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USGS2025

Estimating the probability of export restrictions to inform mineral criticality

To assess risks associated with advanced technologies’ supply chain disruptions, governmental agencies and others have developed mineral “criticality” assessments, with criticality described using the economic impact and probability of supply chain disruptions. Previous work developed subjective supply risk indicators to approximate this probability, typically combining several factors such as supply diversity and trading partners’ political stability, where indicator weightings can substantially impact results. This work explicitly quantifies export barrier probability using an ensemble of machine learning classifiers, with probability estimates informed by exogenous variables, including prior barrier implementation and global export dominance. Major differences in high-probability countries and commodities are observed across models, but the ensemble method highlights Indonesia, China, Tanzania, and the United States as particularly high risk. The Supplementary Data File provides export barrier probability estimates for each analyzed country-commodity pair, enabling a direct, quantitative, objective contribution to assessing mineral criticality, enhancing risk identification and prioritization for policymakers.

Resources, Conservation, and Recycling↗
USGS2025

Geochemical monitoring of volcanic fluids in the twenty-first century

This chapter reviews the state-of-the-art of geochemical volcano monitoring techniques. We cover in-situ monitoring technologies that rely on sampling fluids (direct sampling) and on instrumental analysis of the composition of such fluids in real-time (remote sensing of volcanic fluids is covered elsewhere in this book). We first review key concepts and principles in the field, and then review the results of some selected case studies and applications. We cover the large variety of fluid categories emitted by volcanoes, in both the near-(crater fumaroles and lakes, and plumes) and far-(degassing soils, groundwaters) fields. Our aim is to demonstrate the utility of measuring the chemistry of fluids released by volcanoes, and how these can help characterize volcano unrest, and eventually the increased likelihood of eruption. We conclude with a brief discussion of current challenges and knowledge gaps, and on future directions in geochemical monitoring.

Book chapter↗
USGS2025

Estimating the probability of export restrictions to inform mineral criticality

As demand for advanced technologies rises, mineral commodities will increase in geopolitical importance. To assess risks associated with mineral commodity supply chain disruptions, governmental agencies and others have developed "criticality" assessments, with criticality described using the economic impact and probability of supply chain disruptions. In previous work, subjective supply risk indicators were developed to approximate this probability, typically combining several factors such as supply diversity and political stability of trading partners, where indicator weightings can substantially impact results. This work explicitly quantifies trade barrier probability using an ensemble of several machine learning classifiers, with probability estimates informed by exogenous variables such as prior trade barrier implementation and global export dominance. Major differences in the high-probability countries and commodities are observed across models, but the ensemble method highlights Indonesia, China, Tanzania, and the United States as particularly high risk. This approach enables a direct, quantitative, objective approach to assessing trade barrier probability, enhancing risk identification and prioritization for policymakers.

SSRN↗
USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗
USGS2026

Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations

Recently, the demand for battery-grade lithium has substantially increased, largely due to electrification of the transportation sector. The search for new lithium sources has turned to produced waters (frequently brines), a large-volume wastewater by-product of oil and gas extraction. Geochemical analysis indicates the presence of varying concentrations of lithium from produced water samples collected across the United States and represented in the U.S. Geological Survey’s National Produced Water Geochemical Database, as well as mixtures of Marcellus Shale produced water included in the Pennsylvania Department of Environmental Protection’s Oil and Gas Well Waste Reports. We first examined whether the geochemical signature of the lithium-bearing produced waters is sufficiently distinct so that machine learning (ML) can be used to correctly classify samples to the formation of origin. The produced water sample data used to assess classification accuracy were from the Marcellus Shale, Utica Shale and Point Pleasant Formation (Utica), and Smackover Formation oil and gas wells. Further, we evaluated the potential for ML to accurately classify Marcellus Shale produced water spatially (i.e., northeast versus southwest Pennsylvania). We then investigated whether ML algorithms applied to a suite of geochemical concentration data (i.e. Ba, Br, Cl, K, Mg, Sr) may be used to predict the lithium concentration of an unknown sample. Finally, we applied an estimated economic lithium grade cutoff of 150 milligrams per liter (mg/l) and assessed the utility of ML to predict whether a produced water sample would fall above or below the grade cutoff based on the suite of geochemical parameters. Four machine learning algorithms—Random Forest (RF), Gradient Boosting Trees (GBT), Extreme Boosting (XGBoost), and Deep Neural Networks (DNN) were assessed. This study successfully demonstrates that all four machine learning methods can precisely and accurately estimate lithium concentrations and geologic formation classification. The products of this study contribute to the growing body of knowledge aimed at expanding the lithium resource base within the United States.

Alabama, Arkansas, Florida, Georgia, Louisiana, Mi↗
USGS2025

Ordovician stratigraphy, structure, and karst of the Falling Spring Valley, Alleghany County, Virginia, USA

This one-day trip highlights new findings on a preliminary bedrock geologic map that shows results from ongoing geologic mapping in the Falling Spring Valley of Alleghany County, Virginia, USA, which is the southern end of the larger Warm Springs Valley, an elongated anticlinal valley rimmed by Ordovician and Silurian siliciclastic rocks, and which is famous for its thermal springs. This mapping includes stratigraphic, structural, and karst field and lab research focused on the Ordovician strata exposed in the area, the oldest of which is the dolomitic upper part of the Beekmantown Formation (Lower Ordovician, Darriwilian), and the youngest of which is the Juniata Formation (Upper Ordovician, Katian), a sequence of siliciclastic redbeds. Warm Springs Valley is the location of the only known caves in the eastern United States—three at present—with thermal waters flowing in some of their passages. Stops on the trip will highlight key details from mapping efforts, primarily within the structurally deformed Ordovician carbonate sequence that is exposed in the core and limbs of the anticline, as well as the associated karst features that are developed in those carbonate rocks, including results of recent dye traces and water temperature monitoring that have improved our understanding of the karst hydrogeologic systems developed in these strata.

Virginia↗
USGS2025

Prospectivity mapping for geologic hydrogen

Geologic, or naturally occurring, hydrogen has the potential to become a new, low-carbon, primary energy resource. Often referred to as “white” or “gold” hydrogen, this gas occurs naturally in the Earth’s subsurface, similar to petroleum resources. However, unlike petroleum, which releases carbon dioxide when burned, burning hydrogen only produces water as a byproduct. Exploration for geologic hydrogen remains in an early stage and discoveries of high concentrations of subsurface hydrogen are still relatively rare. To facilitate research and exploration for this potential resource, this report presents the first publicly available prospectivity map of geologic hydrogen accumulations in the conterminous United States. Prospective regions are those regions in which all major components necessary for a hydrogen accumulation likely are present—a source of sufficient hydrogen generation, porous reservoirs for storage, and seals to prevent leakage. The midcontinent region of the United States and the central California coast are revealed as having high prospectivity. This analysis also identifies previously unrecognized prospective regions that may be favorable due to long distance lateral migration of subsurface hydrogen, such as the offshore eastern seaboard of the United States, and can provide a linkage between surface observations of hydrogen degassing and far-field source regions. The methodology developed to create this map is expandable and flexible and may be adapted to incorporate new concepts in the hydrogen system and for application to other regions of the world.

conterminous United States↗
USGS2024

U.S. Geological Survey Earthquake Hazards Program decadal science strategy, 2024–33

Executive Summary Earthquakes represent one of our Nation’s most significant and costly natural hazards, with estimated annual loses from earthquakes close to $15 billion in 2023. Over the past two centuries, 37 U.S. States have experienced an earthquake exceeding a magnitude of 5, and 50 percent of States have a significant potential for future damaging shaking; these statistics speak to the need for nationwide interest and investment in earthquake hazard characterization and risk reduction. Authorized under the Earthquake Hazards Reduction Authorization Act, the U.S. Geological Survey (USGS) Earthquake Hazards Program (EHP) provides the scientific information, situational awareness, and knowledge necessary to reduce deaths, injuries, and economic losses from earthquakes and earthquake-induced tsunamis, landslides, and soil liquefaction. The EHP supports activities in three focused topical areas: (1) earthquake monitoring, (2) hazard assessment, and (3) applied research, using the results of each—and the coordination among them—to further support risk translation and communication in regions at risk nationwide. For earthquake monitoring, the Advanced National Seismic System (ANSS), a cooperative effort of USGS networks, university partner regional seismic networks, and real-time geodetic networks, collects and analyzes data on earthquakes; issues timely, reliable notifications of their occurrence and impacts; and provides data for earthquake research, hazard, and risk assessment as a foundation for building an earthquake-resilient Nation. The USGS-operated ShakeAlert Earthquake Early Warning system is a recent addition to EHP’s ANSS infrastructure. In the realm of earthquake hazard assessment, the EHP contributes to earthquake risk mitigation strategies by developing the National Seismic Hazard Model and maps, and other related products, that describe the likelihood and potential effects of earthquakes nationwide, especially in the urban areas of highest risk. The EHP also conducts research on the causes, characteristics, and effects of earthquakes and prioritizes work that directly increases the accuracy and precision of earthquake hazards assessments, earthquake forecasts, and earthquake monitoring and situational-awareness products and that supports the Nation’s earthquake mitigation practices. Bridging the EHP’s efforts across research, hazard assessments, and earthquake monitoring is a broad and comprehensive collection of earthquake information products, including the National Seismic Hazard Model, ShakeAlert, and other products describing impact, such as ShakeMap and PAGER (Prompt Assessment of Global Earthquakes for Response), which have been developed and integrated into EHP’s real-time monitoring systems. EHP funds external partners to carry out many important collaborative activities through an active external grants program—one of the largest in the USGS—and through cooperative agreements with other partners such as the university-operated regional seismic networks, funded as part of the ANSS. To continue its support of earthquake hazard characterization and risk reduction, the EHP aims to strengthen its foundational products and practices while positioning itself to respond to the evolving needs of the Nation and follow best practices of the scientific community. This document describes a strategy for the program to ensure it can meet these demands. The foundational priorities outlined in this strategy represent those activities that remain critical to the core functionality of the program and those that can be supported under current fiscal year 2024-level appropriations. Priorities described as aspirational are important for future growth, and to maintain the program’s position as a leading global resource in earthquake science, but would require increases in appropriated funding to be fully realized. Across the program’s portfolio of activities, several major themes have been identified as the most critical activities to advance EHP science over the coming decade. Together, these activities provide the framework necessary to integrate critical hazard characterization and risk reduction activities across the program. They provide the structure for research to advance the understanding of where, when, and why earthquakes occur and how we can use improved knowledge to drive short-term and actionable forecasts of seismic activity. They expand the usefulness of critical earthquake products and advance the sophistication of those products to keep pace with the rapidly evolving needs of an ever-expanding user base while maintaining the position of the USGS as a global leader in earthquake science. Focus on system-level science. Establish an automated earthquake-processing pipeline. Enhance the accuracy and reliability of the ShakeAlert Earthquake Early Warning system and plan for extension to other regions. Implement time-dependent earthquake forecasting. Develop physically realistic models. Expand computational capacity. This science strategy is organized into three primary sections. The first section provides an overview of the EHP and its budget, governance, and program council. Readers familiar with the program may wish to focus on the second section, which describes the core of the science strategy, including priorities across each of the EHP’s major program activities in monitoring, hazard assessment, and targeted research. The third section outlines science priorities that cut across program activities, including those involving collaborations external to the EHP.

American Samoa, Guam, Northern Marianas Islands, U↗
USGS2026

Simulated ground motion dataset in the Azores Plateau, Portugal, on rock and soil sites

Building on a previously developed bedrock dataset, this study extends the Azores Plateau ground motion simulations to include soil-amplified records and introduces a comprehensive validation framework. Soil amplification is modeled using one-dimensional soil profiles. A stochastic source-based approach is employed to generate the dataset, incorporating randomization of input-model parameters to account for the aleatory uncertainty in seismic activity. The accuracy of the dataset is verified through a comprehensive validation framework, showing that the randomization effectively captures variance and inter-period correlation observed in records. This work provides a robust dataset for advancing seismic hazard and risk assessment in the Azores Plateau.

central and eastern Azores islands↗
USGS2025

Linking permafrost to the abundance, biomass, and energy density of fish in Arctic headwater streams

Permafrost thaw alters groundwater flow, river hydrology, stream-catchment interactions, and the availability of carbon and nutrients in headwater streams. The impact of permafrost on watershed hydrology and biogeochemistry of headwater streams has been demonstrated, but there is little understanding of how permafrost influences fish in these ecosystems. We examined relations among permafrost characteristics, the resulting changes in water temperature, stream hydrology (e.g., discharge flashiness), and macroinvertebrates, with the abundance, biomass, and energy density of juvenile Dolly Varden ( Salvelinus malma ) and Arctic Grayling ( Thymallus arcticus ) across 10 headwater streams in northwestern Alaska. Macroinvertebrate density was driven by concentrations of dissolved carbon and nutrients supporting stream food webs. Dolly Varden abundance was primarily related to water temperature with fewer fish in warmer streams, whereas Dolly Varden energy density decreased with the flashiness of the headwater streams. Dolly Varden biomass was related to both temperature and bottom-up food web effects. The energy density of Arctic Grayling decreased with warmer temperatures and discharge flashiness. These relations demonstrate the importance of terrestrial–aquatic connections in permafrost landscapes and indicate the complexity of landscape effects on fish. Because permafrost thaw is one of the most impactful changes occurring as the Arctic warms, an improved understanding of how stream temperature, hydrology, and bottom-up food web processes influence fish populations can aid forecasting of future conditions across the Arctic.

Alaska↗
USGS2026

Utilizing hydrophones to detect streambed mobilization in the Wild and Scenic reach of the Rio Chama

This paper describes a study that deployed underwater acoustic sensors (hydrophones) to detect streambed mobilization in the Wild and Scenic reach of the Rio Chama, New Mexico. Fine sediment accumulation in the gravel‑cobble bed affects brown trout spawning habitat, and understanding incipient motion thresholds is essential for planning effective environmental flushing flows released from El Vado Dam. Two hydrophone stations were operated during 2021–2022, capturing audio data during a high‑flow pulse in late 2021. Acoustic processing techniques, including spectral analysis and threshold‑based impact detection, were used to identify sediment‑generated noise associated with coarse sediment transport. Results indicate detectable sediment mobilization during the high‑flow pulse, demonstrating hydrophones’ potential as a passive monitoring tool for environmental flow planning and sediment‑management decision support.

Conference Paper↗
USGS2000

Stormflow chemistry in the Santa Ana River below Prado Dam and at the diversion downstream from Imperial Highway, southern California, 1995-98

The Santa Ana River drains about 2,670 square miles of the densely populated coastal area of southern California, near Los Angeles. Almost all the flow in the river, more than 200,000 acre-feet annually, is diverted into ponds where it infiltrates and recharges underlying aquifers. About 2 million people are dependent on these aquifers for water supply. Stormflow in the Santa Ana River is considered a source of "high-quality" water suitable for use as a source of ground-water recharge. To test this assumption, stormflow samples were collected at two locations--below Prado Dam and at the diversion point downstream from Imperial Highway--for 12 winter storms between 1995 and 1998. Nitrate concentrations decreased during stormflow from a median concentration of 7.8 milligrams per liter in base flow to concentrations less than 1 milligram per liter in some large storms. Concentrations of chemically reduced forms of nitrogen (nitrite, ammonia, and organic nitrogen) increased during stormflow and are the predominant forms of nitrogen in large stormflows. Dissolved organic carbon (DOC) concentrations increased from a median concentration of 4.6 milligrams per liter in base flow to more than 20 milligrams per liter in some stormflows. Concentrations of DOC were especially high during the first storm of the rainy season, and large increases in DOC concentrations were measured even as a result of small early season storms that did not cause large increases in streamflow. DOC present during early season stormflow had less ultraviolet absorbance at 254 nanometers (UV 254 ) per unit of carbon than did DOC from late season stormflows. DOC in water held in storage behind Prado Dam had the highest UV 254 absorbance per unit of carbon. Maximum pesticide concentrations in stormflow did not exceed U.S. Environmental Protection Agency Maximum Contaminant Levels. Most pesticide concentrations were less than 1 microgram per liter and less than the detection limits obtained using standard drinking water analyses. Increases in concentrations of pesticides such as diazinon, malathion, and chlorpyrifos in stormflow result from runoff from urban areas downstream from Prado Dam. In general, large late season stormflows have the most pesticide detections of all stormflows sampled. Concentrations of methyl tert-butyl ether (MTBE), a gasoline additive, during base flow were as high as 0.9 microgram per liter and concentrations decreased during stormflow. Like pesticides, the concentrations did not exceed the U.S. Environmental Protection Agency Maximum Contaminant Levels for MTBE.

California↗
USGS2025

VIPER site analysis

We needed to evaluate available orbital data of NASA’s Volatiles Investigating Polar Exploration Rover (VIPER) mission area in order to derive a variety of maps to help the science team identify scientifically interesting places for the rover to visit and to provide scientific context for our mission. Some of these maps also fulfilled engineering and mission design needs to enable safe and efficient landing and roving. We incorporated data from the Lunar Reconnaissance Orbiter Camera, the Lunar Orbital Laser Altimeter, the Mini-RF instrument, the Chandrayaan-2 Orbital High Resolution Camera, the Korean Pathfinder Lunar Orbiter’s Shadowcam, the Kaguya Spectral Profiler and Multiband Imager, and the Chandrayaan-1 Moon Mineralogy Mapper. We used a variety of techniques to build these maps, including stereogrammetry, shape-from-shading, ice stability depth and surface temperature calculations, and the horizon method for solar illumination and direct-to-Earth communications maps. Altogether, these maps allowed us to survey for boulders, evaluate features in permanently shadowed regions that VIPER might explore, provide mineralogic context for what VIPER’s instruments may learn, estimate the ages and radar properties of craters in the VIPER mission area, and evaluate the potential for gravity traverses with the rover. These data and techniques provided a rich set of information from which both the VIPER science team and engineering teams were able to draw in order to plan a safe landing and to plan a VIPER surface mission that will be both scientifically valuable and robust from an operational perspective.

The Planetary Science Journal↗
USGS2025

Overcoming the data limitations in landslide susceptibility modelling

Data-driven models widely used for assessing landslide susceptibility are severely limited by the landslide and environmental data needed to create them. They rely on inventories of past landslide locations, which are difficult to collect and often nonrepresentative. Furthermore, susceptibility maps are most needed in regions without the means to assemble an inventory. To overcome these challenges, we develop a method for assessing shallow landslide susceptibility based on a probabilistic morphometric analysis of the landscape’s topography, rather than the characteristics of landslides. The model assumes that hillslopes with higher relief and gradient compared to the surrounding landscape are more prone to landslides. We demonstrate the superior performance of this approach over contrasting data-driven models across the northwestern United States. As our morphometric model only requires elevation data, it overcomes the major limitations of data-driven models and facilitates the creation of effective susceptibility models in areas where it was previously unfeasible.

Oregon, Washington↗
USGS2024

Evaluation of an open earthquake early warning system in Mexico, and laboratory tests of their sensors

Microelectromechanical system (MEMS) accelerometers are useful for seismological and engineering applications because of their ability to record unsaturated large seismic signals. Recent advances in MEMS technologies enable the design of instruments with improved capabilities that also allow the recording of small signals. As a result, MEMS can be useful across a broad dynamic range and for both major earthquakes and smaller magnitude events. Here, we analyze the performance of a MEMS‐based accelerometer with the capability of real‐time data transmission. We discuss the self‐noise level, dynamic range, and sensitivity, along with the timing precision and data transmission latency, and compare these parameters to other MEMS‐based instruments. We also summarize observations from a regional network deployed in southern Mexico over a period of 3+ yr for the purpose of earthquake early warning. In addition to the sensor evaluation, we present a large, openly available data set of strong motion data from the Mexican network that comprises continuous ground‐motion records from 24 accelerometers in the period between 2017 and 2022.

Seismological Research Letters↗
USGS2026

Remote compositional analyses of space-weathered lunar maria

Visible-to-shortwave infrared (VSWIR) reflectance spectroscopy has revolutionized our understanding of planetary surface compositions. However, space-weathering processes on airless bodies complicate quantitative compositional analyses. Here, we present a framework to isolate the signatures of space weathering in VSWIR spectra of lunar maria by leveraging radiative transfer modeling under the assumptions that (i) a space-weathered target can be expressed as a mixture of fresh and fully space-weathered components and (ii) remaining signatures can be modeled by including agglutinates as an end-member component. We first validate this approach against laboratory spectra of space-weathered Apollo mare soils of known mineral compositions using a probabilistic Markov Chain Monte Carlo implementation of the Hapke radiative transfer model. Second, we illustrate how this approach can be applied to orbital Moon Mineralogy Mapper data. The proposed space-weathering correction workflow for lunar maria could be expanded to other lunar lithologies and applied to existing and future data sets.

Planetary Science Journal↗
USGS2025

Airborne geophysical efforts for critical mineral systems mapping in the southern Midcontinent, USA

The increasing global demand for critical minerals to support energy and technological advancement has accelerated exploration and research efforts for these essential resources. Since 2019, the United States Geological Survey (USGS) Earth Mapping Resources Initiative (EMRI) has worked to modernize geologic mapping of the Nation to better understand its critical mineral resources. To further this initiative, the USGS has flown a series of high-resolution airborne magnetic and radiometric surveys over large areas of the southern Midcontinent. The surveys cover known critical mineral deposits and areas with the potential to host additional critical minerals based on the presence of one or more overlapping mineral systems. One aspect of EMRI emphasizes close collaboration between the USGS and the Association of American State Geologists, as well as other government and industry partners to leverage geophysical, geological, and geochemical expertise on both regional and local scales. The EMRI high-resolution airborne survey data provide new insights into the geophysical framework of the southern Midcontinent and its critical mineral endowment. Additionally, discoveries made from the data have directed new studies for critical mineral exploration.

southern Midcontinent↗
USGS2025

Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole

We use a high-resolution digital elevation model and a numerical thermal model to produce a variety of inputs for a water-ice prospectivity model for the Volatiles Investigating Polar Exploration Rover (VIPER) landing site. These input data are maps of topography, surface slope, surface aspect, surface curvature, maximum temperature, depth to ice stability, permanently shadowed regions (PSRs), distance to PSRs, and PSR density. This model predicts where water ice is most likely within the top meter of regolith, assuming plausible relationships between ice concentration and the various inputs. The model is designed to be adjusted in near-real time as data are collected during the VIPER mission. As such, it is a tool for both analyzing data from the mission as well as planning operations. Since the current model, at this point, relies only on orbital remote sensing, the final version will also be a tool to extrapolate the VIPER mission results across the lunar poles.

Planetary Science Journal↗