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610 records · Page 20Linked to original sources
USGS2025

Estimating earthquake source depth using teleseismic broadband waveform modeling at the USGS National Earthquake Information Center

The U.S. Geologic Survey National Earthquake Information Center (NEIC) monitors global seismicity, producing a catalog of earthquake source parameters in near-real-time to provide information that can help mitigate the societal impact of earthquakes. The NEIC commonly relies on teleseismic observations to constrain earthquake source parameters (e.g., location, depth, magnitude, and mechanism) due to a lack of local and regional observations. For these ‘teleseismic’ events, depth phase (i.e., pP , sP ) arrival time observations provide the best estimate on source depth. However, depth phases are often difficult to accurately identify and/or pick. Therefore, NEIC relies on waveform modeling, such as those determined from W-phase ( M ww ), body wave ( M wb ), and regional ( M wr ) moment tensor estimations, to provide constraints on source depth. While depth estimates from these approaches are informative, higher frequency observations provide more precise estimates because depth phases are more prominently observed at higher frequencies. Here, we present NEIC’s relatively high-frequency (~0.04 to 1 Hz) teleseismic waveform modeling approach, termed Synthetic Depth Phase Modeling (SynDepth), for determining source depth. SynDepth was developed to provide NEIC with a tool that enables rapid, accurate, and quantifiable estimates of earthquake source depth in cases where locator depths are not reliable. This relatively simple and fast procedure searches over 1 km-incremented source depths and an expanding triangular source-time function to find the best-fitting solution. We compare automatic SynDepth solutions for a dataset of 1,216 earthquakes (M5.5-M7.6) between 2017 and 2021 to NEIC-derived depth estimates from other methods. Our approach provides a robust depth estimate for earthquakes lacking local arrival time data, and it minimizes the need for analyst review of depth-phase picks ( pP , sP ) or using predefined ‘fixed’ depths.

Seismological Research Letters↗
USGS2025

Unlocking ecological insights from sub-seasonal visible-to-shortwave infrared imaging spectroscopy: The SHIFT campaign

We stand at the threshold of a transformative era in Earth observation, marked by space-borne visible-to-shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter- and intra-annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub-seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High-Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub-seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open-source processing methods and cloud-based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high-frequency time series data from space-borne VSWIR imaging spectrometers.

Ecosphere↗
USGS2024

The addition of 144Nd atomic mass to routine ICP-MS analysis as a Quick Screening Tool for Approximating Rare Earth Elements (Q-STAR) in natural waters

Rare earth elements (REEs) are a class of critical minerals, all of which can have supply chain vulnerability that impacts economic security. These elements are widely measured in environmental matrices via inductively coupled plasma mass spectrometry (ICP-MS); however, successful quantification can require time-consuming, sample-specific optimization. While a sample-by-sample approach is appropriate for targeted quantification studies, this approach is not suitable for mineral exploration efforts where rapidly screening thousands of samples for the presence of REEs is desired. Here, we demonstrated the use of a Quick Screening Tool for Approximating REEs (Q-STAR) to detect REEs in surface water and groundwater matrices, collected as part of existing environmental studies. A mass-to-charge ratio of 144 ( m / z = 144) was added to an ICP-MS method to screen for REEs in filtered water samples submitted for metals analyses to the U.S. Geological Survey (USGS) National Water Quality Laboratory. We detected the presence of REEs above a reference threshold of 1200 counts per second in 18 % of pre-selected 6626 samples. Using this screened dataset, we mapped estimated dissolved REE concentrations across the United States in relation to ecoregions and underlying geology. Data are constrained to where sample collection took place but nevertheless show estimated aqueous dissolved REE concentrations on a geographic scale that has not yet been studied. To validate Q-STAR, REEs were measured in a USGS standard reference sample, a subset of 88 archived filtered water samples, and in fresh filtered surface water samples. Our targeted analyses demonstrated a strong linear relationship between Q-STAR predicted and measured values in all archived samples for Nd (r 2 = 0.94), and light REEs (LREEs) such as lanthanum (La) (r 2 = 0.93), praseodymium (Pr) (r 2 = 0.94) and samarium (Sm) (r 2 = 0.94). Using Q-STAR screen values, nine field sites were identified and surface water samples recollected to confirm the continued presence of Nd and LREEs. Q-STAR can be used to screen an unlimited number of water samples for the presence of REEs prior to time-intensive and costly quantitative analyses and to generate large REE datasets for further investigation.

Journal of Geochemical Exploration↗
USGS2024

A seismic nodal deployment to understand magmatic structure in the vicinity of the Pahala earthquake swarm

In summer-fall 2022, 80 three-component SmartSolo IGU-BD3C-5 nodal seismometers were deployed surrounding the Pāhala seismic swarm on the Island of Hawaiʻi, with the goal of improving seismicity catalogs, and seismic velocity images of the crust and upper mantle in this region. The Pāhala swarm, located south of Mauna Loa and Kīlauea, has been the site of a multiyear sustained swarm of seismicity at depths of ~ 25 – 40 km, with order of magnitude increases in rate in 2015, and then again in 2019. This seismicity is possibly related to the input of magma from the mantle plume below, which may then be subsequently transported to volcanic edifices. However, these processes remain enigmatic, in part due to a lack of precise earthquake locations and seismic velocity models in this region. Here we provide an overview of the deployment, an assessment of the quality of the collected data, and discuss the viability of the dataset for local earthquake relocation, tomography, and teleseismic receiver functions. Through comparisons with proximal permanent broadband and short period instruments, we find that the nodes produce high quality data, particularly at periods shorter than 5 s, although we find, document, and correct discrepancies with the gain and polarities of the instruments. We successfully record signals from teleseismic earthquakes, even at periods longer than 5 s (the corner of the flat response of the nodes). We also record local earthquakes, including details related to source characteristics. This indicates that the data is likely to prove useful for investigations using both local and teleseismic earthquake signals to better understand the connections between the deep and shallow magmatic systems of Hawaiʻi. While this deployment provides a snapshot in time, its success may provide a useful benchmark for future studies as the volcanic systems of Hawaiʻi continue to evolve in the future.

Hawaii↗
USGS2025

Using gridded seismicity to forecast the long-term spatial distribution of earthquakes for the 2025 Puerto Rico and U.S. Virgin Islands National Seismic Hazard Model

Gridded (or background) seismicity models are a critical component of probabilistic seismic hazard assessments, accounting for off‐fault and smaller‐magnitude earthquakes. They are typically developed by declustering and spatially smoothing an earthquake catalog to estimate a long‐term seismicity rate that can be used to forecast future earthquakes. Here, we present new gridded seismicity models for use in the 2025 National Seismic Hazard Model (NSHM) for Puerto Rico and the U.S. Virgin Islands (PRVI). The previous PRVI NSHM was released in 2003, and our new models incorporate updates to both data and methodology. We utilize an updated earthquake catalog based on improved Puerto Rico Seismic Network data with newly characterized completeness epochs. The catalog is divided into crustal, subduction interface, and intraslab seismicity using new methods and Slab2 subduction zone geometries. To forecast the long‐term spatial distribution of earthquakes, we use an updated methodology developed for the 2023 U.S. 50‐state NSHM, considering three declustering methods and two spatial smoothing methods based on 2D Gaussian kernels. To adapt it for the complex seismotectonics of the region, we also adopt probabilistic methods to account for events with unknown depths and uncertainties in tectonic classification, and develop a new method for spatial scaling to counteract the effects of spatial variability in network coverage while maintaining the use of smaller events. Finally, we test the performance of these spatial models in forecasting the location of M w ≥ 5earthquakes in the region. Our updated methodology improves the representation of epistemic uncertainty relative to the 2003 model, and our results demonstrate the effectiveness of the new measures we have introduced to address heterogeneities in network detection and systematically evaluate forecast performance.

Puerto Rico, U.S. Virgin Islands↗
USGS2024

How does the onset of offset influence geologic slip rates?

Geologic slip rates are typically based on the displacement accrued by a geomorphic or stratigraphic feature and the age of the offset feature. Because slip rates are commonly calculated by dividing the displacement of a faulted marker by its age, they contain two open time intervals: the elapsed time between the age of an offset feature and the age of the earthquake that displaced the feature, and the time between the present‐day and the most recent earthquake. Here, we explore the influence of including unconstrained open intervals in geologic slip rate calculations. We test the degree to which these open intervals affect geologic slip rates and their uncertainties, and we find that their influence depends primarily on mean earthquake recurrence intervals (RIs). Slip rates on faults with longer RIs, such as the Wasatch fault, can be greatly influenced by an increase of up to 20% when accounting for open intervals. In contrast, slip rates on faults with shorter RIs, such as the San Andreas fault, are only slightly influenced by the assumption that slip rates calculated over open intervals approximate those calculated over closed intervals. Our analyses indicate that faults with moderate slip rates (∼0.2–5 mm/yr) are sensitive to both open interval effects themselves, as well as methods to quantify and account for these effects. We re‐evaluate how slip rates are calculated and defined in displacement–time space using published deformation records. We explore the utility of assigning a probability distribution to the initiation of offset of the oldest faulted feature and the timing of the most recent earthquake (MRE). We find that calculating geologic slip rates without using probability distributions that capture the timing of the MRE and the onset of offset of the oldest faulted feature, especially on slow‐to‐moderate slip rate faults, can lead to systematic underestimation of average geologic slip rates.

Seismological Research Letters↗
USGS2025

Earthquake probabilities and hazards in the U.S. Pacific Northwest

Earthquakes and their cascading consequences pose a significant threat to the people, environment, infrastructure, and economy of the U.S. Pacific Northwest. The Pacific Northwest is susceptible to three types of earthquakes: deep (intraslab) earthquakes, subduction zone (megathrust) earthquakes, and shallow crustal earthquakes. For each of these earthquake types, earth scientists can use a variety of methods to estimate the probability of occurrence for future events, which constrains seismic hazard and informs building codes. The timing of past earthquakes indicates that there is an 85-percent chance of a magnitude 6.5 or greater deep earthquake in the Puget Sound region; a 10-15-percent chance of an approximately magnitude 9 earthquake on the Cascadia Subduction Zone; and a 17-percent chance of a magnitude 6.5 or greater crustal fault earthquake in the Puget Sound region in the next 50 years. Individuals and communities can take simple steps to prepare for and reduce the impact of future earthquakes.

California, Oregon, Washington↗
USGS2024

Earthquake scenario development in conjunction with the 2023 USGS National Seismic Hazard Model

We present earthquake scenarios developed to accompany the release of the 2023 update to the US Geological Survey National Seismic Hazard Model (NSHM). Scenarios can serve a range of local and regional needs, from developing proactive-targeted mitigation strategies for minimizing impending risk to aiding emergency management planning. These deterministic scenarios can also be used to communicate seismic hazard and risk to audiences who are not well versed in methods, such as probabilistic seismic hazard analyses. Specifically, we discuss the scenarios developed, challenges, and lessons learned in the development process, and how this work aided the development of the 2023 NSHM itself. In total, 28 scenarios were developed for Hawaii, Utah, Alaska, and Virginia considering the 2023 NSHM science, past scenario efforts, and input from local experts and stakeholders. Finally, we investigate how NSHM modeling decisions can change estimated impacts to Utah and Hawaii in more detail showing, for example, that a shallower dip of the Wasatch fault under Salt Lake City can increase predicted ground-motion intensities and therefore estimated losses and deaths.

Earthquake Spectra↗
USGS2026

Bayesian belief network model to predict human-wildlife conflict in protected areas

Human-wildlife conflict (HWC) poses a pervasive global challenge, affecting livelihoods and threatening biodiversity. To better anticipate and mitigate HWC risk, we developed a large-scale predictive model using a Bayesian Belief Network (BBN). We surveyed 1,011 park rangers across 135 terrestrial protected areas in three Andean countries, documenting recent HWC incidents involving wildlife persecution or killing, livestock depredation, crop damage, or threats to human safety and property. We identified key drivers of HWC risk, including governance, wildlife acceptance, participation, and habitat quality. A sensitivity analysis revealed that enhancing governance and improving wildlife acceptance could reduce HWC risk by > 85%. The BBN model demonstrated scalability, effectively identifying strategies to reduce HWC risk at multiple scales, from individual protected areas to national networks. Our findings highlight the importance of strengthening governance, increasing wildlife acceptance, and enhancing community participation in conservation efforts. BBNs provide a flexible, cost-effective, and data-driven tool to guide protected areas and wildlife managers in monitoring, anticipating, and making informed decisions to mitigate conflict and promote coexistence.

Scientific Reports↗
USGS2026

Evolution of arsenic speciation during thermal treatment simulating wildfire heating in arsenic-rich sediments

Understanding arsenic transformations during wildfires can help better constrain the environmental impacts of increased wildfire intensity, frequency, and burned area. Previous studies have monitored the evolution of metal(loid) speciation at specific time/temperature endpoints, hampering the comprehension of the dynamic transformations of metal(loid)s during wildfires. Here, a novel approach, namely in situ time-resolved X-ray absorption near edge structure (TR-XANES) spectroscopy, was used for the first time to monitor the heat-induced redox transformations of arsenic (As) in two As-rich sediments with different characteristics. No arsenic transformations were observed in either sediment at room temperature or when the sediments were heated to a maximum temperature of 400 ºC. When heated to a maximum temperature of 600 or 700 ºC, As underwent complex, dynamic, and partially reversible redox transformations, the extent of which varied with sediment properties, initial As speciation, and heating temperature and duration. In the case of sediments initially containing As(V) and a low sulfide/sulfate ratio, the As(V) was reduced to As(III) over a short period of time, followed by immediate reoxidation of As(III) to As(V). The extent of the transient As reduction increased with increasing maximum temperature. The final As speciation at the end of the heating experiment was not substantially different from that at the beginning of the experiment. In contrast, in sediments that were best described by a more complex combination of reference compounds (As(V) coprecipitated with or adsorbed on iron oxide, arseniosiderite, arsenopyrite) with a high sulfide/sulfate ratio, As(V) phases were reduced to As(III) and As(III) was further reduced to As(-I) (at 700 ºC, only), followed by delayed reoxidation of As(-I) to As(III) (at 700 ºC, only) and reoxidation of As(III) to As(V). Approximately 9% and 26% of all As remained as As(III) ( i.e., was not reoxidized to As(V)) at the end of the experiment when heated to a maximum temperature of 600 and 700 ºC, respectively, which is higher than the relative abundance of As(III) in the native sediment. These fire-induced As transformations can potentially enhance As mobilization to surface water and groundwater, posing risks to environmental and human health.

California↗
USGS2025

A newly identified creeping strand of the Concord fault, San Francisco Bay Area

The Concord fault constitutes a major branch of the Pacific–North America transform plate boundary in Northern California, bridging the strike‐slip Bartlett Springs ‐ Green Valley Fault system to the north with the Greenville and Calaveras Faults to the south. Like many faults in the San Francisco Bay Area its long‐term slip is partially accommodated by aseismic slip (creep). Although creep has been recognized and monitored on the northern half of the fault for decades, the precise location of the southern half of the fault and its slip rate—whether accommodated seismically or aseismically—has remained enigmatic. How slip transfers between the Concord and Greenville or Calaveras faults to the south remains an outstanding question. New field observations presented here indicate that the active trace of the fault south of downtown Concord is not where previously interpreted and is indeed actively creeping. We report observations of shallow creep continuing >7 km farther south along the Concord fault than previously reported, along a fault strand not previously recognized for most of its length. This is evident as right‐laterally deflected concrete curbs and sidewalk slabs on both sides of every street that crosses the fault at a high angle in southeast Concord and northeast Walnut Creek. We document the magnitude and location of these deflections to estimate accumulated right‐lateral aseismic slip expressed in engineered structures. Offsets of these piercing lines range from 8 to 18 cm, over widths varying from narrow breaks along centimeter‐scale concrete joints to 10‐m‐wide zones of deflection. Significantly, this active trace is ∼400 m west of where the Quaternary active trace has previously been inferred, placing it within—rather than bounding—the built area of suburban Concord. Slip along the fault has already caused infrastructure damage. These results revise our understanding of the southern Concord fault and help constrain its seismic potential.

California↗
USGS2025

SURF: An automated method for building nonplanar 3D fault models from earthquake hypocenters

Accurately characterizing 3D fault geometry is vital for improving our understanding of earthquake behavior and informing the development of seismic hazard models. Despite their importance, subsurface fault structures tend to be poorly constrained because of limitations in observational data. Improvements to the seismic networks and earthquake detection algorithms have increased the precision and volume of earthquake catalogs, which help illuminate detailed subsurface fault structure and provide the most direct information available about fault geometries at depth. We present a Python package to automate generating 3D fault geometries directly from hypocentral seismicity patterns. This method begins with clustering events based on their spatial density, identifying coherent patterns. Nearby clusters are then merged based on the similarity of their orientations. We fit nonplanar surfaces using support vector regression to balance surface accuracy with minimal deviations from planarity. The fault models are output as quadrilateral meshes at user‐defined resolution. In the process of generating the 3D fault surfaces, we compute the spatial density of seismicity around the surface and the planarity as quantitative metrics of the model outputs. As a proof of concept, we apply this approach to the San Andreas–Calaveras fault junction region and the 2019 Ridgecrest earthquake sequence, both in California, which contain complex subparallel faults well defined at the Earth’s surface and abundant microseismicity. These case studies demonstrate the method’s ability to model complex fault structures, including long continuous fault surfaces, crossing faults, variably dipping segments, and subparallel faults. We test the method on both standard network catalogs and double‐difference relocated catalogs. We find that our seismicity‐based fault model results align with published 3D models that incorporate additional constraints and interpretations ( Plesch et al. , 2020 ; Aagaard and Hirakawa, 2021 ). This workflow provides a low‐user‐input solution for estimating fault geometries at depth from earthquake catalogs.

California↗
USGS2024

Event classification, seismicity, and eruption forecasting at Great Sitkin Volcano, Alaska: 1999–2023

The frequency content of volcanogenic seismicity is often used to classify events and their spatial and temporal progression is then used to map subsurface volcanic processes. The progression of volcano-seismic events and associated source processes also plays a critical role in eruption forecasting. Here we develop and evaluate a computerized methodology for characterizing volcano-seismic event types using Frequency Index and Average Peak Frequency. We apply and test this technique at Great Sitkin Volcano, Alaska, classifying over 9000 hypocenters between 1999 and 2023. This 24-year time span covers periods of seismic quiescence, earthquake activity on nearby tectonic (bookshelf) faults, precursory unrest from 2016 to 2021, and the explosive onset in May 2021 of the ongoing effusive eruption. We use the spatial and temporal evolution of classified event types to map the active volcanic and tectonic processes, develop a conceptual model of the subsurface magmatic system, and perform a retrospective analysis of eruption forecasts at Great Sitkin Volcano between 2016 and the present. The classification and progression of hypocenters suggests the subsurface Great Sitkin Volcano magmatic system consists of a mid- to lower- crustal source zone between 10 and 40 km depth and an upper crustal magma storage area between −1 and 10 km depth (hypocenter depth is referenced to sea level and negative depths reflect height above sea level). The earliest precursors occurred in July 2016 and consisted of deep long-period and volcano-tectonic earthquakes at mid-crustal depths suggesting the subsequent unrest and eruption were triggered by a deeper intrusion of magma. This mid-crustal seismic activity was immediately followed by the onset upper-crustal long-period events and volcano-tectonic earthquakes VTs suggesting a strong linkage between the shallow and deeper portions of the magmatic system. The upper crustal area was likely capped by the 1974 lava dome until the magmatic explosion on May 26, 2021.

Alaska↗
USGS2026

The petrogenesis of Þingmúli volcano, East Fjords, Iceland

In this work we revisit Þingmúli volcano (Þ = Th), a classic locality known as an example of a complete tholeiitic differentiation. Þingmúli is a ~ 9.5 Ma extinct central volcano located in the East Fjords of Iceland, in which the whole compositional spectrum from basalt to rhyolites have erupted. These volcanic products have been previously considered as petrogenetically related by an ideal fractionation trend, regardless any temporal relationship or volumetric considerations. Here we report new whole-rock geochemistry, mineral chemistry, isotope analyses, estimation of residence times of the different eruptive deposits, and an update of the original petrogenetic model. Our results highlight that an enriched source, likely spinel lherzolites, generated transitional-alkaline basaltic melts after 15–20% of partial melting at depths of 40–45 km. Many of these basaltic melts erupted at various stages of the volcano's history, while others remained longer in the volcanic plumbing system. These evolved by fractional crystallisation into basaltic andesite magmas with a residence time of ~5 years based on the crystal size distribution of the plagioclase population. Isotopic differences between the basalts/basaltic andesites ( 87 Sr/ 86 Sr ~ 0.7034; 143 Nd/ 144 Nd ~ 0.51315) and the erupted rhyolites ( 87 Sr/ 86 Sr ~ 0.7037; 143 Nd/ 144 Nd ~ 0.51304) indicate that the latter are not petrogenetically related to the former. Therefore, instead of a fractional crystallisation mechanism to generate the rhyolites, we propose the partial melting of ignimbrite layers located beneath the volcano. The broad range of trace element concentrations in andesites and dacites and their different isotopic values compared to the basalts strongly suggest that these magmas have been generated by magma mixing between basaltic and rhyolitic melts, similar to modern day Icelandic volcanoes such as Hekla. These results highlight the need to revisit previously studied Icelandic classic localities and reassess their traditionally proposed petrogenetic models.

Þingmúli volcano, East Fjords↗
USGS2025

Deterministic physics-based earthquake sequence simulators match empirical ground-motion models and enable extrapolation to data poor regimes: Application to multifault multimechanism ruptures

We use the deterministic earthquake simulator RSQSim to generate complex sequences of ruptures on fault systems used for hazard assessment. We show that the source motions combined with a wave propagation code create surface ground motions that fall within the range of epistemic uncertainties for the Next Generation Attenuation‐West2 set of empirical models. We show the model is well calibrated where there are good data constraints, and has good correspondence in regions with fewer data constraints. We show magnitude, distance, and mechanism dependence all arising naturally from the same underlying friction. The deterministic physics‐based approach provides an opportunity for better understanding the physical origins of ground motions. For example, we find that reduced stress drops in shallow layers relative to constant stress drop with depth lead to peak ground velocities in the near field that better match empirical models. The simulators may also provide better extrapolations into regimes that are poorly empirically constrained by data because physics, rather than surface shaking data parameterizations, is underlying the extrapolations. Having shown the model is credible, we apply it to a problem where observations are lacking. We examine the case of crustal faults above a shallow subduction interface seen to break coseismically in simulations of the New Zealand fault system. These types of events were left out of consideration in the most recent New Zealand national seismic hazard model due to the modeling complexity and lack of observational data to constrain ground‐motion models (GMMs). Here, we show that in the model, by breaking up the coseismic crustal and interface rupturing fault motions into two separate subevents, and then recombining the resulting ground‐motion measures in a square‐root‐of‐sum‐of‐squares incoherent manner, we reproduce well the ground‐motion measures from the full event rupture. This provides a new method for extrapolating GMMs to more complex multifault ruptures.

Seismological Research Letters↗
USGS2026

Quantitative mineral resource assessment of lithium pegmatite deposits in the southern Appalachian orogen

The first quantitative mineral resource assessment for undiscovered lithium pegmatite deposits in the southern Appalachian region of the United States was conducted. Permissive tracts for lithium pegmatite deposits were delineated by integrating lithological, tectonic, geochemical, geophysical and mineral occurrence data. Lithium pegmatite prospectivity of the tracts was ranked with simplified mappable criteria, including proximity to Paleozoic felsic intrusions and major lithotectonic structures, stream sediment geochemical anomalies, and pegmatite occurrence data. The geospatial data and permissive tracts were used to estimate the number of undiscovered lithium pegmatite deposits. These estimates were integrated into probabilistic simulations along with a new global lithium pegmatite grade and tonnage dataset to quantify potential contained undiscovered lithium resources. An economic filter was applied to convert the probabilistic estimates of contained lithium into recoverable material. The identified lithium pegmatite resources for the Carolina Lithium and Kings Mountain deposits, North Carolina, contain 1589 thousand tons (kt) of Li 2 O. The median contained undiscovered resource for the southern Appalachian orogen was estimated to be 2240 kt Li 2 O. At 90% confidence, the region contains at least 130 kt Li 2 O, and 10,700 kt at 10% confidence. After applying economic filters, the median recoverable contained resource was 1430 kt Li 2 O, corresponding to approximately 201 years of current lithium imports for consumption in the United States. North and South Carolina are likely to contain most of these resources. Coarse data resolution and intra-state variations in the geological data contribute to uncertainty of undiscovered lithium pegmatite resources. Continued efforts to harmonize disparate geospatial datasets with updated or new information can improve the accuracy and precision of estimated undiscovered lithium pegmatite resources in the study area and at broader scales.

Alabama, Georgia, Maryland, North Carolina, South ↗
USGS2026

Using a temporary emigration model to estimate abundance of stream fishes from hybrid removal surveys with and without block nets

Monitoring programs are often faced with a decision to allocate resources into either robust spatiotemporal coverage to estimate a population index (e.g., not true abundance) or confirming closed sampling conditions (e.g., with block nets) for an unbiased population estimate at the cost of spatiotemporal coverage. However, making accurate and precise abundance estimates at robust spatiotemporal scales is possible when combining open and closed sampling designs with integrated modeling techniques. We used simulations and a case study of backpack electrofishing surveys in the Santa Ana River, California to test the efficacy of an integrated abundance model (temporary emigration model, TE) to estimate abundance of fishes using removal sampling methods with a hybrid sampling design (sampling with and without block nets during removal sampling). We found that the TE model performed well under most modeling scenarios (sample size, amount of closure violation, number of samples collected during closure), although at least a few samples with block nets were necessary for all parameters to be estimable. When applied to fish surveys in the Santa Ana River, we found that catch of the fishes fit to the TE model (Santa Ana Sucker, Arroyo Chub, Channel Catfish, Largemouth Bass, Yellow Bullhead) showed little evidence that the closure assumption was violated when block nets were not used. Additionally, we found that the abundance of non-native fishes negatively affected the abundance of the native Santa Ana Sucker, which was also found to adversely affect the native fish’s access to critical habitat consisting of gravel and cobble substrate. Our results indicate that the TE model presents a viable solution to common sampling problems that impact many monitoring programs, where precise and accurate population estimates can be made at large spatiotemporal scales even when most samples violate the closure assumption.

California↗
USGS2025

The GorDAS Distributed Acoustic Sensing experiment above the Cascadia locked zone and subducted Gorda Slab

The southernmost portion of the Cascadia Subduction zone in Northern California produces high rates of moderate and large earthquakes owing to subduction of the Gorda slab and deformation associated with the Mendocino Triple Junction. Distributed Acoustic Sensing (DAS) is rapidly advancing as a method for detecting earthquakes and imaging crustal structure. We have begun a long-term DAS monitoring experiment on buried telecom fiber in Arcata, California, with the goal of increasing the available recordings of moderate to large earthquakes as well as imaging seismogenic structures. We have recorded over a year's worth of data, including most aftershocks of the 2022 M w 6.4 Ferndale earthquake, though not the mainshock itself. The dataset includes numerous magnitude 3.5 and larger earthquakes including the 2023/01/01 M w 5.4 Rio Dell earthquake. Here we present initial results comparing an earthquake detection algorithm, run in real-time on the processing unit of the interrogator system, with both the ShakeAlert earthquake early warning system as well as a post-processed earthquake catalog developed with deep-learning phase-picker algorithms. The rapid onboard processing of the detector demonstrates the potential utility of DAS-based edge computing for earthquake early warning. We also verify the quality of the strain waveforms both in terms of peak amplitudes and waveform similarity using about five months of nodal seismometer data. These instruments were deployed roughly every 300 m along the ~15km long cable and validate large variations in peak strain over short distances that are seen in the DAS data. All data from time windows surrounding both the local and teleseismic earthquakes are publicly available, which will improve our understanding of both the performance of DAS systems in moderate earthquakes and earthquake hazards associated with the Gorda subduction zone.

California↗