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USGS2026

Archean to Mesozoic–Cenozoic seismic crustal structure: Implications for geological and biological evolution

We use >4500 measurements of crustal structure to investigate the seismic structure of continental crust, Archean to Mesozoic–Cenozoic. The mean crustal thickness of continents, including their margins, is 36.5 km. We find that Archean, Paleoproterozoic, and Mesoproterozoic crust have similar mean crustal thickness (41 km), seismic velocities, Vp/Vs ratio, and density. Crusts of these ages span 3 Ga, from 4.0 to 1.0 Ga, and their similarity of physical properties (thickness, density, and seismic velocities) suggests that the process of crustal formation may have been similar during this time period, which covers 66% of Earth's history. Neoproterozoic crustal properties differ significantly from Mesoproterozoic and older crusts. The mean thickness of Neoproterozoic crust is 32 km, some 9 km thinner than the mean thickness of Archean, Paleoproterozoic, and Mesoproterozoic crust. A lithospheric root with a thickness of 150–200 km underlies Archean, Paleoproterozoic, and Mesoproterozoic crusts, and this root resists lithospheric rifting and crustal extension and thinning. Based on these observations, particularly the lithospheric thickness, we conclude that Archean, Paleoproterozoic, and Mesoproterozoic lithosphere are unique and together form the stable cratonic nuclei, defined as the thick (150–250 km), long-lived cores of continents. Higher mantle temperatures during the Archean, Paleoproterozoic, and Mesoproterozoic may have played a key role in the formation of the thick lithospheric roots. The second key finding is that the similarities of mean seismic properties indicate that the process of crustal formation operating in the Archean continued during the Paleoproterozoic and Mesoproterozoic. The thin (mean value < 135 km) lithospheric root beneath Neoproterozoic and younger crust may be related to the steady decrease in mantle temperature through time. Neoproterozoic and Paleozoic crust have similar physical properties, and these eras are characterized by pronounced biodiversification, including the renowned Garden of Ediacara, the Cambrian Explosion, and the Great Ordovician Biodiversification. Mesozoic–Cenozoic crust is the most diverse and reflects the current tectonic and magmatic processes of crustal formation.

Book chapter↗
USGS2024

Integration of rupture directivity models for the US National Seismic Hazard Model

Several rupture directivity models (DMs) have been developed in recent years to describe the near-source spatial variations in ground motion amplitudes related to propagation of rupture along the fault. We recently organized an effort towards incorporating these directivity effects into the USGS National Seismic Hazard Model (NSHM), by first evaluating the community's work and potential methods to implement directivity adjustments into probabilistic seismic hazard analysis (PSHA). Guided by this evaluation and comparison among the considered DMs, we selected an approach that can be readily implemented into the USGS hazard software, that provides an azimuthally varying adjustment to the median ground motion and its aleatory variability. This method allows assessment of the impact on hazard levels and provides a platform to test the DM amplification predictions using a generalized coordinate system, necessary for consistent calculation of source-to-site distance terms for complex ruptures. We give examples of the directivity-related impact on hazard, progressing from a simple, hypothetical rupture, to more complex fault systems, composed of multiple rupture segments and sources. The directivity adjustments were constrained to strike-slip faulting, where DMs have good agreement. We find that rupture directivity adjustments using a simple median and aleatory adjustment approach can impact hazard both from a site perspective and on a regional scale, increasing shaking off the end of the fault trace up to 30--40\% and potentially reducing it for sites along strike. Statewide hazard maps of California show that the change in shaking along major faults can be a factor to consider for assessing long-period (>ls) near-source effects within the USGS NSHM going forward, reaching up to 10--20\%. Finally, we suggest consideration of minimum parameter ranges and baseline requirements as future DMs are developed to minimize single approach adaptations, to enable more consistent application within both ground motion and hazard studies.

Earthquake Spectra↗
USGS2025

Aeromagnetic and magnetotelluric imaging of west-central Idaho and the Stibnite-Yellow Pine mining district: A regional to district perspective

Aeromagnetic and magnetotelluric (MT) data are used to better understand the geology and mineral resources near the Stibnite-Yellow Pine mining district in central Idaho. The reduced-to-pole (RTP) transformation of regional-scale aeromagnetic data shows that allochthonous island-arc rocks west of the Salmon River suture are significantly more magnetic than the Laurentian continental rocks east of the suture and that the granitoids of the Idaho batholith have moderate to low magnetization in both early, metaluminous, and late, peraluminous phases. Application of tilt derivative to aeromagnetic data highlights major crustal-scale structures. The 5-km upward continued magnetic data indicate island-arc rocks have deep magnetic sources. The 110-km-long MT profile images resistivity structure to depths around 30 km. At shallow depths, resistivity corresponds to mapped geologic units, with moderate resistivities underlying volcanic and roof-pendant metasedimentary rocks and moderate to high resistivities occurring beneath the Idaho batholith. Crustal-scale moderate resistivities beneath the suture image the results of tectonomagmatic processes that accompanied suturing and translating allochthonous terranes. Low resistivity values beneath and fringing the batholith are derived from metasedimentary rocks that may have served as a melt source and reductant during melt generation and provided metals during later ore formation. In the Stibnite-Yellow Pine mining district, a high-resolution aeromagnetic compilation is shown to correlate with mapped lithologies and mineral deposit-related structures. The RTP transform distinguishes magnetic and nonmagnetic granitoid phases of the Idaho batholith. The tilt derivative highlights metasedimentary rocks, some of which are favorable ore hosts. The Meadow Creek fault hosts the Stibnite and Hangar Flats deposits and is imaged as a magnetic low due to hydrothermal alteration. Reconstructions of magnetic anomaly offsets and orebodies indicate around 3 km of post-95 Ma dextral separation, with some or all of the offset inferred to postdate the main Au mineralization episode (61–66 Ma).

Idaho↗
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↗
USGS2026

ShakeAlert Earthquake Early Warning System performance during the Mw 7.0 offshore Cape Mendocino earthquake

The 5 December 2024 M w 7.0 Offshore Cape Mendocino earthquake was a challenging test of the U.S. West Coast ShakeAlert earthquake early warning system due to its offshore epicenter and limited near‐source station coverage. We analyzed real‐time performance of all components of the ShakeAlert system, including the seismic algorithms (earthquake point‐source integrated code [EPIC] and Finite‐fault rupture Detector [FinDer]), the geodetic algorithm (Geodetic First Approximation of Size and Time–peak ground displacement [GFAST‐PGD]), and network telemetry during the event. EPIC created the first solution for this earthquake 15 s after origin time with an initial magnitude estimate of M 5.6 and location error of 10 km from the Advanced National Seismic System epicenter. An early spurious trigger from station CE.89101 fortuitously maintained location accuracy and, correspondingly, magnitude accuracy. FinDer contributed its first solution at 18 s with a location estimate closer to the seismic network and produced two distinct rupture geometries, leading to minor fluctuations in estimated intensity contours. GFAST‐PGD did not meet alerting thresholds but otherwise performed as expected. Network latencies were <2 s for most stations, supporting the rapid detection of this earthquake by the system. Roughly five million alerts were delivered to cell phone devices in California and Oregon during this event. This was also the first instance of a school district‐wide ShakeAlert‐powered system being activated. Comparisons to recorded seismograms demonstrate that the maximum warning times before potentially damaging shaking (intensity 6+) were in the range of 5–55 s. Although the ShakeAlert system provided accurate solutions and useful alert delivery, this earthquake raised awareness of potential issues within the system, including the need for improved offshore location estimates, a combination of solutions from ShakeAlert servers, and handling of spurious triggers.

California↗
USGS2025

Slow rupture, long rise times, and multi-fault geometry: The 2020 M6.4 southwestern Puerto Rico mainshock

The M 6.4 mainshock of the southwestern Puerto Rico seismic sequence on 7 January 2020, was one of the most impactful modern earthquakes in the northeastern Caribbean. Due to its offshore location and complex aftershock distribution, its source kinematics remain poorly constrained. This active sequence illuminated a complex set of previously unrecognized structures that indicate multiple causative faults may have slipped during its rupture. Here, we utilize seismic and geodetic observations to enhance model resolution, estimate the finite slip of the mainshock, and test a multi-segment, geologically realistic fault geometry. Our refined model finds a lower rupture velocity and longer rise times than typical for an event of this magnitude. This indicates a slow-evolving rupture process that resembles characteristics of a tsunami earthquake. Although this normal/strike-slip faulting event was not tsunamigenic, these qualities, if pervasive for this region, have important implications for future seismic monitoring and hazards in southwestern Puerto Rico.

Puerto Rico↗
USGS2025

Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm

Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance – both across all reaches and by stream type (e.g., reservoir or groundwater influence) – as a baseline for future improvement. The model showed reach-level RMSE of <2 °C with 90 % prediction intervals that contain 90.7 % of observations. We also assessed how the model captured variability in ecologically relevant metrics (e.g., R 2 for annual 7-day maximum = 0.76; R 2 for days exceeding 25 °C = 0.75). This model does not outperform state-of-the-art machine learning efforts (e.g., RMSE ≤1.5 °C) due to a limited input set but does provide the most spatially complete modeling to date to support water availability assessments.

contiguous United States↗
USGS2025

Colored shaded-relief bathymetry and acoustic backscatter of Lake Sammamish, Washington

Evidence of strong earthquakes (such as underwater landslides and associated deposits) may be recorded within the lacustrine sediments of Pacific Northwest lakes. The floor of Lake Sammamish, Wash., an approximately 11 kilometer (6.8 mile) long, 2 kilometer (1.2 mile) wide, and 35 meter (114.8 feet) deep lake located in a populated region just east of Seattle, was mapped by the U.S. Geological Survey in November of 2021 to search for evidence of past earthquakes. Mapping was conducted using a SWATHplus-M 234-kHz interferometric side-scan sonar system was pole-mounted on the U.S. Geological Survey research vessel Parke Snavely, and the system collected full-coverage bathymetric and acoustic backscatter data, which were processed to 2-meter spatial resolution. Two maps were created, a colored shaded-relief bathymetric map showing lake floor morphology (sheet 1), and an acoustic-backscatter map showing backscatter intensities (sheet 2). The results may then be utilized together to investigate past earthquake activity.

Washington↗
USGS2024

SCEC/USGS Community Stress Drop Validation Study: How spectral fitting approaches influence measured source parameters

Spectral source parameters used to estimate an earthquake’s stress drop (Δσ) can vary significantly across measurement approaches. The Statewide California Earthquake Center/U.S. Geological Survey Community Stress-Drop Validation Study was initiated to compare source parameter estimates, focusing initially on a dataset from the 2019 Ridgecrest earthquake sequence. As part of that validation effort, here we focus on one potential source of uncertainty: whether spectral fitting approaches alone, applied to a common set of spectra from the 2019 Ridgecrest sequence result in different source parameter estimates. By using a common set of benchmark spectra analyzed across a consistent frequency band of 1–40 Hz, we eliminate many sources of variability. A subgroup of validation study participants volunteered to estimate the low-frequency displacement (Ω0) and corner frequency ( f c ) by fitting a smooth function to benchmark displacement spectra. Participants used linear- or log-sampled spectra, assumed a Brune or Boatwright spectral model, and applied different misfit criteria. We compare 17 approaches used to estimate Ω0, f c , and Δσ for 54 earthquake spectra. Our results reveal that 35% of events have Δσ estimates within a factor of two, whereas others exhibit variations exceeding an order of magnitude. The variability in Ω0 and f c can largely be attributed to whether a spectrum is consistent with the smooth function of an idealized simple crack model. The trade-off between Ω0 and f c may be more pronounced when using linearly sampled spectra, as higher frequency spectral bumps control the fits. As expected, methods that assumed a Boatwright model tended to have lower Ω0 and somewhat higher f c compared to those assuming a Brune model, although resulting Δσ estimates are similar. When compared to the overall validation study results, the fitting approach alone may account for between 5% and 90% (25% on average) of the total variability in spectral Δσ.

California↗
USGS2024

The 3D National Topography Model Call for Action—Part 1. The 3D Hydrography Program

The U.S. Geological Survey is initiating the 3D Hydrography Program (3DHP), the first systematic remapping of the Nation’s surface waters since the original 1:24,000-scale topographic mapping program was active from 1947 to 1992. Building on decades of experience maintaining the National Hydrography Dataset (NHD), the Watershed Boundary Dataset (WBD), and the NHDPlus High Resolution (NHDPlus HR), the 3DHP will completely refresh the Nation’s hydrography data and improve discovery and sharing of water-related data. The design of the 3DHP is based on the results of a study that estimated that the fully implemented program would have the potential to provide more than $1 billion in benefits to Federal, State, Tribal, Territorial, and local governments and to private and nonprofit organizations every year, in addition to myriad societal benefits. The 3DHP would directly support better decision making regarding water resources by providing more accurate, complete, and integrated information than is currently available. The 3DHP datasets will include a three-dimensional (3D) hydrography network generated from and integrated with elevation data from the 3D Elevation Program (3DEP) to better represent stream gradients and channel conditions, along with waterbodies, hydrologic units, hydrologically enhanced elevation and other surfaces, and more consistent and accurate attributes. The 3DHP datasets will inherit key attributes of the NHD, WBD, and NHDPlus HR, and they also will include new attributes and links to other data such as the U.S. Fish and Wildlife Service National Wetlands Inventory, groundwater data, and engineered hydrologic systems such as stormwater networks. The 3DHP will be designed to provide a set of open and interoperable web-based tools, maps, and data catalogs, creating a robust system for users to reference their information about water; the system elements are collectively referred to as the “infostructure.” The 3DHP and the infostructure can provide a foundational geospatial underpinning for the Internet of Water, a community-based effort to modernize tools and technologies to share water data. As proposed, the 3DHP would begin providing products and services to the public in 2024.

Circular↗
USGS2026

Subaerially exposed Iceberg Lake sediments: An exceptional record of historical subaqueous earthquake disturbance at the eastern edge of the Alaskan-Aleutian subduction zone

Paleoseismic records are limited in the Yakutat Terrane (eastern edge of the Alaskan-Aleutian subduction zone) due to the extensive ice cover that hinders traditional methods such as trenching of the faults, but lacustrine sediments offer an alternative archive. We investigated lakebed sediments exposed after recent outburst floods (1999 CE) at Iceberg Lake, a glacier-dammed basin whose stratigraphy was revealed after the lake drained and partially eroded. We logged outcrops across the basin and sampled their sediments. Between annually laminated background deposits, we identified sediment gravity flow beds and in-situ soft-sediment deformation structures (convolutions, sand blows, and fractures) interpreted to be earthquake-induced. Our age model links some of the uppermost depositional and deformational events to the 1958, 1964 and 1979 CE earthquakes. These results demonstrate that Iceberg Lake was a sensitive recorder of seismic shaking and its sediments hold strong potential for producing a paleoseismic record for the northern Yakutat Terrane.

Alaska↗
USGS2025

Using probability difference to compare streamflow information of alternatives for efficient operation of monitoring networks

Efficient operation of streamflow monitoring networks requires investments in technology and labor that provide the greatest benefits from available resources. Economic analyses comparing the costs and benefits from different types of alternatives for monitoring have not been practical to implement. Streamflow information provides a generic measure of benefits that can be incorporated into operational decisions as an objective for monitoring networks. A methodology for comparing how accuracy, monitoring period, and monitoring instead of modeling affects streamflow information is developed from information-theoretic approaches for network design but contributes three novel features: (1) a probability-difference model for conditional probability of monotonically paired variables, (2) explicit discounting of unverified information that may exceed the accuracy of streamflow records, and (3) run analysis to account for non-stationarity in streamflow probabilities. Application of the methodology to the U.S. Geological Survey streamflow monitoring network indicates the value of monitoring period to reduce the uncertainty of streamflow probabilities and, thus, increase streamflow information. The methodology has important limitations, particularly for sites with non-perennial streamflow, but demonstrates that probability difference could be used to evaluate operational alternatives to increase the efficiency of monitoring networks.

PLOS Water↗
USGS2025

Mine waste as a potential source of critical minerals and other commodities: Examples from the Four Corners states, USA

The growing demand for critical minerals and other mineral resources has raised concerns about possible supplies of these essential commodities. Mine waste is a potential source of these essential commodities. We compiled a geospatial database of publicly available data of the largest non-fuel mine waste features (>200,000 m 2 in areal extent) in the Four Corners states of the United States, where most of those features are from porphyry Cu deposits. The combined volume of those large porphyry Cu mine waste features is approximately 17 cubic kilometers, 60% of which is ore-related material such as tailings. Using publicly available data on density, grade, and previous recovery values, we estimate the contained endowments of Ag, As, Au, Bi, Cu, Mo, Re, S, Sb, Se, Te, and W. These estimates indicate endowments within ore-related mine wastes are collectively comparable to endowments of giant to supergiant deposits. If fully recovered, these commodities could meet current global demand from less than 1 year (Sb) to more than 200 years (Re), underscoring the enormous untapped resource potential of mine waste.

Arizona, Colorado, New Mexico, Utah↗
USGS2025

Methodology for compilation of previously published contour data showing the altitude of the base of Dakota Sandstone on the Colorado Plateau, Arizona, Colorado, New Mexico, and Utah

Structure contours and other geologic information from numerous published geologic maps were digitized and compiled into a digital dataset showing the configuration of a single stratigraphic datum, the base of the Dakota Sandstone and its equivalents across the Colorado Plateau. The principal maps compiled in digital form are a series of 1:250,000-scale 1 degree (°) × 2° quadrangle maps published by the U.S. Geological Survey, augmented by other geologic maps published at various map scales. The compiled digital dataset contains geologic map polygons of the Dakota Sandstone and regional stratigraphic equivalents, the location of faults and fold axes, structure contour lines that define the altitude of the base of the unit and bedding orientation data computed from the structure contour lines. This report provides the scientific rationale for compilation of these data and describes the compilation methodology for each of the data elements. This report provides an extended description of the data compilation in a companion U.S. Geological Survey digital data release of spatial data and attributes associated with the contoured surface and associated geologic data layers.

Arizona, Colorado, New Mexico, Utah↗
USGS2025

Groundwater age estimates for the Mississippi River Valley alluvial aquifer based on tracer data collected during 2018–20

This study characterized groundwater age across the Mississippi River Valley alluvial aquifer (MRVA). Groundwater samples from 69 MRVA wells and 19 wells in Tertiary units of the Mississippi embayment aquifer system (MEAS) were analyzed for sulfur hexafluoride (SF 6 ), tritium ( 3 H), helium (He), and (or) carbon-14 of dissolved inorganic carbon ( 14 C). The age distributions of 89 samples were estimated by fitting lumped parameter models to processed tracer concentrations with the U.S. Geological Survey software TracerLPM. Mean ages of MRVA groundwater samples ranged from 12 to 22,000 years, with a median of 140 years. Mean ages of MEAS groundwater samples ranged from 230 to 52,000 or more years, with a median of 13,500 years. The spatial distribution of MRVA groundwater ages was found to be influenced by depth, inflow of groundwater from deeper units, and soil saturated hydraulic conductivity. In parts of the MRVA, the spatial distribution of MRVA groundwater ages was found to be influenced by annual recharge and (or) annual groundwater pumpage.

Alabama, Arkansas, Illinois, Kentucky, Louisiana, ↗
USGS2026

A three-dimensional geologic framework model of the northern Great Plains region of Montana, North Dakota, South Dakota, and Wyoming, USA

This report presents a new three-dimensional geologic framework model (GFM) of the northern Great Plains region, encompassing parts of Montana, North Dakota, South Dakota, and Wyoming. The model provides a regionally consistent, geographic information system (GIS)-ready representation of Phanerozoic sedimentary strata, major fault systems, and Precambrian basement geometry across two sedimentary basins and adjacent uplifts. More than 300,000 geologic and geophysical data inputs were synthesized to model 41 stratigraphic horizons and 47 faults, yielding an internally coherent, sealed-volume interpretation of the subsurface. The modeling workflow developed for this study demonstrates an efficient and scalable approach for constructing basin-to regional-scale GFMs in geologically complex and data-variable settings. Although model fidelity varies with data density and quality, the resulting geometry is broadly consistent with 1:500,000-scale geologic mapping and highlights areas where additional geologic study is most needed. The three-dimensional GFM provides a foundational framework to support groundwater, energy, and mineral resource assessments, and offers a transferable methodology for potential future U.S. Geological Survey efforts to build large-area subsurface models in underexplored regions of the United States.

Montana, North Dakota, South Dakota, Wyoming↗
USGS2024

Telecommunications fiber for sensing earthquake aftershocks: Progress and hurdles

Aftershocks offer valuable clues to earthquake behavior. The challenge: quickly deploying sensors to capture the early details of earthquake ruptures within the zone of aftershocks. Telecommunication fibers might be an answer, providing denser networks in otherwise difficult areas, potentially faster than traditional methods.

Conference Paper↗
USGS2025

Preventing overfitting when using tree-based methods for mapping hydrothermal favorability

Ensemble tree-based algorithms are robust tools for estimating sparsely distributed resources with non-linear dependencies (e.g., hydrothermal systems). These algorithms naturally accommodate the threshold conditions necessary to enable and support hydrothermal systems (e.g., having sufficient heat and permeability) and are simpler than many other non-linear machine learning strategies (e.g., artificial neural networks), which is an advantage when working with few labeled examples from which to learn. In previous work, we used eXtreme Gradient Boosting (XGBoost) to produce regional prediction and uncertainty maps of hydrothermal favorability; however, recent studies suggest that, even when properly applied, XGBoost has some risk of overfitting when there are few labeled examples from which to learn. To evaluate overfitting when constructing hydrothermal favorability maps with tree-based methods, we compare XGBoost with Extremely Randomized Trees (ExtraTrees), another ensemble tree-based algorithm that has the potential to underfit when using few labeled examples. We hold all other modeling parameters constant, resulting in two contrasting favorability maps of conventional geothermal resources for the Great Basin. Our results indicate that ExtraTrees demonstrably reduces overfitting compared with XGBoost. After considering overall performance, we conclude that ExtraTrees provides a more suitable modeling approach than XGBoost for the purposes of conventional hydrothermal resource assessments.

Conference Paper↗