapped by the U.S. Geological Survey in July of 2019 to search for evidence of past earthquakes. Mapping was completed using a SWATHplus-M 234-kHz interferometric side scan sonar system pole-mounted on the U.S. Geological Survey research vessel San Lorenzo. The system collected full-coverage bathymetric and acoustic backscatter data that were processed to 2-meter spatial resolution (Dartnell and others, 2024). This two-map series displays the results of this mapping. A colored shaded relief bathymetry map (sheet 1) and an acoustic backscatter map (sheet 2) show the lake floor morphology and backscatter intensities, respectively, that can be analyzed for evidence of past earthquakes.

Washington↗
USGS2025

A generalized deep learning model to detect and classify volcano seismicity

Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective at detecting and classifying tectonic seismicity, particularly using Convolutional Neural Networks (CNNs) and leveraging labeled datasets from regional seismic networks. Progress has been made applying ML to volcano seismicity, but efforts have typically been focused on a single volcano and are often hampered by the limited availability of training data. We build on the method of Tan et al. [2024] ( 10.1029/2024JB029194 ) to generalize a spectrogram-based CNN termed the VOlcano Infrasound and Seismic Spectrogram Neural Network ( VOISS-Net ) to detect and classify volcano seismicity at any volcano. We use a diverse training dataset of over 270,000 spectrograms from multiple volcanoes: Pavlof, Semisopochnoi, Tanaga, Takawangha, and Redoubt volcanoes\replaced (Alaska, USA); Mt. Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. The model occasionally confuses transient signals such as earthquakes and explosions and misclassifies seismicity not included in the training dataset (e.g. teleseismic earthquakes). We envision the generalized VOISS-Net model to be applicable in both research and operational volcano monitoring settings.

Volcanica↗
USGS2026

Postglacial ecosystem development of a hydrothermal landscape in Yellowstone National Park

The Yellowstone geo-ecosystem has been the subject of much research, but the ecological history of the Yellowstone Plateau volcanic field and its iconic geyser basins is less known. In this investigation, paleoenvironmental analyses of sediment cores from lakes in Lower Geyser Basin were compared with regional records and paleoclimate model simulations to reconstruct the vegetation, wildfire, limnology, hydrothermal dynamics, and climate drivers since deglaciation, 15,000 to 14,000 years ago. Pollen data from Lower Geyser Basin lakes reveal the strong influence of infertile rhyolitic soils on vegetation history: an initial late-glacial steppe was replaced by lodgepole pine forest from 12.8 to 11.0 ka, with little change in forest composition or cover thereafter despite changing climate. This stability contrasts with the more dynamic vegetation response on nonrhyolite substrates in the Yellowstone region where nutrient and moisture availability is greater. Highest wildfire activity and low lake nutrient levels in Lower Geyser Basin occurred from 12 to 4 ka, when summers were substantially warmer and drier and fire-inducing vapor pressure deficits were 29 to 56% higher. The hydrothermal history, inferred from sedimentary arsenic and cesium abundances, was spatially and temporally variable but lake-forming hydrothermal events align with periods of abundant moisture. Thus, long-term changes in wildfire, limnology, and, to some extent, hydrothermal activity were governed by insolation-driven climate variations, whereas the vegetation response was muted and constrained by geologic processes. These findings suggest that warmer, drier conditions in the future could result in less hydrothermal activity yet little change in forest cover across the Yellowstone Plateau volcanic field despite more wildfires.

Idaho, Montana, Wyoming↗
USGS2025

A scientific vision and roadmap for earthquake rupture forecast developments, a USGS perspective

We articulate a scientific vision and roadmap for the development of improved Earthquake Rupture Forecast models, which are one of the two main modeling components used in modern seismic hazard and risk analysis. One primary future objective is to provide fully time-dependent models that include both elastic rebound and spatiotemporal clustering nationwide, which is particularly important for shorter-term hazard and risk considerations (e.g., earthquake insurance products). We also discuss the importance and perennial challenges associated with quantifying epistemic uncertainties, including those associated with deformation-model slip rates, un-quantified sampling errors with respect to off-fault seismicity, and any spatial covariances. The need for more physics-based approaches is also emphasized, as is the benefit of adding model valuation (quantifying usefulness) to our verification and validation protocols. Given the multidisciplinary and system-level nature of this activity, modular design is critical. Future updates will also draw from best-available science by both the United States Geological Survey and the external community. The primary goal of this paper is to highlight plans that guide research and facilitate community engagement with model development, especially with respect to lowering the entry barrier for early career scientists and engineers. The paper is written so readers can focus on the sections that interest them most (see table of contents), with the Introduction and Discussion providing a stand-alone overview and summary.

Bulletin of the Seismological Society of America↗
USGS2026

Long-term intermittent connection between the western Snake River Plain and Columbia basin: A two-phased incision history of Hells Canyon

For more than a century, researchers have debated the route of the Snake River across the northern Cordillera and U.S. Pacific Northwest, including the associated incision history of Hells Canyon. Here, we use detrital zircon U-Pb provenance analysis of Miocene strata upstream and downstream of Hells Canyon to constrain the evolution in fluvial pathways over time. Downstream of Hells Canyon, we show that the ca. 10−6 Ma Clarkston Heights gravel was dominantly sourced from the nearby Salmon and Clearwater rivers but also with substantial input from drainages that now flow into the western Snake River Plain. These same western Snake River Plain drainages were contributing to time-equivalent Lake Idaho strata, upstream of Hells Canyon, indicating a Hells Canyon fluvial connection between the western Snake River Plain and Columbia basin before 6 Ma. Results from Lake Idaho strata suggest a two-phased lacustrine history in the western Snake River Plain: Before ca. 6.0 Ma, lake strata were derived from local tributaries, suggesting that the western Snake River Plain was isolated from the modern upper Snake River system. Between ca. 4.3 Ma and 2.2 Ma, the source area expanded to include eastern tributaries in conjunction with migration of high-standing topography of the Yellowstone hotspot. Our results challenge the long-held hypothesis of “capture” of the Snake River through Hells Canyon. Instead, we infer that Hells Canyon was a long-established route for outflow of lakes occupying the western Snake River Plain, possibly intermittently, during a ca. 10−2 Ma phase of slow regional incision, followed by rapid incision in Hells Canyon starting ca. 2 Ma in conjunction with erosion of the topographic barrier impounding Lake Idaho.

Idaho, Oregon↗
USGS2025

Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems

Although rainfall-triggered landslides are initiated by subsurface hydro-mechanical processes related to the loading, weakening, and eventual failure of slope materials, most landslide early warning systems (LEWS) have relied solely on rainfall event information. In previous decades, several studies demonstrated the value of integrating proxies for subsurface hydrologic information to improve rainfall-based forecasting of shallow landslides. More recently, broader access to commercial sensors and telemetry for real-time data transmission has invigorated new research into hydrometeorological thresholds for LEWS. Given the increasing number of studies across the globe using hydrologic monitoring, mathematical modeling, or both in combination, it is now possible to make some insights into the advantages versus limitations of this approach. The extensive progress demonstrates the value of in situ hydrologic information for reducing both failed and false alarms, through the ability to characterize infiltration during, as well as the drainage and drying processes between major storm events. There are also some areas for caution surrounding the long-term sustainability of subsurface monitoring in landslide-prone terrain, as well as unresolved questions in hillslope hydrologic modeling, which relies heavily on the assumptions of diffuse flow and vertical infiltration but often ignores preferential flow and lateral drainage. Here, we share a collective perspective based on our previous collaborative work across Europe, North America, Africa, and Asia to discuss these challenges and provide some guidelines for integrating knowledge of hydrology and climate into the next generation of LEWS. We propose that the greatest opportunity for improvement is through a measure-and-model approach to develop an understanding of landslide hydro-climatology that accounts for local controls on subsurface storage dynamics. Additionally, new efforts focused on the subsurface hydrology are complementary to existing rainfall-based methods, so leveraging these with near-term precipitation forecasts is a priority for increasing lead times.

Alaska↗
USGS2025

Seismic response of rock towers at the Trona Pinnacles (U.S.A.) to the 2019 Ridgecrest earthquake sequence: Theory, observations, and models

We analyze the seismic response of a class of fragile geologic features (FGFs), referred to as rock towers (RTs) at the Trona Pinnacles, a group of RTs in southern California that suffered strong shaking during the 2019 Ridgecrest earthquake sequence. FGFs, including RTs, may provide maximum constraints on past earthquake shaking intensity, and thereby support probabilistic seismic hazard assessments (PSHAs). In a rare case study, we explore the hypothesis that RT structural integrity is time dependent, as damage accumulates progressively. We develop finite‐element method (FEM) models of the RTs using photogrammetric shape models. We validate the models by comparing numerical simulations of their response to broadband ground shaking with low‐intensity seismic recordings obtained at the Pinnacles. Results of our simulations are in good agreement with the seismic recordings of actual earthquake aftershocks. We next use the results of the FEM models to analyze the response and evolution of RTs. Our analyses elucidate the influence of geometry over their seismic response, providing a rationale that may explain the rarity of slender RTs at Trona: high‐aspect‐ratio structures that respond in bending develop detrimental tensile stresses that crack the rock, whereas low‐aspect‐ratio ones’ response also includes shearing, which does not compromise material integrity as much as tension. Field measurements with a rebound hammer support this finding, suggesting that the material around the base of slender rocks has been weakened relative to other parts of the RT. We also study how to define simplified mechanical models (“archetypes”) to predict the natural frequencies of RTs. Results from our work illuminate the fundamental mechanisms of seismic response and progressive failure of RTs, and open new avenues of research to potentially incorporate these geologic features as long‐return period constraints on PSHA, in ways analogous to those of the widely used precariously balanced rocks.

California↗
USGS2024

Field evidence and indicators of rockfall fragmentation and implications for mobility

Rockfall fragmentation can play an important role in hazard studies and the design of protective measures. However, the current lack of modeling tools that incorporate rock fragmentation mechanics is a limitation to enhancing studies and design. This research investigates the fragmentation patterns of rockfalls and analyzes the resulting distribution of fragment sizes within corresponding rockfall deposits. We focus on small rock fragments, which provide insights into the dynamics of the rockfall event and can be used as input for numerical modeling. We analyzed multiple rockfall events from locations worldwide, each exhibiting different degrees of fragmentation. Using image analysis techniques, we mapped all visible blocks, determined their volumes, and measured the distances they travelled from the initial point of impact. A key finding is the identification of three indicators of fragmentation. First, in cases where fragmentation was largely absent, we observed a trend of increasing block size with distance from the impact point or source area, which aligns with previously published findings. However, for energetic rockfall events characterized by intense fragmentation, we observed that small fragments exhibited longer travel distances compared to larger fragments. This distinction allowed us to differentiate blocks primarily resulting from the disaggregation process from those primarily resulting from dynamic fragmentation, with implications for rockfall mobility. Second, although the size distribution of rockfall deposits exhibits a power-law scaling for volumes larger than a minimum size threshold corresponding to a rollover of the distribution, in some case studies a deviation from power-law scaling is observed, indicating a process of larger block comminution due to fragmentation. Third, we found that rockfalls with fragmentation experience reduced mobility, indicated by higher reach angles, and higher lateral dispersion showing a wider distribution of trajectories. We interpret these findings as being directly related to the energy-consuming nature of fragmentation, which prevents farther deposition of fragmented rock blocks.

Albacete province, Lombardy and Aosta Valley, Yose↗
USGS2025

Earthquake magnitude and source parameter estimation with a distributed acoustic sensing dataset in the Gorda subduction zone

Distributed acoustic sensing (DAS) systems offer a cost‐effective way to create large‐scale strainmeter arrays for seismological applications using fiber‐optic cables. DAS‐based strain measurements are known to be influenced by various factors, bringing into question their general reliability for accurate earthquake characterization. A 15‐km‐long DAS deployment in northern California was operational within 3 days of the 2022 M w 6.4 Ferndale earthquake and ran continuously throughout the aftershock sequence. We utilize these aftershock data to validate DAS‐based strain measurements in two ways. We first test the accuracy of DAS‐based magnitude estimates from peak dynamic strains by comparing them with magnitude and attenuation scaling relations derived independently from traditional borehole strainmeter (BSM) data. We demonstrate that DAS‐based magnitudes are comparable to BSM‐based magnitudes when corrections for variations in site response along the fiber‐optic cable are properly made. Magnitude errors are spatially correlated, potentially because of factors such as finite‐fault effects (e.g., stress drop) or more complex, unmodeled path attenuation or because of wave propagation effects in heterogeneous media. We then apply more advanced source characterization methodology to the DAS data using a time‐domain empirical Green’s function (EGF) deconvolution approach to measure details of the moment rate history. The EGF approach using DAS data depends on careful treatment of distorting factors such as anthropogenic sources of noise and optical phase wrapping but successfully isolates source spectra for moderate‐magnitude earthquakes: source spectral ratios obtained from DAS data, broadband seismometer data, and BSM data in the same region show consistent results, revealing differences in directivity and spectral shape among earthquakes. Although further research is needed to refine source‐time‐function estimation techniques for DAS data, particularly for larger magnitude events, these case studies demonstrate the clear potential of DAS for earthquake source characterization.

California↗
USGS2025

Favorability mapping for hydrothermal power resource assessments of the Great Basin, USA

The U.S. Geological Survey (USGS) is updating the 2008 assessment of conventional hydrothermal resources for the Great Basin in the western United States. As part of this work, the workflow for hydrothermal resource favorability maps is being modified to integrate modern data-driven machine learning (ML) methods. Improvements include: [1] using new and refined evidence layers (features); [2] using an order of magnitude more training sites (labeled examples); [3] utilizing simple but non-linear supervised ML algorithms; [4] representing positive training sites (wells with measured heat flow) with their ordinal value proportional to the magnitude of convective upflow (i.e., low, high, or very high convective signals instead of past strategies using positive-negative labels); [5] supplementing training sites with additional sites with low convective signals to represent diverse under-sampled areas where hydrothermal systems are unlikely to exist; [6] comparing with competing approaches; and [7] utilizing Monte Carlo cross-validation to estimate and evaluate prediction uncertainty. For the new favorability map, over half of the power-producing systems (i.e., 15 of 28) are predicted in the 99th percentile of most favorable locations (i.e., the highest 1 % of favorability, corresponding to 1 % of the map area), exceeding the performance of past models that have explicitly used power plants as training sites. Previous favorability maps predicted approximately half of the power-producing hydrothermal systems above the 80th percentile (i.e., 20 % of the map area). For the new favorability map, 93 % of power-producing systems (i.e., 26 of 28) are above the 80th percentile. The power-producing systems for which the new model does not perform well are either comparatively small, low-temperature systems or systems also not predicted well by prior modeling approaches, suggesting that these few systems are unusual when compared with most power-producing systems. Focusing research on these known, seemingly different systems may yield new insights and subsequent discovery of new prospects.

California, Idaho, Nevada, Oregon, Utah↗
USGS2024

Coastal Science Navigator companion guide—Discover the U.S. Geological Survey coastal science products you need

The Coastal Science Navigator is an online gateway to a wide variety of U.S. Geological Survey (USGS) coastal change hazards-related information, data, and tools relevant to stakeholders’ scientific and decision-making needs. The products within the Coastal Science Navigator provide data related to past, present, and future threats to our coastlines. The filter search allows users to see all available products and identify relevant options by time scale, geographic scope, coastal hazard theme, and other filters. The guided search suggests products based on users’ answers to a short series of questions. A comprehensive summary is available for each product. The idea for the Coastal Science Navigator arose in 2020 in response to stakeholder feedback identifying the need for a central source for USGS coastal science information. It was published in July 2023 and initially included 55 products. Regular updates are planned to integrate other existing and new products. This guide introduces some of the many coastal change hazards-related products available through the USGS. In it, we showcase the products included in the Coastal Science Navigator’s initial publication in July 2023. While it is not representative of all the information, tools, and data available, we hope it serves as a compelling snapshot of what the USGS has to offer and encourages you to explore the Coastal Science Navigator to discover more of the products you need. To navigate this guide, the products have been organized by the time scale they are best suited for—past, present, or future—although many products cover multiple time scales. An additional section features software, one of the many product types available as filters within the Coastal Science Navigator. Other products include downloadable data, websites, and geonarratives (web pages that combine text, images, and interactive maps into narratives you can scroll through). Featured geographic scopes are also highlighted within this guide, detailing some of the many regions in which the USGS conducts research and illustrating another way to filter products within the Coastal Science Navigator.

Circular↗
USGS2023

Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions

Selecting negative training sites is an important challenge to resolve when utilizing machine learning (ML) for predicting hydrothermal resource favorability because ideal models would discriminate between hydrothermal systems (positives) and all types of locations without hydrothermal systems (negatives). The Nevada Machine Learning project (NVML) fit an artificial neural network to identify areas favorable for hydrothermal systems by selecting 62 negative sites where the research team had confidence that no hydrothermal resource exists. Herein, we compare the implications of the expert selection of negatives (i.e., the NVML strategy) with a random sample strategy, where it is assumed that areas outside the favorable structural ellipses defined by NVML are negative. Because hydrothermal systems are sparse, it is highly probable that, in the absence of a favorable geological structure, hydrothermal favorability is low. We compare three training strategies: 1) the positive and negative labeled examples from NVML; 2) the positive examples from NVML with randomly selected negatives in equal frequency as NVML; and 3) the positive examples from NVML with randomly selected negatives reflecting the expected natural distribution of hydrothermal systems relative to the total area. We apply these training strategies to the NVML feature data (input data) using two ML algorithms (XGBoost and logistic regression) to create six favorability maps for hydrothermal resources. When accounting for the expected natural distribution of hydrothermal systems, we find that XGBoost performs better than the NVML neural network and its negatives. Model validation was less reliable using F1 scores, a common performance metric, than comparing probability estimates at known positives, likely because of the extreme natural class imbalance and the lack of negatively labeled sites. This work demonstrates that expert selection of negatives for training in NVML likely imparted modeling bias. Accounting for the sparsity of hydrothermal systems and all the types of locations without hydrothermal systems allows us to create better models for predicting hydrothermal resource favorability.

Geothermal Resources Council Transactions↗
USGS2025

Ungulate migrations of the Western United States, volume 5

Many ungulates migrate between distinct summer and winter ranges to take advantage of spatially and temporally variable food sources and avoid threats such as predators and deep snow. In 2018, the U.S. Department of the Interior established Secretarial Order 3362, which provided Federal support to expand existing research efforts to study ungulate populations and conserve their migrations by enhancing habitat quality for ungulates across the Western United States. In response to the order, the U.S. Geological Survey (USGS) created the Corridor Mapping Team, which is a collaboration among 11 State agencies, regional and Federal partners, and an expanding number of Tribal wildlife agencies. Together, the Corridor Mapping Team maps ungulate migrations throughout the Western United States and publishes them in the USGS “Ungulate Migrations of the Western United States” report series. This report details migrations and seasonal ranges from 36 additional herds and includes 2 herd updates detailed in previous reports. The Corridor Mapping Team has mapped the migrations and seasonal ranges of 218 unique herds for the report series, including this report. The report series serves as a map-based inventory of the ungulate migrations across the Western United States for biologists, managers, policymakers, and conservation practitioners. Building on the previous report volumes in the series, volume 5 additionally describes some of the local and national initiatives that are incorporating the products, tools, and information from this growing USGS report series.

Navajo Nation↗
USGS2025

ShakeAlert®—Communication, education, outreach and technical engagement strategic vision

Executive Summary In 2006, the U.S. Geological Survey (USGS) began directly supporting ShakeAlert ® research and in 2012 the ShakeAlert demonstration system began testing ( Given and others, 2018 ). The ShakeAlert earthquake early warning (EEW) system is a partnership between the U.S. Geological Survey (USGS) and the three West Coast States (Washington, Oregon, and California) served by the ShakeAlert System, which is part of the larger Advanced National Seismic System (ANSS). With more than 143 million people exposed to potentially damaging shaking in the United States ( Jaiswal and others, 2015 ), earthquakes are a national hazard. Most of our Nation’s earthquake risk is concentrated in the highly populated areas on the active plate tectonic boundaries on the West Coast of the conterminous United States. ShakeAlert is the first public alert system in the United States to provide rapid mass notification of earthquake detection, potentially offering seconds of warning before strong shaking arrives. A few seconds may not seem like much time, but the information in ShakeAlert Messages can be used to trigger automated actions that can prevent injury or death, reduce immediate damage, and speed recovery from earthquakes. The information product issued by the ShakeAlert system is called a ShakeAlert Message and is one of the information products and tools of the ANSS. The ShakeAlert System includes the USGS component, plus the pathways by which ShakeAlert-powered products and (or) services are delivered to end users. Alerts can be delivered to cell phones or be used to trigger automated systems to protect equipment, facilities, and infrastructure, such as slowing or stopping a train. ShakeAlert-powered automated actions can include fire house doors that can be opened to prevent jamming, heavy equipment (for example, trains, elevators, and cranes) that can be automatically stopped or parked in safe positions, and pipeline valves that can be closed to prevent surges and spills. A few seconds of warning also may be sufficient for people to take protective actions, such as drop, cover, and hold on or modified protective actions for a broad range of populations. Advance training may increase the benefit of a speedy response to an alert. Outreach and education about EEW may raise awareness of the overall earthquake threat and how people can best react when they receive an alert or feel shaking. ShakeAlert communication, education, outreach, and technical engagement (CEO&TE) efforts are highly collaborative and essential for the success of the ShakeAlert System. This strategic vision informs how the vast ShakeAlert CEO&TE Community operates and works together. The CEO&TE Community delineates a strategic framework that is intended to set the path for a long-term, sustainable approach to CEO&TE through three focus areas and five priorities. Focus Areas Technical engagement.— The objective of this focus area is to expand and broaden ShakeAlert technical engagement and implementation. External engagement.— This focus area targets engagement in long-term communication, education, and outreach planning, implementation, and evaluation. Internal engagement.— The purpose of this focus area is to build and sustain a robust infrastructure to optimize collaboration, information sharing, and project planning among the community of stakeholders charged with implementing ShakeAlert CEO&TE. Priorities The enumeration of the five priorities listed below does not suggest priority ranking. Public safety, preparedness, and resilience; Technical implementation and engagement; Consistent messaging and communication; Integration with other Federal and State earthquake hazards products; and Educational resources development and dissemination. This strategic vision is a tangible outcome of collaboration among many stakeholders beginning in July 2016. Since then, the work of the ShakeAlert CEO&TE Community has grown into an international effort. The USGS has developed, tested, and implemented a broad spectrum of communication, education, and outreach tools and resources—all of which recognize that seconds matter when it comes to safety and mitigating harm from earthquake hazards. The CEO&TE social science research effort has provided invaluable insights into the ShakeAlert System’s human interface. USGS-licensed technical partners develop, test, and implement real-world applications using ShakeAlert Messages. The success of ShakeAlert CEO&TE efforts is predicated on robust collaboration across numerous agencies, organizations, and groups. As such, this strategic vision outlines a “partnership model” that delineates roles and responsibilities to ensure alignment with focus areas and priorities. The partnership model includes the CEO&TE lead agency (USGS); its principal partners (State agencies and university partners); its implementation partners (for example, technical partners who build systems to deliver ShakeAlert-powered products and (or) services [focus area one]), earthquake education partners who work to increase public preparedness for seismic events (focus area two); and other organizations that work together to enhance the adoption and effectiveness of the ShakeAlert System. These partners collaborate and convene through a variety of working groups and forums, which are also described in this strategic vision and align with focus area three (internal engagement). The CEO&TE Community collaboratively developed its operating principles and a consensus-based, decision-making strategic framework to guide its collective work. Performance metrics are used to continually measure success. Ultimately, the USGS and ShakeAlert CEO&TE Community are advancing the ShakeAlert System that as of the publication of this strategic vision to “provide earthquake early warning for all” serves more than 50 million people.

Circular↗