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At least 1,045 records · Page 58Linked to original sources
USGS2025

Seismic response of rock towers at the Trona Pinnacles (U.S.A.) to the 2019 Ridgecrest earthquake sequence: Theory, observations, and models

We analyze the seismic response of a class of fragile geologic features (FGFs), referred to as rock towers (RTs) at the Trona Pinnacles, a group of RTs in southern California that suffered strong shaking during the 2019 Ridgecrest earthquake sequence. FGFs, including RTs, may provide maximum constraints on past earthquake shaking intensity, and thereby support probabilistic seismic hazard assessments (PSHAs). In a rare case study, we explore the hypothesis that RT structural integrity is time dependent, as damage accumulates progressively. We develop finite‐element method (FEM) models of the RTs using photogrammetric shape models. We validate the models by comparing numerical simulations of their response to broadband ground shaking with low‐intensity seismic recordings obtained at the Pinnacles. Results of our simulations are in good agreement with the seismic recordings of actual earthquake aftershocks. We next use the results of the FEM models to analyze the response and evolution of RTs. Our analyses elucidate the influence of geometry over their seismic response, providing a rationale that may explain the rarity of slender RTs at Trona: high‐aspect‐ratio structures that respond in bending develop detrimental tensile stresses that crack the rock, whereas low‐aspect‐ratio ones’ response also includes shearing, which does not compromise material integrity as much as tension. Field measurements with a rebound hammer support this finding, suggesting that the material around the base of slender rocks has been weakened relative to other parts of the RT. We also study how to define simplified mechanical models (“archetypes”) to predict the natural frequencies of RTs. Results from our work illuminate the fundamental mechanisms of seismic response and progressive failure of RTs, and open new avenues of research to potentially incorporate these geologic features as long‐return period constraints on PSHA, in ways analogous to those of the widely used precariously balanced rocks.

California↗
USGS2010

EAARL Coastal Topography-Chandeleur Islands, Louisiana, 2010: Bare Earth

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and submerged topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the Chandeleur Islands, acquired March 3, 2010. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2010

EAARL Coastal Topography-Eastern Florida, Post-Hurricane Jeanne, 2004: First Surface

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the eastern Florida coastline beachface, acquired post-Hurricane Jeanne (September 2004 hurricane) on October 1, 2004. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2013

EAARL coastal topography and imagery–Western Louisiana, post-Hurricane Rita, 2005: First surface

These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived first-surface (FS) topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, Virginia. This project provides highly detailed and accurate datasets of a portion of the Louisiana coastline beachface, acquired post-Hurricane Rita on September 27-28 and October 2, 2005. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the National Aeronautics and Space Administration (NASA) Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Website.

Louisiana↗
USGS2014

EAARL-B submerged topography: Barnegat Bay, New Jersey, pre-Hurricane Sandy, 2012

These remotely sensed, geographically referenced elevation measurements of lidar-derived submerged topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida. This project provides highly detailed and accurate datasets for part of Barnegat Bay, New Jersey, acquired pre-Hurricane Sandy on October 18, 22, 23, and 26, 2012. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar, known as the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), was used during data acquisition. The EAARL-B system is a raster-scanning, waveform-resolving, green-wavelength (532-nm) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL-B sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, down-looking red-green-blue (RGB) and infrared (IR) digital cameras, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL-B platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL-B system. The resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed originally in a NASA-USGS collaboration. The exploration and processing of lidar data in an interactive or batch mode is supported using ALPS. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. The Airborne Lidar Processing System (ALPS) is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Web site.

New Jersey↗
USGS2014

EAARL-B coastal topography: Fire Island, New York, pre-Hurricane Sandy, 2012: seamless (bare earth and submerged)

These remotely sensed, geographically referenced elevation measurements of lidar-derived seamless (bare-earth and submerged) topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida. This project provides highly detailed and accurate datasets for part of Fire Island, New York, acquired pre-Hurricane Sandy on October 27, 2012. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar, known as the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), was used during data acquisition. The EAARL-B system is a raster-scanning, waveform-resolving, green-wavelength (532-nm) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL-B sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, down-looking red-green-blue (RGB) and infrared (IR) digital cameras, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL-B platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL-B system. The resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed originally in a NASA-USGS collaboration. The exploration and processing of lidar data in an interactive or batch mode is supported using ALPS. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. The Airborne Lidar Processing System (ALPS) is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Web site.

New York↗
USGS2020

Are elevation and open-water conversion of salt marshes connected?

Salt marsh assessments focus on vertical metrics such as accretion or lateral metrics such as open-water conversion, without exploration of how the dimensions are related. We exploited a novel geospatial dataset to explore how elevation is related to the unvegetated-vegetated marsh ratio (UVVR), a lateral metric, across individual marsh “units” within four estuarine-marsh systems. We find that elevation scales consistently with the UVVR across systems, with lower elevation units demonstrating more open-water conversion and higher UVVRs. A normalized elevation-UVVR relationship converges across systems near the system-mean elevation and a UVVR of 0.1, a critical threshold identified by prior studies. This indicates that open-water conversion becomes a dominant lateral instability process at a relatively conservative elevation threshold. We then integrate the UVVR and elevation to yield lifespan estimates, which demonstrate that higher elevation marshes are more resilient to internal deterioration, with an order-of-magnitude longer lifespan than predicted for lower elevation marshes.

Massachusetts, New York, Maryland, Virginia↗
USGS2009

EAARL Coastal Topography-Assateague Island National Seashore, 2008: First Surface

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Assateague Island National Seashore in Maryland and Virginia, acquired March 24-25, 2008. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for pre-survey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations.

Data Series↗
USGS2009

EAARL Coastal Topography-Assateague Island National Seashore, 2008: Bare Earth

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Assateague Island National Seashore in Maryland and Virginia, acquired March 24-25, 2008. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL) was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for pre-survey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations.

Data Series↗
USGS2011

An Analysis of the Published Mineral Resource Estimates of the Haji-Gak Iron Deposit, Afghanistan

The Haji-Gak iron deposit of eastern Bamyan Province, eastern Afghanistan, was studied extensively and resource calculations were made in the 1960s by Afghan and Russian geologists. Recalculation of the resource estimates verifies the original estimates for categories A (in-place resources known in detail), B (in-place resources known in moderate detail), and C 1 (in-place resources estimated on sparse data), totaling 110.8 Mt, or about 6% of the resources as being supportable for the methods used in the 1960s. C 2 (based on a loose exploration grid with little data) resources are based on one ore grade from one drill hole, and P 2 (prognosis) resources are based on field observations, field measurements, and an ore grade derived from averaging grades from three better sampled ore bodies. C 2 and P 2 resources are 1,659.1 Mt or about 94% of the total resources in the deposit. The vast P 2 resources have not been drilled or sampled to confirm their extent or quality. The purpose of this article is to independently evaluate the resources of the Haji-Gak iron deposit by using the available geologic and mineral resource information including geologic maps and cross sections, sampling data, and the analog-estimating techniques of the 1960s to determine the size and tenor of the deposit.

Haji-Gak iron deposit in Eastern Afghanistan↗
USGS2008

Soil sedimentology at Gusev Crater from Columbia Memorial Station to Winter Haven

A total of 3140 individual particles were examined in 31 soils along Spirit's traverse. Their size, shape, and texture were quantified and classified. They represent a unique record of 3 years of sedimentologic exploration from landing to sol 1085 covering the Plains Unit to Winter Haven where Spirit spent the Martian winter of 2006. Samples in the Plains Unit and Columbia Hills appear as reflecting contrasting textural domains. One is heterogeneous, with a continuum of angular-to-round particles of fine sand to pebble sizes that are generally dust covered and locally cemented in place. The second shows the effect of a dominant and ongoing dynamic aeolian process that redistributes a uniform population of medium-size sand. The texture of particles observed in the samples at Gusev Crater results from volcanic, aeolian, impact, and water-related processes.

Journal of Geophysical Research E: Planets↗
USGS2023

Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data

Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)-based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three-layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three-layer composite generally provided lower overall losses for the training samples, a higher F1-score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN-based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN-based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three-layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.

Earth and Space Science↗
USGS2024

Assisted migration of coho salmon: Influences of passage and habitat availability on population dynamics

Assisted migration is a means of introducing a species into a previously unoccupied area. Although this idea is relatively new for many species, there are many extant examples involving fish that can be instructive. We studied a case of assisted migration where upstream access of migrating adult coho salmon Oncorhynchus kisutch over a naturally impassible barrier was established through construction of fish ladders. Although these passage structures have successfully allowed coho salmon to colonize upstream locations, managers had concerns regarding how efficiently these structures passed fish, as well as questions regarding access to specific upstream habitats, and passage barriers further upstream. To address these concerns, we developed a stage-based population model to explore: (1) influences of passage over structures, (2) rearing habitats upstream of the structures, and (3) consequences of additional barriers to passage in the system. Model simulations suggest high fish passage at the ladders was associated with the highest smolt and adult abundance of coho salmon. The importance of passage was strongly influenced by juveniles rearing in a lake, where increased lake rearing at each passage scenario increased abundance of smolts and adults. Opening habitat further upstream was estimated to increase adult and smolt abundance up to 12%. Results of model simulations also helped to identify uncertainties that could be evaluated further (e.g., juvenile rearing in the lake). In general, our findings point to the importance of considering a full range of processes that can drive expected outcomes for assisted migration.

Oregon↗
USGS1979

Catalog of earthquakes in southern Alaska: January-March 1978

The National Center for Earthquake Research of the U.S. Geological Survey (USGS) began a program of telemetered seismic recording in south-central Alaska in 1971. The principal objectives of this program have been to use data recorded by this network to precisely locate earthquakes in the active seismic zones of southern Alaska, to delineate seismically active faults, to assess seismic risk, to document potential premonitory earthquake phenomena, to investigate current tectonic deformation, and to study the structure and physical properties of the crust and upper mantle. A task fundamental to all of these goals is the routine cataloging of earthquake parameters for earthquakes located within and adjacent to the seismograph network. The initial network of 10 stations, 7 around Cook Inlet and 3 near Valdez, was installed in 1971. Each summer since then additions or modifications to the network have been made. By the Fall of 1973, 26 stations extended from western Cook Inlet to eastern Prince William Sound, and 4 stations were located between Cordova and Yakutat. A year later 20 additional stations were installed. Thirteen of these were placed along the eastern Gulf of Alaska with support from the National Oceanic and Atmospheric Administration (NOAA) under the Outer Continental Shelf Environmental Program to investigate the seismicity of the outer continental shelf (OCS) region of interest for oil exploration. During the subsequent years the region covered by the network has remained relatively fixed while effort has been made to improve the instrumentation and installation of the stations in order to make them more reliable. This earthquake catalog presents origin times, focal coordinates and magnitudes for 384 shocks occurring in the first quarter of 1978. Readings from a total of 62 stations were used to locate the shocks, including 11 stations operated by the NOAA Alaska Tsunami Warning Center (formerly Palmer Observatory), and 5 stations operated by the Geophysical Institute of the University of Alaska (U. of A.). Earthquakes in south-central Alaska as small as magnitude 3.0 have been routinely located by the National Earthquake Information Service of the USGS and its predecessor since the great Alaska earthquake of 1964 and published in the reports "Preliminary Determination of Epicenters" (PDE). In contrast the shocks included in this catalog are as small as magnitude 1.0 and most are smaller than magnitude 3.0. Data for the larger historic earthquakes in south-central Alaska have been tabulated by Meyers (1976). The locations of the stations of the USGS seismograph network are plotted in Figure 1 and listed in Table 1 along with the additional stations from which readings were obtained. The USGS stations have single, vertical-component seismometers except for GLB, PNL, RDT, SKN, and VLZ which also have two horizontal seismometers.

Alaska↗
USGS2025

Hyperspectral imaging of river bathymetry using an ensemble of regression trees

Remote sensing has emerged as an effective tool for characterizing river systems, and machine learning (ML) techniques could make this approach even more powerful. To explore this possibility, we developed an ML-based workflow for hyperspectral imaging of river bathymetry using an ensemble of regression trees (HIRBERT). This approach involves using paired observations of depth and reflectance to select wavelength bands as predictors and then train a depth retrieval model; applying the model to the image yields a spatially continuous bathymetric map. We used data from five rivers with diverse morphologies and optical characteristics to assess whether HIRBERT can (1) provide more accurate depth estimates than a band ratio-based algorithm and (2) extend the range of depths detectable via remote sensing. Relative to single band combinations identified via optimal band ratio analysis (OBRA), regression tree ensembles improved depth retrieval performance, with observed versus predicted (OP) regression R 2 values increasing for all five sites. Similarly, HIRBERT provided more reliable depth estimates than OBRA over the full range of depths present along each river. These results suggest that by incorporating additional spectral information from multiple wavelength bands, ML could enhance bathymetric mapping across a range of river environments. In addition, we show how graphical tools can facilitate interpretation of ML-based depth retrieval models and yield insight regarding relationships between depth and reflectance. The HIRBERT workflow is packaged in free, standalone software developed to support applications in river research and management. Although ML can enhance remote sensing of river bathymetry, the limitations of this approach must also be acknowledged: Field measurements of water depth are required to train a depth retrieval model and the resulting model should only be applied to the image from which the training data were derived. The inherently image-specific nature of this approach implies that developing generalized regression tree ensembles that could be applied at larger scales would require additional research.

California, Idaho, Nebraska, Oregon, Wyoming↗
USGS2007

Mineralogic constraints on sulfur-rich soils from Pancam spectra at Gusev crater, Mars

The Mars Exploration Rover (MER) Spirit excavated sulfur-rich soils exhibiting high albedo and relatively white to yellow colors at three main locations on and south of Husband Hill in Gusev crater, Mars. The multispectral visible/near-infrared properties of these disturbed soils revealed by the Pancam stereo color camera vary appreciably over small spatial scales, but exhibit spectral features suggestive of ferric sulfates. Spectral mixture models constrain the mineralogy of these soils to include ferric sulfates in various states of hydration, such as ferricopiapite [Fe2/32+Fe43+ (SO4)6(OH)2??20(H2O)], hydronium jarosite [(H3O)Fe33+ (SO4)2(OH)6], fibroferrite [Fe3+(SO4)(OH)??5(H2O)], rhomboclase [HFe3+(SO4)2??4 (H2O)], and paracoquimbite [Fe23+ (SO4)3.9(H2O)]. Copyright 2007 by the American Geophysical Union.

Geophysical Research Letters↗
USGS2005

Geography of spring landbird migration through riparian habitats in southwestern North America

Migration stopover resources, particularly riparian habitats, are critically important to landbirds migrating across the arid southwestern region of North America. To explore the effects of species biogeography and habitat affinity on spring migration patterns, we synthesized existing bird abundance and capture data collected in riparian habitats of the borderlands region of the U.S. and Mexico. We determined the importance of geographic factors (longitude and latitude) in explaining variation in abundances and capture rates of 32 long-distance and three short-distance migrant species. Abundances and capture rates of 13 and 11 species, respectively, increased with increasing longitude, and four species' abundance and capture rates decreased with increasing longitude. Riparian associates, but not nonriparian species, were more abundant in western sites. Their abundance patterns were only weakly influenced by species biogeography. In contrast, biogeography did influence abundance patterns of nonriparian birds, suggesting that they choose the shortest, most direct route between wintering and breeding areas. We hypothesize that riparian obligate birds may, to some degree, adjust their migration routes to maximize time spent in high-quality riparian zones, but they are able to find suitable habitat opportunistically when crossing more hostile landscapes. In contrast, nonriparian birds adhere more closely to a hierarchical model in which the migratory route is determined by biogeographic constraints. Conservation of riparian habitats is necessary to meet future habitat stopover requirements of many western Neotropical migrant birds. We advocate a coordinated research effort to further elucidate patterns of distribution and habitat use so that conservation activities can be focused effectively.

The Condor↗
USGS1975

Linkage effects between deposit discovery and postdiscovery exploratory drilling

For the 1950-71 period of petroleum exploration in the Powder River Basin, northeastern Wyoming and southeastern Montana, three specific topics were investigated. First, the wildcat wells drilled during the ambient phases of exploration are estimated to have discovered 2.80 times as much petroleum per well as the wildcat wells drilled during the cyclical phases of exploration, periods when exploration plays were active. Second, the hypothesis was tested and verified that during ambient phases of exploration the discovery of deposits could be anticipated by a small but statistically significant rise in the ambient drilling rate during the year prior to the year of discovery. Closer examination of the data suggests that this anticipation effect decreases through time. Third, a regression model utilizing the two independent variables of (1) the volume of petroleum contained in each deposit discovered in a cell and the directly adjacent cells and (2) the respective depths of these deposits was constructed to predict the expected yearly cyclical wildcat drilling rate in four 30 by 30 min (approximately 860 mi 2 ) sized cells. In two of these cells relatively large volumes of petroleum were discovered, whereas in the other two cells smaller volumes were discovered. The predicted and actual rates of wildcat drilling which occurred in each cell agreed rather closely.

Montana, Wyoming↗