Geology topics

Research about Anchorage, Alaska

Source-linked reports with geographic coverage including Anchorage, Alaska.

At least 19 recordsLinked to original sources

USGS2025

Detection of Giardia and Cryptosporidium in surface water of a subarctic city

Giardia and Cryptosporidium spp. are globally distributed protozoan parasites that can cause gastrointestinal disease in humans and animals. These zoonotic parasites and their ecological relationships have been understudied in Alaska and elsewhere, despite being identified as priority zoonotic pathogens. We aimed to detect and characterize Giardia and Cryptosporidium spp. in waterbodies within Anchorage, Alaska, USA using two methods, including the Environmental Protection Agency (EPA) Method 1623 that relies on microscopy and a molecular detection approach. The molecular approach was ultimately unsuccessful and therefore only data obtained using Method 1623 are presented. Giardia or Cryptosporidium spp. was detected from nine of 15 urban streams and lakes sampled (60%), six of which were positive for both parasites (40%). Fewer than 10 cysts or oocysts were detected in 10 L of surface water. Further research to characterize Giardia and Cryptosporidium beyond the genus level would help elucidate the zoonotic potential and ecology of these parasites within the region and more broadly in Alaska.

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USGS2021

Evaluation of remote mapping techniques for earthquake-triggered landslide inventories in an urban subarctic environment: A case study of the 2018 Anchorage, Alaska Earthquake

Earthquake-induced landslide inventories can be generated using field observations but doing so can be challenging if the affected landscape is large or inaccessible after an earthquake. Remote sensing data can be used to help overcome these limitations. The effectiveness of remotely sensed data to produce landslide inventories, however, is dependent on a variety of factors, such as the extent of coverage, timing, and data quality, as well as environmental factors such as atmospheric interference (e.g., clouds, water vapor) or snow and vegetation cover. With these challenges in mind, we use a combination of field observations and remote sensing data from multispectral, light detection and ranging (lidar), and synthetic aperture radar (SAR) sensors to produce a ground failure inventory for the urban areas affected by the 2018 magnitude (M w ) 7.1 Anchorage, Alaska earthquake. The earthquake occurred during late November at high latitude (∼61°N), and the lack of sunlight, persistent cloud cover, and snow cover that occurred after the earthquake made remote mapping challenging for this event. Despite these challenges, 43 landslides were manually mapped and classified using a combination of the datasets mentioned previously. Using this manually compiled inventory, we investigate the individual performance and reliability of three remote sensing techniques in this environment not typically hospitable to remotely sensed mapping. We found that differencing pre- and post-event normalized difference vegetation index maps and lidar worked best for identifying soil slumps and rapid soil flows, but not as well for small soil slides, soil block slides and rock falls. The SAR-based methods did not work well for identifying any landslide types because of high noise levels likely related to snow. Some landslides, especially those that resulted in minor surface displacement, were identifiable only from the field observations. This work highlights the importance of the rapid collection of field observations and provides guidance for future mappers on which techniques, or combination of techniques, will be most effective at remotely mapping landslides in a subarctic and urban environment.

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USGS2021

Response of an asymmetrical five-story building in Fairbanks, Alaska during the November 30, 2018 M7.1 Anchorage, Alaska earthquake

A recently constructed, five-story, asymmetrical steel building on the campus of the University of Alaska, Fairbanks was equipped with a strong-motion array that recorded the M7.1 Anchorage earthquake of November 30, 2018 at an epicentral distance of 408 km. The largest recorded peak accelerations at the basement and top of the building are 0.021g and 0.071g, respectively. The steel building is designed with several bays that utilize K-shaped buckling restrained braces. The building response records allow identification of fundamental periods (frequencies) as 0.73s (1.4 Hz), 0.63s (1.60 Hz), and 0.56s (1.78 Hz) in the NS, EW, and torsional directions, respectively. System identification computations resulted in estimated critical damping percentages as 7.7% and 4.7 % in the NS and EW directions, respectively. At this low-level of shaking, the building is not expected to (and did not) experience observable damage, which is confirmed with very small average drift ratios. This is the first time a seismic response from this structural array has been analyzed.

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USGS2020

Citizen science collaboration with the U.S. Geological Survey in Alaska

Citizen science is science undertaken by the public, usually in collaboration with professional scientific institutions. It encourages citizens to tackle real-world scientific problems and augments traditional science by expanding the coverage of data collection and by reducing costs of fieldwork in remote locations. Information collected by volunteers enables us all to gain a deeper understanding of how the natural world works and how it is changing. The U.S. Geological Survey is a leading Federal agency in fostering citizen-science collaborations, and the examples in this publication show the value of those collaborations.

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USGS2020

Ground failure triggered by shaking during the November 30, 2018, magnitude 7.1 Anchorage, Alaska, earthquake

We developed an initial inventory of ground failure features from the November 30, 2018, magnitude 7.1 Anchorage earthquake. This inventory of 153 features is from ground-based observations soon after the earthquake (December 5–10) that include the presence or absence of liquefaction, landslides, and individual crack traces of lateral spreads and incipient landslides. This is not a complete inventory and simply shows general trends and examples of types and distribution of ground failures documented. Overflight observations (December 1–6) documented landslide and liquefaction presence or absence within the Chugach Mountains and along Cook Inlet in regions inaccessible to vehicles, which greatly expanded the geographic scope of this reconnaissance. Field-mapped ground-failure observations have been augmented with a set of 565 georeferenced and annotated images from both field and overflight reconnaissance cataloging additional ground-failure presence or absence from the Anchorage earthquake. Tidal erosion, fresh snowfall, limited daylight, and adverse flying conditions contributed significantly to the uncertainty and incompleteness of ground-failure observations during this reconnaissance. Notably, substantial liquefaction features at the mouths of the Little Susitna River (December 1) and Ingram Creek (December 5) were absent during subsequent overlapping missions (December 6 and 9, respectively) because of tidal action. A large rockfall observed on December 1 on Rainbow Peak was not observed during a subsequent December 5 overflight because of snow cover, which suggests that the general lack of observations of landsliding within the Chugach Mountains is uncertain.

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USGS2019

Evaluation of ground‐motion models for U.S. Geological Survey seismic hazard models: 2018 Anchorage, Alaska, Mw 7.1 subduction zone earthquake sequence

Instrumental ground‐motion recordings from the 2018 Anchorage, Alaska ( ⁠ M w "> M w 7.1), earthquake sequence provide an independent data set allowing us to evaluate the predictive power of ground‐motion models (GMMs) for intraslab earthquakes associated with the Alaska subduction zone. In this study, we evaluate 15 candidate GMMs using instrumental ground‐motion observations of peak ground acceleration and 5% damped pseudospectral acceleration (0.02–10 s) to inform logic‐tree weights for the update of the U.S. Geological Survey seismic hazard model for Alaska. GMMs are evaluated using two methods. The first is a total residual visualization approach that compares the probability density function, mean, and standard deviations σ "> σ of the observed and predicted ground motion. The second GMM evaluation method we use is the common total residual probabilistic scoring method (log likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the Alaska seismic hazard model logic trees. To test logic branches in previous seismic hazard models, we evaluate GMM performance as a function of depth and we demonstrate that some GMMs show improved performance for earthquakes with focal depths greater than 50 km. Ten of the initial 15 candidate GMMs fit the observed ground motions and meet established criteria for inclusion in the next update of the Alaska seismic hazard model.

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USGS2019

Preface to the focus section on the 30 November 2018 Mw 7.1 Anchorage, Alaska earthquake

On 30 November 2018, a magnitude ( ⁠ M w "> M w ⁠ ) 7.1 intraslab earthquake occurred under the most densely populated area of Alaska, ∼ 12    km "> ∼ 12 km north of downtown Anchorage. It generated the strongest ground shaking in the greater Anchorage area since the 1964 M w "> M w 9.2 Great Alaska earthquake. Although the 30 November 2018 earthquake did not cause deaths or serious injuries, its economical and psychological impacts on the local population were profound. It caused widespread power outages, structural and nonstructural damage to public and private buildings, and damage to roadways and railways, and resulted in closures of several schools and multiple businesses.

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USGS2019

Ground failure from the Anchorage, Alaska, earthquake of 30 November 2018

Investigation of ground failure triggered by the 2018 M w "> M w Mw 7.1 Anchorage earthquake showed that landslides, liquefaction, and ground cracking all occurred and caused significant damage. Shallow rock falls and rock slides were the most abundant types of landslides, but they occurred in smaller numbers than global models that are based on earthquake magnitude predict; this might result from the 2018 earthquake being an intraslab event. Liquefaction was common in alluvial and intertidal areas; ground deformation probably related to liquefaction damaged numerous houses and port facilities in Anchorage. Ground cracking was pervasive near the edges of slopes in hilly areas and caused perhaps the most significant property damage of all types of ground failure. A complex of slump–earth flows was triggered along coastal bluffs in southern Anchorage where slides also occurred in 1964; the 2018 slides involved both mobilization of new landside material and reactivation of parts of the 1964 landslide deposits. Large translational slides that formed during the 1964 Alaska earthquake showed evidence of deformation along pre‐existing failure surfaces but did not reactivate with new net downslope displacement. Modeling suggests that ground motion in 2018 was of insufficient duration and too high frequency to trigger reactivation of the deep landslides.

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USGS2019

Highlights of a cursory study of behavior of three instrumented buildings during the Mw7.1 Anchorage, Alaska, earthquake of November 30, 2018

This is a cursory study of the recorded responses of three buildings instrumented by the U.S. Geological Survey (USGS) in Anchorage, Alaska, during the M w "> M w Mw 7.1 earthquake of 30 November 2018. The earthquake caused the strongest shaking in Anchorage since the well‐known 1964 M w "> M w Mw 9.2 Great Alaska earthquake. Since the 1964 event, several structures (buildings and bridges) in Anchorage have been instrumented by the USGS, and their responses have been recorded during multiple events. For each of the three buildings (the 14‐story Frontier Building, the 20‐story Atwood Building, and the 22‐story Hilton Hotel) studied herein, essential dynamic characteristics and significant behavioral aspects such as beating and torsional motions are identified. Recorded peak accelerations and displacements are provided, and average drift ratios are computed using the peak displacements at the roof levels with respect to the ground level. These average drift ratios imply that the motions are at levels expected not to cause damage to the buildings. Visualization videos of both the 14‐story Frontier and the 20‐story Atwood Buildings have been developed to display overall shaking of the buildings during the earthquake.

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USGS2019

The 30 November 2018 Mw7.1 Anchorage Earthquake

The M w "> M w 7.1 47 km deep earthquake that occurred on 30 November 2018 had deep societal impacts across southcentral Alaska and exhibited phenomena of broad scientific interest. We document observations that point to future directions of research and hazard mitigation. The rupture mechanism, aftershocks, and deformation of the mainshock are consistent with extension inside the Pacific plate near the down‐dip limit of flat‐slab subduction. Peak ground motions > 25 % g "> g >25%g were observed across more than 8000    km 2 "> 8000 km 2 ⁠ , though the most violent near‐fault shaking was avoided because the hypocenter was nearly 50 km below the surface. The ground motions show substantial variation, highlighting the influence of regional geology and near‐surface soil conditions. Aftershock activity was vigorous with roughly 300 felt events in the first six months, including two dozen aftershocks exceeding M 4.5. Broad subsidence of up to 5 cm across the region is consistent with the rupture mechanism. The passage of seismic waves and possibly the coseismic subsidence mobilized ground waters, resulting in temporary increases in stream flow. Although there were many failures of natural slopes and soils, the shaking was insufficient to reactivate many of the failures observed during the 1964 M 9.2 earthquake. This is explained by the much shorter duration of shaking as well as the lower amplitude long‐period motions in 2018. The majority of observed soil failures were in anthropogenically placed fill soils. Structural damage is attributed to both the failure of these emplaced soils as well as to the ground motion, which shows some spatial correlation to damage. However, the paucity of instrumental ground‐motion recordings outside of downtown Anchorage makes these comparisons challenging. The earthquake demonstrated the challenge of issuing tsunami warnings in complex coastal geographies and highlights the need for a targeted tsunami hazard evaluation of the region. The event also demonstrates the challenge of estimating the probabilistic hazard posed by intraslab earthquakes.

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USGS2019

Repeated detection of carbapenemase-producing Escherichia coli in gulls inhabiting Alaska, USA

We report the first detection of carbapenemase-producing Escherichia coli in Alaska and in wildlife in the United States. Wild bird (gull) feces sampled at three locations in Southcentral Alaska yielded isolates that harbored plasmid-encoded bla KPC-2 or chromosomally-encoded bla OXA-48 , and genes associated with antimicrobial resistance to up to eight antibiotic classes.

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USGS2018

On the potential duration of the aftershock sequence of the 2018 Anchorage earthquake

Currently, an aftershock sequence is ongoing in Alaska after the magnitude 7.0 Anchorage earthquake of November 30, 2018. Using two scenarios, determined with observations as of December 14, 2018, this report estimates that it will take between 2.5 years and 3 decades before the rate of aftershocks decays to the rate of earthquakes that were occurring in this area before the magnitude 7.0 mainshock. All of the time estimates have significant uncertainty owing to different scenarios of how the sequence may decrease over time and could also change if a large aftershock occurs. The report also estimates the amount of time after the mainshock until the annual probability of magnitude 5 or greater and 6 or greater aftershocks—which could cause additional damage—decreases to 50, 25, 10, and 5 percent. For instance, the probability of one or more magnitude 6 or greater aftershocks in the following year decreases to 10 percent between 7 and 250 days after the mainshock. The same probability for magnitude 5 or greater earthquakes is reached between 500 and 7,000 days after the mainshock.

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USGS2017

System identification based on deconvolution and cross correlation: An application to a 20‐story instrumented building in Anchorage, Alaska

Deconvolution and cross‐correlation techniques are used for system identification of a 20‐story steel, moment‐resisting frame building in downtown Anchorage, Alaska. This regular‐plan midrise structure is instrumented with a 32‐channel accelerometer array at 10 levels. The impulse response functions (IRFs) and correlation functions (CFs) are computed based on waveforms recorded from ambient vibrations and five local and regional earthquakes. The earthquakes occurred from 2005 to 2014 with moment magnitudes between 4.7 and 6.2 over a range of azimuths at epicenter distances of 13.3–183 km. The building’s fundamental frequencies and mode shapes are determined using a complex mode indicator function based on singular value decomposition of multiple reference frequency‐response functions. The traveling waves, identified in IRFs with a virtual source at the roof, and CFs are used to estimate the intrinsic attenuation associated with the fundamental modes and shear‐wave velocity in the building. Although the cross correlation of the waveforms at various levels with the corresponding waveform at the first floor provides more complicated wave propagation than that from the deconvolution with virtual source at the roof, the shear‐wave velocities identified by both techniques are consistent—the largest difference in average values is within 8%. The median shear‐wave velocity from the IRFs of five earthquakes is 191 m/s for the east–west (E‐W), 205 m/s for the north–south (N‐S), and 176 m/s for the torsional responses. The building’s average intrinsic‐damping ratio is estimated to be 3.7% and 3.4% in the 0.2–1 Hz frequency band for the E‐W and N‐S directions, respectively. These results are intended to serve as reference for the undamaged condition of the building, which may be used for tracking changes in structural integrity during and after future earthquakes.

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USGS2015

A genetic discontinuity in moose ( Alces alces ) in Alaska corresponds with fenced transportation infrastructure

The strength and arrangement of movement barriers can impact the connectivity among habitat patches. Anthropogenic barriers (e.g. roads) are a source of habitat fragmentation that can disrupt these resource networks and can have an influence on the spatial genetic structure of populations. Using microsatellite data, we evaluated whether observed genetic structure of moose ( Alces alces ) populations were associated with human activities (e.g. roads) in the urban habitat of Anchorage and rural habitat on the Kenai Peninsula, Alaska. We found evidence of a recent genetic subdivision among moose in Anchorage that corresponds to a major highway and associated infrastructure. This subdivision is most likely due to restrictions in gene flow due to alterations to the highway (e.g. moose-resistant fencing with one-way gates) and a significant increase in traffic volume over the past 30 years; genetic subdivision was not detected on the Kenai Peninsula in an area not bisected by a major highway. This study illustrates that anthropogenic barriers can substructure wildlife populations within a few generations and highlights the value of genetic assessments to determine the effects on connectivity among habitat patches in conjunction with behavioral and ecological data. .

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USGS2014

Effects of invasive European bird cherry (Prunus padus) on leaf litter processing by aquatic invertebrate shredder communities in urban Alaskan streams

European bird cherry (Prunus padus) (EBC) is an invasive ornamental tree that is spreading rapidly in riparian forests of urban Alaska. To determine how the spread of EBC affects leaf litter processing by aquatic invertebrate shredders, we conducted complementary leaf pack experiments in two streams located in Anchorage, Alaska. The first experiment contrasted invasive EBC with three native tree species—thin-leaf alder (Alnus tenuifolia), paper birch (Betula neoalaskana), and black cottonwood (Populus trichocarpa)—in one reach of Chester Creek; finding that EBC leaf litter broke down significantly faster than birch and cottonwood, but at a similar rate to alder. The second experiment contrasted EBC with alder in four reaches of Campbell and Chester creeks; finding that while EBC leaf litter broke down significantly faster than alder in Chester Creek, EBC broke down at a similar rate to alder in Campbell Creek. Although EBC sometimes supported fewer shredders by both count and mass, shredder communities did not differ significantly between EBC and native plants. Collectively, these data suggest that invasive EBC is not currently exhibiting strong negative impacts on leaf litter processing in these streams, but could if it continues to spread and further displaces native species over time.

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USGS2014

Use of DNA from bite marks can determine species and individual animals that attack humans

During the summer of 2008, 6 documented attacks and close encounters with brown bears ( Ursus arctos ) occurred in the greater Anchorage, Alaska (USA) area. We discuss findings from 2 incidents in which people were mauled within 2 km of each other over a 6-week period and in which it was assumed that a single animal was responsible. To ensure public safety, authorities killed a brown bear implicated in the attacks by circumstantial evidence, though it was not known a priori that the animal was responsible. We extracted DNA from hairs and bite sites on the clothing of both victims and determined species and individual identity of the animal(s) involved in both incidents. Genetic data indicated the brown bear killed by authorities was responsible for one of the maulings, but not both. This research demonstrates that DNA-based techniques, with appropriate sampling, can provide unambiguous identification of animals involved in attacks, as well as provide reasonable justification for excluding others. Because DNA-based techniques can unequivocally identify individual bears carrying out attacks, they should be considered a standard method employed in wildlife attack investigations.

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USGS2014

Maps showing seismic landslide hazards in Anchorage, Alaska

The devastating landslides that accompanied the great 1964 Alaska earthquake showed that seismically triggered landslides are one of the greatest geologic hazards in Anchorage. Maps quantifying seismic landslide hazards are therefore important for planning, zoning, and emergency-response preparation. The accompanying maps portray seismic landslide hazards for the following conditions: (1) deep, translational landslides, which occur only during great subduction-zone earthquakes that have return periods of =300-900 yr; (2) shallow landslides for a peak ground acceleration (PGA) of 0.69 g, which has a return period of 2,475 yr, or a 2 percent probability of exceedance in 50 yr; and (3) shallow landslides for a PGA of 0.43 g, which has a return period of 475 yr, or a 10 percent probability of exceedance in 50 yr. Deep, translational landslide hazards were delineated based on previous studies of such landslides, with some modifications based on field observations of locations of deep landslides. Shallow-landslide hazards were delineated using a Newmark-type displacement analysis for the two probabilistic ground motions modeled.

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USGS2014

1964 Great Alaska Earthquake: a photographic tour of Anchorage, Alaska

On March 27, 1964, at 5:36 p.m., a magnitude 9.2 earthquake, the largest recorded earthquake in U.S. history, struck southcentral Alaska (fig. 1). The Great Alaska Earthquake (also known as the Good Friday Earthquake) occurred at a pivotal time in the history of earth science, and helped lead to the acceptance of plate tectonic theory (Cox, 1973; Brocher and others, 2014). All large subduction zone earthquakes are understood through insights learned from the 1964 event, and observations and interpretations of the earthquake have influenced the design of infrastructure and seismic monitoring systems now in place. The earthquake caused extensive damage across the State, and triggered local tsunamis that devastated the Alaskan towns of Whittier, Valdez, and Seward. In Anchorage, the main cause of damage was ground shaking, which lasted approximately 4.5 minutes. Many buildings could not withstand this motion and were damaged or collapsed even though their foundations remained intact. More significantly, ground shaking triggered a number of landslides along coastal and drainage valley bluffs underlain by the Bootlegger Cove Formation, a composite of facies containing variably mixed gravel, sand, silt, and clay which were deposited over much of upper Cook Inlet during the Late Pleistocene (Ulery and others, 1983). Cyclic (or strain) softening of the more sensitive clay facies caused overlying blocks of soil to slide sideways along surfaces dipping by only a few degrees. This guide is the document version of an interactive web map that was created as part of the commemoration events for the 50th anniversary of the 1964 Great Alaska Earthquake. It is accessible at the U.S. Geological Survey (USGS) Alaska Science Center website: http://alaska.usgs.gov/announcements/news/1964Earthquake/ . The website features a map display with suggested tour stops in Anchorage, historical photographs taken shortly after the earthquake, repeat photography of selected sites, scanned documents, and small-scale maps, as well as links to slideshows of additional photographs and Google Street View™ scenes. Buildings in Anchorage that were severely damaged, sites of major landslides, and locations of post-earthquake engineering responses are highlighted. The web map can be used online as a virtual tour or in a physical self-guided tour using a web-enabled Global Positioning System (GPS) device. This publication serves the purpose of committing most of the content of the web map to a single distributable document. As such, some of the content differs from the online version.

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