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We used matched filter detection and multiple-event relocation techniques to characterize the spatiotemporal evolution of the sequence. Our analysis is from the 14 closest seismic stations to the earthquake sequence, which included seven permanent stations from the Montana Regional Seismic Network, one permanent station from the ANSS backbone network and three temporary seismic stations deployed by the USGS within four days after the mainshock. A catalog of 685 well-located earthquakes larger than M 1 occurring Between 5 July and 15 October 2017 were relocated using a hypocentroid decomposition (HD) multiple-event relocation approach. The resulting dataset had an average epicentral and depth uncertainties (90% confidence)...
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This data release supersedes version 1.0, published in September 2022 at https://doi.org/10.5066/P9XEFRYR. Versioning details are documented in the accompanying Dunex_revision_history.txt file. These data provide grain-size measurements from sediment samples collected as part of the USGS DUring Nearshore Event eXperiment (DUNEX) site on Pea Island National Wildlife Refuge, NC. DUNEX is a multi-agency, academic, and non-governmental organization collaborative community experiment designed to study nearshore coastal processes during storm events. USGS participation in DUNEX will contribute new measurements and models that will increase our understanding of storm impacts to coastal environments, including hazards...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Marine geophysical mapping of the Queen Charlotte Fault in the eastern Gulf of Alaska was conducted in 2016 as part of a collaborative effort between the U.S. Geological Survey and the Alaska Department of Fish and Game to understand the morphology and subsurface geology of the entire Queen Charlotte system. The Queen Charlotte fault is the offshore portion of the Queen Charlotte-Fairweather Fault: a major structural feature that extends more than 1,200 kilometers from the Fairweather Range of southern Alaska to northern Vancouver Island, Canada. The data published in this data release were collected along the Queen Charlotte Fault between Cross Sound and Noyes Canyon, offshore southeastern Alaska from May 18 to...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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This data release provides a map of the time-averaged shear-wave velocity in the upper 30 m (Vs30) for California using the method described by Thompson and others (2014). There are two adjustments to the algorithm described by Thompson and others (2014), which is built on the geology-based Vs30 map by Wills and Clahan (2006). In this data release, we use the Wills and others (2015) updated geology-based Vs30 map. The second change is that we have adjusted the kriging procedure so that measured Vs30 values do not affect the predictions across distinctly different geologic units. July 2022 Update (ver. 2.0) Resolution is now 3 arcseconds instead of 7.5 arcseconds Fixed a code error that prevented some of the Vs30...
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The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers understand which areas of the coast are vulnerable to change. This data release, and other associated...
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This data release comprises a georeferenced raster layer depicting the estimated susceptibility to intense rainfall-induced landslides in Puerto Rico, which is a supplement to: Hughes, K.S., and Schulz, W.H., 2020, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-File Report 2020–1022, 91 p., 1 plate, scale 1:150,000, https://doi.org/10.3133/ofr20201022. Users of this layer are strongly encouraged to read the text herein and available with Open-File Report 2020-1022. DEVELOPMENT OF THE LANDSLIDE SUSCEPTIBILITY MAP Landslides commonly occur in Puerto Rico during or soon after intense rainfall and present significant hazards to the built environment...
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The GeoTiff raster data were created from subtraction of elevation values collected in 2012 from elevation values collected in 2011. Both data collections were conducted by airborne lidar surveys contracted by the Minnesota Department of Natural Resources. These survey occured before and after a historical rain event that cause widespread landscape change. The shapefiles are polygon objects that illustrate where erosion and deposition occurred on slopes and in valleys in the June 2012 rain event in the Duluth, MN region
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This dataset contains linework of lineaments mapped on 4 <1-m-resolution lidar datasets and the 10-m-resolution National Elevation Dataset digital elevation models in the Pit River region of northeastern California. Lineaments are classified by confidence in tectonic origin, map certainty, and the ages of the bedrock and surficial deposits they cross.
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The radiogenic isotope ratios of strontium (Sr) and uranium (U), specifically 87Sr/86Sr and 234U/238U, are useful tracers of water-rock interactions. Sr isotopic compositions in groundwater are mostly controlled by dissolution or exchange with Sr contained in aquifer rocks whereas the U isotopic compositions are more controlled by chemical and kinetic processes during groundwater flow. Insights into groundwater circulation patterns through the shallow subsurface at Yellowstone National Park can be aided by investigations of these isotopes. This data release contains tables with new isotope data consisting of concentrations (Sr, U) and radiogenic-isotope compositions (87Sr/86Sr, 234U/238U) for water samples from...
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Gravity data were collected in August of 2019 at 21 sites on and around Iliamna Volcano and Anchorage, Alaska. Measurements were taken with a Lacoste & Romberg G-161 meter and reduced to obtain the complete Bouguer anomaly. A total of 39 magnetic susceptibility measurements were taken at 13 locations using a ZH Instruments SM30 susceptibility meter. This data release includes susceptibility measurements, processed gravity data, shapefiles with field locations, and site photos.
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This maps portrays the spatial potential for damaging earthquake ground shaking quantified as considerable (MMI ≥ VIII) in 100 years. The maps and data are based on the average of the results obtained from peak ground acceleration and 1.0-second horizontal spectral acceleration. Site specific soil factors based on Vs30 shear wave velocities were implemented using a simple topographic proxy technique (Allen and Wald, 2009) and site amplification based on the relationships of Seyhan and Stewart (2014). MMI ≥ VIII is equivalent to peak ground acceleration of 0.40g and 1.0-second horizontal spectral acceleration of 0.50g (Worden et al., 2012). Allen, T.A. and Wald, D.J. 2009,. On the use of high-resolution topographic...


map background search result map search result map An Updated Vs30 Map for California with Geologic and Topographic Constraints (ver. 2.0, July 2022) Spatiotemporal Analysis of the Foreshock-Mainshock-Aftershock Sequence of the 6 July 2017 M5.8 Lincoln, Montana, Earthquake - Data Release Trackline navigation collected with a Reson 7160 Multibeam echosounder in the eastern Gulf of Alaska during USGS Field Activity 2016-625-FA (Esri polyline shapefile, UTM 8 WGS 84) DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Coast Guard Beach, MA, 2014 ElevMHW: Elevation adjusted to local mean high water: Coast Guard Beach, MA, 2014 ElevMHW: Elevation adjusted to local mean high water: Parker River, MA, 2014 DisOcean: Distance to the ocean: Assateague Island, MD & VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cobb Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fisherman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Metompkin Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Parramore Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Wreck Island, VA, 2014 Bouguer gravity and magnetic susceptibility measurements at Iliamna Volcano, Alaska 2019 Raster and mapping data indicating landscape change from the June 2012 storm in the Duluth, MN region Geographic Information System Layer of a Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico Shoreline intersects for the coast of Puerto Rico's main island generated by the Digital Shoreline Analysis System version 5.1 (ver. 2.0, March 2023) Lineament mapping from lidar datasets in the Pit River region, northeastern California Sr and U concentrations and radiogenic isotope compositions (87Sr/86Sr, 234U/238U) of thermal waters, streamflow, travertine, and rock samples along with U-Th disequilibrium ages for travertine deposits from various locations in Yellowstone National Park, USA Grain-size analysis data of sediment samples from the beach and nearshore environments at the Pea Island National Wildlife Refuge DUNEX site, North Carolina in 2021 Grain-size analysis data of sediment samples from the beach and nearshore environments at the Pea Island National Wildlife Refuge DUNEX site, North Carolina in 2021 ElevMHW: Elevation adjusted to local mean high water: Wreck Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fisherman Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Coast Guard Beach, MA, 2014 ElevMHW: Elevation adjusted to local mean high water: Coast Guard Beach, MA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cobb Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Metompkin Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Parramore Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Parker River, MA, 2014 DisOcean: Distance to the ocean: Assateague Island, MD & VA, 2014 Lineament mapping from lidar datasets in the Pit River region, northeastern California Sr and U concentrations and radiogenic isotope compositions (87Sr/86Sr, 234U/238U) of thermal waters, streamflow, travertine, and rock samples along with U-Th disequilibrium ages for travertine deposits from various locations in Yellowstone National Park, USA Spatiotemporal Analysis of the Foreshock-Mainshock-Aftershock Sequence of the 6 July 2017 M5.8 Lincoln, Montana, Earthquake - Data Release Shoreline intersects for the coast of Puerto Rico's main island generated by the Digital Shoreline Analysis System version 5.1 (ver. 2.0, March 2023) Geographic Information System Layer of a Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico Raster and mapping data indicating landscape change from the June 2012 storm in the Duluth, MN region Bouguer gravity and magnetic susceptibility measurements at Iliamna Volcano, Alaska 2019 Trackline navigation collected with a Reson 7160 Multibeam echosounder in the eastern Gulf of Alaska during USGS Field Activity 2016-625-FA (Esri polyline shapefile, UTM 8 WGS 84) An Updated Vs30 Map for California with Geologic and Topographic Constraints (ver. 2.0, July 2022)