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USGS2026

Deformed submarine terraces in Puget Sound, Pacific Northwest, indicate only one M >~7.5 earthquake on the Seattle fault zone in the past 11,000 yr

Submerged marine terraces in Puget Sound, deformed across the Seattle fault zone (SFZ), indicate that only one earthquake as large as M~7.5 has occurred in at least the past 11 kyr. Previous paleoseismic studies document a M~7.5 earthquake between 923–4 CE, which uplifted coastal marine terraces by as much as 8 m. We demonstrate that this earthquake was the only such event since ~11 ka by mapping and quantifying deformation of older marine terraces, now submerged in Puget Sound. The submerged terraces, attributed to a late-glacial sea-level lowstand, record both glacial isostatic rebound and tectonic deformation. Vertical offset of the ~11 ka terraces within the SFZ is comparable to that of the marine terraces uplifted in 923 CE, implying no additional large (M>~7.5) earthquake on the SFZ since ~11 ka. This result implies a longer recurrence interval than current hazard estimates, which assumes recurrence of M>7.1 events every 5 kyr. Our mapping of SFZ deformation since ~11 ka also supports fault segmentation and contiguous block uplift between the Seattle and Tacoma fault zones.

Washington↗
USGS2025

Aftershocks in stress shadows are inconsistent with modeled static Coulomb stress changes

Aftershock triggering is commonly attributed to increases in static Coulomb stress. In some areas, termed "stress shadows", a decrease in Coulomb stress is predicted to suppress earthquake occurrence. However, aftershocks are often observed in the modeled stress shadows. We examine several hypotheses that attempt to reconcile these shadow aftershocks with the static Coulomb stress change model: (1) they appear to be in shadows because of inaccuracy in the stress change calculations, (2) they occur on faults of unusual orientation which actually experienced increased Coulomb stress, (3) they occur on faults with different frictional properties, not modeled well by Coulomb stress, and (4) they are secondary aftershocks triggered by prior aftershocks or afterslip. When tested on the 2016 Mw7.0 Kumamoto, Japan, and 2019 Mw7.1 Ridgecrest, California, aftershock sequences, none of these hypotheses can explain the majority of the shadow aftershocks, and taken together these hypotheses can explain only about half of these aftershocks. This implies that Coulomb stress modeling that lacks small-scale fault zone heterogeneity might be inadequate to fully capture the true static stress changes and/or that other physical triggering models are needed, for example transient processes such as delayed triggering by dynamic stress changes from the passing seismic waves.

California↗
USGS2025

Beyond optimality: Dryland ecosystems infrequently use water efficiently for carbon gain

Optimality theory assumes plants maximize carbon gain per unit water lost and is often implemented to scale leaf-level carbon gain and water use to regional and global scales. Optimality theory is often mathematically represented by assuming plant water-use efficiency (WUE) scales with VPD k , where k = ½ represents expected optimal behavior. It is unclear, however, if this relationship holds in arid and semi-arid ecosystems that are strongly impacted by soil and atmospheric moisture status. We used data from seven flux tower sites along an aridity gradient in New Mexico to answer: how does the relationship between WUE and VPD compare to expectations based on optimality theory? To address this question, we integrated the Dynamic Evapotranspiration Partitioning Approach for Rapid Timescales with a stochastic antecedent model to estimate ecosystem-level WUE (GPP/T) and the net sensitivity of WUE to VPD, or k Dynamic , which we compare to the theoretical optimal sensitivity of k = ½. Our results show that optimality theory is not always appropriate, and k Dynamic often deviates from ½, especially at some of the more arid sites or during periods of low soil moisture. At less arid, higher elevation sites, k Dynamic is most consistent with optimality theory at moderate VPD levels, but not at high VPD. In general, the sensitivity of WUE to VPD is highly variable such that k Dynamic exhibits notable daily and seasonal variability, suggesting highly dynamic stomatal behavior. These results emphasize that representing plant water-use strategies as dynamic in time and space is critical to improving large-scale estimates of plant water use.

New Mexico↗
USGS2024

Uncertainty and spatial correlation in station measurements for mb magnitude estimation

The body‐wave magnitude (⁠⁠) is a long‐standing network‐averaged, amplitude‐based magnitude used to estimate the magnitude of seismic sources from teleseismic observations. The U.S. Geological Survey National Earthquake Information Center (NEIC) relies on in its global real‐time earthquake monitoring mission. Although waveform modeling‐based moment magnitudes are the modern standard to characterize earthquake size, is important because (1) in many cases, waveform modeling is not possible (e.g., low signal‐to‐noise events), (2) is applicable over a broad range of magnitudes, ∼M 4–7, and (3) there is a many decades‐long history of estimating magnitudes. We use the NEIC Preliminary Determination of Epicenters earthquake catalog to investigate the uncertainty in NEIC station measurements. We show that measurements are spatially correlated, which can bias event ⁠, and we describe an empirical relation between this spatial correlation and station‐to‐station distance. We further describe an approach to mitigate bias from the spatial correlation. Accounting for the spatial covariance of observations can change the event from −0.15 to 0.07 units (10th to 90th percentile) for smaller events (⁠⁠). These smaller events have the largest standard deviations ranging from 0.05 to 0.15 units (10th to 90th percentile).

The Seismic Record↗
USGS2026

SlideDetect: Spatio-temporal landslide detection using a three-dimensional convolutional neural network

Landslides pose a serious and ongoing threat to both human lives and infrastructure worldwide; therefore, it is of interest to predict where and when landslides are likely to occur. Advances in machine learning techniques have spurred numerous studies aimed at estimating relative landslide propensity, but are limited to spatial (as opposed to temporal) prediction due to the sparsity of landslide timing data. We address this data gap by training SlideDetect, a 3-dimensional convolutional neural network (3D CNN), to identify landslides based on their spatial and temporal occurrence within multitemporal image stacks. We use an inventory of landsides triggered by the 2018 Hokkaido earthquake and two years of monthly composite optical imagery spanning this event. The model can identify not only landslide location but also landslide date with an area under the precision-recall curve (PR-AUC) of 0.84. We further present a new standard for presenting PR curve results that explicitly compares model performance at different confidence thresholds, allowing for clearer model evaluation and comparison. Our new approach to constraining landslide timing paired with this more consistent and objective method for evaluating model performance shows considerable promise, and with further application and testing, SlideDetect could enhance the data availability and tools needed to advance landslide hazard and risk assessments.

JGR Machine Learning and Computation↗
USGS2026

Roadway runoff induced acute mortality in juvenile coho salmon during spring storm events

Extensive mortalities of adult coho salmon ( Oncorhynchus kisutch ), often called “Urban Runoff Mortality Syndrome” (URMS), have been documented during the fall in creeks where water quality has been degraded by roadway runoff. The primary cause of mortality is 6PPD-quinone (6PPDQ; N -(1,3-dimethylbutyl)- N ′-phenyl- p -phenylenediamine quinone)–an ozone transformation product that forms on all vehicle tires. Laboratory studies have shown that juvenile coho salmon are highly sensitive to 6PPDQ exposure. Unlike adults, juveniles reside in impacted watersheds year-round, including during the spring when 6PPDQ concentrations can frequently exceed lethal thresholds during storms. To assess the potential incidence of URMS in springtime rearing habitats for juvenile coho salmon, we conducted a paired water quality and toxicology study at Miller Creek, a runoff-impacted watershed in Normandy Park, WA, USA. Using a small field facility, three naïve groups of juvenile coho salmon ( N = 720) were exposed to either creek water or groundwater ( N = 120 per treatment per storm), across three spring storms while comparing water quality and mortality end points. In creek water during exposures, peak 6PPDQ concentrations reached 73–110 ng/L, exceeding reported median lethal concentrations (LC 50 ) for coho salmon. Over each 24–73 h storm exposure period, ∼80% of Miller Creek-exposed juvenile salmon died. No mortality occurred among control fish exposed to groundwater. These results indicate previously unidentified mortality risks for juvenile life stages of coho salmon during spring storms, suggesting substantial and year-round water quality impediments to coho salmon health and recovery across roadway runoff-impacted spawning, rearing, and migratory habitats.

Washington↗
USGS2024

Plan to coordinate post-earthquake investigations supported by the National Earthquake Hazards Reduction Program (NEHRP)

Introduction This report presents a plan supported by the National Earthquake Hazards Reduction Program (NEHRP) to coordinate domestic and international post-earthquake investigations (herein called “the Plan”). Post-earthquake scientific and engineering investigations are undertaken to capture critical information to understand the causes and impacts of the event, lessons from which can substantially improve the Nation’s resilience after future earthquakes. NEHRP is the Federal Government’s coordinated nationwide program to reduce risks to life and property from earthquakes. The Plan describes the activation and coordination of the four designated NEHRP Agencies in the Federal Government: Federal Emergency Management Agency, National Institute of Standards and Technology, U.S. National Science Foundation, and U.S. Geological Survey (USGS). The Plan also describes coordination between NEHRP Agencies and other organizations that may participate in pre-event and post-earthquake investigations, including non-NEHRP Federal agencies; State, regional, local, Tribal, and territorial agencies; domestic nongovernmental organizations; academic institutions and affiliated organizations; private companies; foreign governmental agencies and nongovernmental organizations; and international organizations. The Plan delineates the coordination of NEHRP post-earthquake scientific and engineering investigations to document the direct, indirect, and cascading physical and societal impacts from fault rupture and ground shaking hazards and from secondary hazards such as landslides, liquefaction, and tsunamis. In addition, the Plan identifies pre-event activities necessary to ensure that post-earthquake investigations are executed effectively. The USGS is the lead NEHRP Agency for activating and coordinating NEHRP post-earthquake investigations and for implementing this Plan. The USGS also leads coordination of the NEHRP Agencies in completing the pre-event activities identified in the Plan. The Plan has new information and supersedes USGS Circular 1242, “The Plan to Coordinate NEHRP Post-Earthquake Investigations,” which was published in 2003. This second-generation Plan was developed with the assistance of the Applied Technology Council (ATC) of Redwood City, California, under USGS contract 140G0121P0309, ATC-155. A 17-member Project Review Panel provided guidance on plan development, and input was solicited from subject matter experts representing key stakeholder groups and from participants at a public workshop.

Circular↗
USGS2025

Enhancing mineral systems exploration through geochronology, thermochronology, and isotope analysis: USGS Geochron and USGS Isotope databases

A mineral systems approach to mineral exploration provides a comprehensive framework for understanding ore deposit formation by examining the geodynamic, magmatic, hydrothermal, and sedimentary processes responsible for mineralization, alteration, and remobilization of economic mineral deposits. Temporal and thermal constraints on ore genesis are crucial for refining mineral system models and guiding predictive exploration strategies. Geochronology and thermochronology offer invaluable insights into the timing and thermal evolution of ore-forming processes, whereas isotopic analyses provide critical information on the source and geochemical history of ore-forming fluids. Combining these methodologies have proven highly effective for mineral exploration in regions like Australia, however, their combined application has been limited in the United States. To apply these tools to mineral systems-based exploration, the U.S. Geological Survey (USGS) has developed two products: (1) The USGS Geochron Database, and (2) the USGS Isotope Database. These databases provide centralized repositories of geo/thermochronological dates and data (Geochron Database) and both radiogenic and stable isotope data (Isotope Database) generated by the USGS and partners over the past decades. Integrating these datasets together and with traditional exploration approaches provides the mineral exploration community with powerful tools for determining the temporal and thermal histories of ore systems and identifying metallogenic source provinces.

Continental United States↗
USGS2026

Early Miocene volcanic rocks and associated tectonics, Lava Hills and southern Bristol Mountains, California

Volcanic rocks of latest Oligocene to early Miocene age form an east-west belt across part of the central eastern Mojave Desert from the Whipple Mountains on the east to the Rosamond Hills on the west. We term this the central belt because it is separated from northern and southern belts by swaths with no volcanic rocks. Limited geochronologic data indicate that much of the belt is latest Oligocene and early Miocene in age, about 24 to 19 Ma, a finding that is consistent with these rocks being overlain by the 18.8 Ma Peach Spring Tuff in many places. We describe Miocene geology in a central area of the belt, in the Lava Hills, southern Bristol Mountains, and southern Old Dad Mountains. Sedimentary basins formed coeval with early andesite to rhyolite volcanism, progressing from fluvial and lacustrine tuffaceous sandstone to volcanic lavas, tuffs, and breccias, indicating that early basins formed proximal to volcanic edifices. Higher strata are fluvial and lacustrine with lavas punctuating the sequence. Although basins may partly have been formed within topographic lows bounded by volcanic domes, plateaus, and stratovolcanoes, consistent stratigraphic sections over wide areas indicate that tectonic basin evolution affected broad areas. The volcanic section is capped by local basalt flows and the regional Peach Spring Tuff. Limited data on normal faults support interpretations of early extensional basin development caused by northeast-southwest oriented stretching. Later extension caused stratal rotations, tilting early deposits down to the southwest. This tilted and subsequently beveled basin architecture was overlain by the youngest volcanic deposits, primarily rhyolite and basalt. The Peach Spring Tuff, 18.8 Ma, lies within this upper unit. Similar stratigraphic and structural relations are exposed in the nearby Marble Mountains and Van Winkle Mountain sections, reinforcing that a broad area underwent similar volcanism and tectonism. In our study area the upper unit is only very gently tilted except near dextral strike-slip faults of the eastern California shear zone. These late Miocene to Recent faults are represented as four main fault zones spaced about 5 km apart, representing distributed shear north of the Bristol Lake basin.

California↗
USGS2022

South Carolina and Landsat

South Carolina, the eighth State admitted to the union, transcends its size with its deep, rich history; striking beauty; vast natural resources; and extensive cultural diversity. Home to part of the Blue Ridge Mountains of the Central Appalachians, the Upstate is graced with more than 100 waterfalls, while the Lowcountry borders the Atlantic Ocean with 187 miles of coastline and 35 barrier islands. Forests cover two-thirds of the State, and forestry and agriculture together, as agribusiness, make up South Carolina’s leading industry. Two historic crops—cotton and tobacco—still rank in the top 10 commodities, though corn and soybeans now rank higher. Poultry, cattle, peanuts, and flowers also make the list. South Carolina’s population totals more than five million. Other residents include a variety of wildlife, bird, reptile, and fish species, including Ursus americanus (black bears), Alligator mississippiensis (American alligators), and Tursiops truncatus (bottlenose dolphins). More than 100 tree species also reside in South Carolina, which pays homage to one with its “The Palmetto State” nickname. South Carolina’s subtropical climate, long coastline, and lower elevations make it highly susceptible to tornado and hurricane activity and coastal flooding. Projected sea-level rise is a growing concern. A view from space can help monitor and manage natural resources on the land and in rivers, marshes, and the coast. Landsat reveals not just what an area looks like now, but also insights from decades ago.

South Carolina↗
USGS2026

Groundwater dependency and hydroclimatic influences on riparian and upland vegetation productivity, Upper San Pedro, Arizona, United States

In arid and semi-arid regions, groundwater sustains vegetation through subsurface water access, yet the responses of groundwater-dependent ecosystems (GDEs) to changing hydroclimate and groundwater availability are relatively understudied. This study investigates seasonal and spatial patterns in vegetation greenness using Landsat Enhanced Vegetation Index (EVI) values across riparian and upland zones in the semi-arid Upper San Pedro (USP) watershed, southern Arizona, which experiences a bimodal precipitation regime. We paired 25 years (2000–2024) of EVI and depth to groundwater (DTG) data from 89 wells and climate metrics (precipitation and vapour pressure deficit) to quantify the sensitivity of vegetation to subsurface moisture as well as atmospheric moisture supply and demand. Vegetation at wells near the USP riparian area showed strong associations between EVI and DTG anomalies during the monsoon season, indicating sustained groundwater use even during this wet period when summer precipitation is abundant. In contrast, upland vegetation that lacked access to groundwater showed minimal sensitivity in EVI to DTG and was generally less responsive to vapour pressure deficit. Interestingly, the riparian GDEs were not decoupled from precipitation and climate variability. These results underscore the importance of groundwater for maintaining riparian productivity and highlight the utility of remote sensing in identifying vegetation-climate-groundwater linkages across heterogeneous dryland landscapes.

Arizona↗
USGS1998

Trace elements in streambed sediment and fish liver at selected sites in the Upper Colorado River Basin, Colorado 1995-96

Trace elements were analyzed in streambed-sediment samples collected from 16 sites and in fish-liver samples collected from 14 sites in the Upper Colorado River Basin in Colorado as part of the National Water-Quality Assessment program. Sites sampled represented agricultural, mining, mixed, and urban/recreation land uses and background conditions. The results for 15 trace elements in streambed-sediment and in fish-liver samples are presented in this report. Fourteen of the selected trace elements were detected in streambed-sediment samples collected at all sites. Twelve of the selected trace elements were detected in fish liver at more than 50 percent of the sites. Cadmium, copper, selenium, and zinc were selected for a more detailed analysis. Cadmium, copper, and zinc concentrations in streambed sediment were highest at mining land-use sites in the Southern Rocky Mountains physiographic province. Selenium concentrations in streambed sediment were highest at an agricultural land-use site in the Colorado Plateau physiographic province. The concentration of trace elements in streambed sediment generally increased as particle size decreased. Concentrations of trace elements in fish liver generally did not follow the same relation to land use as concentrations in streambed sediment; however, cadmium concentrations in fish liver were highest at a mining land-use site in the Southern Rocky Mountains physiographic province, and selenium concentrations in fish liver were highest at an agricultural land-use site in the Colorado Plateau physiographic province. Copper and zinc concentrations in fish liver were highest at mixed land-use sites. Comparison of streambed-sediment and fish-liver concentrations to two other similar NAWQA studies in the Rocky Mountain region generally indicated similar patterns in relation to land use for streambed sediment, but not for fish liver. Cadmium, copper, and zinc concentrations in streambed sediment were highest at sites affected by mining in all three study units. Selenium concentrations in streambed sediment did not indicate relations among the three study units when compared to land use. Cadmium in fish liver was highest at sites affected by mining in all three study units. Copper, selenium, and zinc in fish liver did not indicate relations among the three study units when compared to land use.

Colorado↗
USGS2026

Spatially referenced watershed models for the binational Red–Assiniboine River Basin: Bayesian vs frequentist comparison

Excess nutrient loading remains a leading cause of declining water quality in lakes, estuaries, and coastal waters worldwide, with global economic costs of US$200 billion – US$2 trillion annually from impacts on fisheries, tourism, freshwater resources, and water treatment. Our study focuses on total phosphorus (TP) in Lake Winnipeg and its binational Red-Assiniboine River Basin, where nutrient inputs have degraded water quality and increased cyanobacterial blooms. These changes pose ecological, public health, and economic risks. We applied a spatially referenced watershed model with a hybrid statistical-mechanistic structure partitioning annual nutrient loads into land-use export, land-to-water delivery, and in-reservoir decay. Bayesian and traditional frequentist model calibrations were compared. In the frequentist model, coefficients for agricultural inputs, forests /wetlands, stream channels, precipitation, and reservoir losses were statistically significant, whereas coefficient for wastewater was not. In contrast, all variables were successfully calibrated using the Bayesian approach. Model results delineate TP-export hotspots across the basin, showing that 54–62% of TP originates from the U.S., with agricultural sources ranging 62–72%—highlighting the importance of agriculture-focused Best Management Practices. Given the global relevance of nutrient-driven water-quality challenges, our results highlight Bayesian calibration for robust risk assessment and adaptive nutrient management.

Red–Assiniboine River Basin↗
USGS2024

Forecasting storm-induced coastal flooding for 21st century sea-level rise scenarios in the Hawaiian, Mariana, and American Samoan Islands

Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated Hawaiian, Mariana, and American Samoan Islands as a result of climate change and sea-level rise. We followed a hybrid (dynamical and statistical) downscaling approach to map flooding due to waves and storm surge at 10-square meter resolution along all 1,870 kilometers of these islands’ coastlines for annual (1-year), 20-year, and 100-year return-interval storm events and +0.00 meter (m), +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m sea-level rise scenarios. We quantified the coastal flood depths and extents using the latest climate forcing from Intergovernmental Panel for Climate Change’s Sixth Assessment Report Coupled Model Intercomparison Project. The data generated using these methods provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when climate change and sea-level rise increase coastal storm-induced flooding to help identify areas where management and (or) restoration could potentially help reduce the risk to, and increase the resiliency of, the coastal communities in the populated Hawaiian, Mariana, and American Samoan Islands.

Hawaii↗
USGS2025

Increased flood exposure in the Pacific Northwest following earthquake-driven subsidence and sea-level rise

Climate-driven sea-level rise is increasing the frequency of coastal flooding worldwide, exacerbated locally by factors like land subsidence from groundwater and resource extraction. However, a process rarely considered in future sea-level rise scenarios is sudden (over minutes) land subsidence associated with great (>M8) earthquakes, which can exceed 1 m. Along the Washington, Oregon, and northern California coasts, the next great Cascadia subduction zone earthquake could cause up to 2 m of sudden coastal subsidence, dramatically raising sea level, expanding floodplains, and increasing the flood risk to local communities. Here, we quantify the potential expansion of the 1% floodplain (i.e., the area with an annual flood risk of 1%) under low (~0.5 m), medium (~1 m), and high (~2 m) earthquake-driven subsidence scenarios at 24 Cascadia estuaries. If a great earthquake occurred today, floodplains could expand by 90 km 2 (low), 160 km 2 (medium), or 300 km 2 (high subsidence), more than doubling the flooding exposure of residents, structures, and roads under the high subsidence scenario. By 2100, when climate-driven sea-level rise will compound the hazard, a great earthquake could expand floodplains by 170 km 2 (low), 240 km 2 (medium), or 370 km 2 (high subsidence), more than tripling the flooding exposure of residents, structures, and roads under the high subsidence scenario compared to the 2023 floodplain. Our findings can support decision-makers and coastal communities along the Cascadia subduction zone as they prepare for compound hazards from the earthquake cycle and climate-driven sea-level rise and provide critical insights for tectonically active coastlines globally.

California, Oregon, Washington↗
USGS2024

The 2023 Alaska National Seismic Hazard Model

US Geological Survey (USGS) National Seismic Hazard Models (NSHMs) are used extensively for seismic design regulations in the United States and earthquake scenario development, as well as risk assessment and mitigation for both buildings and infrastructure. This 2023 update of the long-term, time-independent Alaska NSHM includes substantial changes to both the earthquake rupture forecast (ERF) and ground motion models (GMMs). The ERF includes numerous additions to the finite-fault model, considers two deformation models, and introduces updated declustering and smoothing algorithms in the gridded background seismicity model. For the Alaska–Aleutian subduction zone, megathrust earthquakes occur on an updated structural and segmentation model, and the moment magnitude (M) 8+ rupture and rate model include a logic tree branch that considers slip rates derived from geodetic models of interface coupling. The megathrust model considers multiple models of down-dip width, and magnitudes are computed using newly developed scaling relations. For subduction intraslab events and subduction interface events with M < 7, the 2023 update uses a smoothed seismicity model with rupture depths derived from Slab2. The 2023 model updates GMMs in all tectonic settings using the recently published Next Generation Attenuation Subduction (NGA-Sub) GMMs for subduction interface and intraslab events, and the NGA-West2 GMMs for active crustal settings. Collectively, additions and updates to the Alaska NSHM result in hazard increases across most of south-central Alaska relative to the previous model, published in 2007. These changes are primarily due to the adoption of updated rate models for the large-magnitude interface events and the NGA-Sub GMMs that have much higher aleatory variability (sigma), consistent with global observations, and that include models of epistemic uncertainty.

Alaska↗
USGS2026

Spatio-temporal modeling for assessing geoenergy resources: A workflow applied to gas in place variation in coal beds

The ability to estimate spatio-temporal changes in hydrocarbon reservoir properties and energy resources within pore volumes is essential for optimizing production, reservoir management, geologic energy storage, and safety in underground mining operations. In coal seams, predicting remaining methane gas-in-place (GIP) is critical for quantifying producible gas and improving mine safety and productivity through effective ventilation planning. Although such changes are commonly evaluated using physics-based numerical simulation models, these approaches often require extensive data, calibration effort, and time. This study presents a spatio-temporal geostatistical modeling approach that bridges the gap between purely spatial models and full numerical simulations. The method is applied to a case study of coal seam degasification in the Mary Lee coal group, Black Warrior Basin, Alabama, USA, to estimate GIP evolution over time within a selected mining district. The analysis uses published data from prior natural gas production history-matching of degasification using vertical wells. Empirical spatial and temporal statistics were calculated for reservoir pressure and water saturation, and spatio-temporal variogram models were fitted to experimental variograms. These models provided the structural basis for spatio-temporal kriging, integrated with spatial estimates of time-invariant parameters (porosity, density, and thickness) to estimate GIP. This approach enabled estimation of GIP changes over time, including periods without data. Boxplots of GIP estimates indicated systematic depletion and decreasing spatial variability, reflecting the impacts of degasification. Comparison with cumulative gas production from empirical well records showed approximately 85% agreement based on a relative similarity metric. Spatio-temporal GIP estimates were also used to estimate methane emissions to longwall ventilation systems and compared with reported emissions from the U.S. EPA Greenhouse Gas Reporting Program, showing similar distributions (≈80%) given data limitations. Overall, this integrated modeling approach provides time-dependent GIP estimates with broader implications for resource assessment applications.

Alabama↗
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↗