SEARCH · Search USGS

Results for “Research Information Letter”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11Linked to original sources

USGS2022

Simulating burn severity maps at 30 meters in two forested regions in California

Climate change is altering wildfire behavior and vegetation regimes in California’s forested ecosystems. Present day fires are seeing an increase in high burn severity area and high severity patch size. The ability to predict future burn severity patterns would support better policy and land management decisions. Here we demonstrate a methodology to first, statistically estimate individual burn severity classes at 30 meters and second, cluster and smooth high severity patches onto a landscape. Our goal here was not to exactly replicate observed burn severity maps, but rather to utilize observed maps as one realization of a random process dependent on climate, topography, fire weather, and fuels, to inform creation of additional realizations through our simulation technique. We developed two sets of empirical models with two different vegetation datasets to test if coarse vegetation could accurately model for burn severity. While visual acuity can be used to assess the performance of our simulation process, we also employ the Ripley’s K function to compare spatial point processes at different scales to test if the simulation is capturing an appropriate amount of clustering. We utilize FRAGSTATS to obtain high severity patch metrics to test the contiguity of our high severity simulation. Ripley’s K function helped identify the number of clustering iterations and FRAGSTATS showed how different focal window sizes affected our ability to cluster high severity patches. High severity patch simulations was comparable between the coarse and fine resolution vegetation datasets. Improving our ability to simulate burn severity will help advance our understanding of the potential influence of land and fuels management on ecosystem-level response variables that are important for decision-makers. Simulated burn severity maps can support managing habitat and estimating risks of habitat loss, protecting infrastructure and homes, improving future wildfire emissions projections, and better mapping and planning for fuels treatment scenarios.

California↗

USGS2018

Seismology with dark data: Image-based processing of analog records using machine learning for the rangely earthquake control experiment

Before the digital era, seismograms were recorded in analog form and read manually by analysts. The digital era represents only about 25% of the total time span of instrumental seismology. Analog data provide important constraints on earthquake processes over the long term, and in some cases are the only data available. The media on which analog data are recorded degrades with time and there is an urgent need for cost‐effective approaches to preserve the information they contain. In this study, we work directly with images by constructing a set of image‐based methods for earthquake processing, rather than pursue the usual approach of converting analog data to vector time series. We demonstrate this approach on one month of continuous Develocorder films from the Rangely earthquake control experiment run by the U.S. Geological Survey (USGS). We scan the films into images and compress these into low‐dimensional feature vectors as input to a classifier that separates earthquakes from noise in a defined feature space. We feed the detected event images into a short‐term average/long‐term average (STA/LTA) picker, a grid‐search associator, and a 2D image correlator to measure both absolute arrival times and relative arrival‐time differences between events. We use these measurements to locate the earthquakes using hypoDD. In the month that we studied, we identified 40 events clustered near the injection wells. In the original study, Raleigh et al. (1976) identified only 32 events during the same period. Scanning without vectorizing analog seismograms represents an attractive approach to archiving these perishable data. We demonstrated that it is possible to carry out precision seismology directly on such images. Our approach has the potential for wide application to analog seismograms.

Seismological Research Letters↗

USGS2026

Landslide volume estimation from seismic waveform features using a global catalog of seismogenic events

Seismic waves generated by large, rapid landslides encode information about the source and can be analyzed rapidly following an event. Even remote landslides can pose hazards to downstream communities, so rapid detection and characterization using existing seismic monitoring networks could be beneficial. In this study, we expand on past regionally limited work by presenting a globally applicable method for estimating landslide volume from seismic features that could be integrated into future landslide seismic monitoring frameworks. We train the model using multivariable linear regression and five seismic features derived from recordings of 129 landslide events of a range of styles and locations with independently estimated volumes. We present two preferred models, one that combines long‐period (LP) and high‐frequency (HF) features and one for use on smaller landslides without observed long‐period signals. We find that our best‐performing model, applicable to landslides larger than 100,000 m 3 with signals containing observable long‐period energy, requires only two features: LP (20–100 s) absolute maximum amplitude and HF (1–5 Hz) rise time (time between the signal start and the maximum envelope amplitude) and has an R 2 score of 0.79. This model predicted volumes within one order of magnitude for 55 out of 58 events. We find that this combination mitigates the trade‐off between mass and acceleration and other variations of landslide style that limit methods based on amplitude alone.

Seismological Research Letters↗

USGS2008

The USGS Earthquake Notification Service (ENS): Customizable notifications of earthquakes around the globe

At the beginning of 2006, the U.S. Geological Survey (USGS) Earthquake Hazards Program (EHP) introduced a new automated Earthquake Notification Service (ENS) to take the place of the National Earthquake Information Center (NEIC) "Bigquake" system and the various other individual EHP e-mail list-servers for separate regions in the United States. These included northern California, southern California, and the central and eastern United States. ENS is a "one-stop shopping" system that allows Internet users to subscribe to flexible and customizable notifications for earthquakes anywhere in the world. The customization capability allows users to define the what (magnitude threshold), the when (day and night thresholds), and the where (specific regions) for their notifications. Customization is achieved by employing a per-user based request profile, allowing the notifications to be tailored for each individual's requirements. Such earthquake-parameter-specific custom delivery was not possible with simple e-mail list-servers. Now that event and user profiles are in a structured query language (SQL) database, additional flexibility is possible. At the time of this writing, ENS had more than 114,000 subscribers, with more than 200,000 separate user profiles. On a typical day, more than 188,000 messages get sent to a variety of widely distributed users for a wide range of earthquake locations and magnitudes. The purpose of this article is to describe how ENS works, highlight the features it offers, and summarize plans for future developments.

Seismological Research Letters↗

USGS2022

Prospective and retrospective evaluation of the U.S. Geological Survey public aftershock forecast for the 2019-2021 Southwest Puerto Rico Earthquake and aftershocks

The M w "> M w 6.4 Southwest Puerto Rico Earthquake of 7 January 2020 was accompanied by a robust fore‐ and aftershock sequence. The U.S. Geological Survey (USGS) has issued regular aftershock forecasts for more than a year since the mainshock, available on a public webpage. Forecasts were accompanied by interpretive and informational material, published in English and Spanish. Informational products included narrative “scenarios” for how the aftershock sequence could play out, infographics, and a report on the potential duration of the aftershock sequence through the next decade. Forecasts are based on the epidemic‐type aftershock sequence (ETAS) model and generated using the USGS AftershockForecaster software—an interactive graphical user interface built on the OpenSHA platform ( Field et al. , 2003 ). The initial forecast is based on past sequences in similar tectonic environments; subsequent forecasts are tuned to the ongoing sequence via Bayesian model updating. Probabilistic aftershock forecasts for the next day, week, month, and year were publicly released and archived at a daily to monthly tempo, allowing for a truly prospective test of the forecast. Here, we evaluate the forecast over the first year of the recorded aftershocks. The ETAS‐based forecast performed well overall, successfully capturing both the chance of having at least one earthquake of a given magnitude in a forecast interval as well as the non‐Poissonian distribution of the total number of aftershocks within an interval. A retrospective analysis shows that the ETAS model is a substantial improvement over the existing Reasenberg and Jones (1989) forecast model. The exercise also reveals some limitations of the current model, in particular, with respect to nonstationarities in the aftershock magnitude distribution and model parameters throughout the evolving sequence.

Puerto Rico↗

USGS2022

An interactive viewer to improve operational aftershock forecasts

The U.S. Geological Survey (USGS) issues forecasts for aftershocks about 20 minutes after most earthquakes above M 5 in the United States and its territories, and updates these forecasts 75 times during the first year. Most of the forecasts are issued automatically, but some forecasts require manual intervention to maintain accuracy. It is important to identify the sequences whose forecasts will benefit from a modified approach so the USGS can provide accurate information to the public. The oaftools R package ( Paris and Michael, 2022 ) includes functions that analyze and plot earthquake sequences and their forecasts to identify which sequences require such intervention. The package includes the Operational Aftershock Forecast (OAF) Viewer, which incorporates the functions into an interactive web environment that can be used to explore aftershock sequences. The OAF Viewer starts with a global map and table of mainshocks. After a mainshock has been selected, the map and a new table show its aftershocks and the OAF Viewer generates five analytical plots: (1) magnitude–time, which is used to look for patterns in the data; (2) cumulative number, to see how the productivity of the sequence compares to a Reasenberg and Jones (1989) aftershock model over time; (3) magnitude–frequency, to compare the ratio of large to small magnitudes and extrapolate to higher magnitudes with sparse data and lower magnitudes with incomplete data; (4) forecast success, to compare the forecasts with observations for a sequence; and (5) parameter–time, which examines the temporal evolution of the forecast model parameters. The user can interact with the functions provided by the oaftools package through the OAF Viewer or by incorporating the functions into their own analysis methods. The OAF Viewer will help seismologists understand complexities in the data, communicate with the public and emergency managers, and improve the OAF system by maintaining operational awareness.

Seismological Research Letters↗

USGS2017

A synoptic view of the Third Uniform California Earthquake Rupture Forecast (UCERF3)

Probabilistic forecasting of earthquake‐producing fault ruptures informs all major decisions aimed at reducing seismic risk and improving earthquake resilience. Earthquake forecasting models rely on two scales of hazard evolution: long‐term (decades to centuries) probabilities of fault rupture, constrained by stress renewal statistics, and short‐term (hours to years) probabilities of distributed seismicity, constrained by earthquake‐clustering statistics. Comprehensive datasets on both hazard scales have been integrated into the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3). UCERF3 is the first model to provide self‐consistent rupture probabilities over forecasting intervals from less than an hour to more than a century, and it is the first capable of evaluating the short‐term hazards that result from multievent sequences of complex faulting. This article gives an overview of UCERF3, illustrates the short‐term probabilities with aftershock scenarios, and draws some valuable scientific conclusions from the modeling results. In particular, seismic, geologic, and geodetic data, when combined in the UCERF3 framework, reject two types of fault‐based models: long‐term forecasts constrained to have local Gutenberg–Richter scaling, and short‐term forecasts that lack stress relaxation by elastic rebound.

Seismological Research Letters↗

USGS2025

SURF: An automated method for building nonplanar 3D fault models from earthquake hypocenters

Accurately characterizing 3D fault geometry is vital for improving our understanding of earthquake behavior and informing the development of seismic hazard models. Despite their importance, subsurface fault structures tend to be poorly constrained because of limitations in observational data. Improvements to the seismic networks and earthquake detection algorithms have increased the precision and volume of earthquake catalogs, which help illuminate detailed subsurface fault structure and provide the most direct information available about fault geometries at depth. We present a Python package to automate generating 3D fault geometries directly from hypocentral seismicity patterns. This method begins with clustering events based on their spatial density, identifying coherent patterns. Nearby clusters are then merged based on the similarity of their orientations. We fit nonplanar surfaces using support vector regression to balance surface accuracy with minimal deviations from planarity. The fault models are output as quadrilateral meshes at user‐defined resolution. In the process of generating the 3D fault surfaces, we compute the spatial density of seismicity around the surface and the planarity as quantitative metrics of the model outputs. As a proof of concept, we apply this approach to the San Andreas–Calaveras fault junction region and the 2019 Ridgecrest earthquake sequence, both in California, which contain complex subparallel faults well defined at the Earth’s surface and abundant microseismicity. These case studies demonstrate the method’s ability to model complex fault structures, including long continuous fault surfaces, crossing faults, variably dipping segments, and subparallel faults. We test the method on both standard network catalogs and double‐difference relocated catalogs. We find that our seismicity‐based fault model results align with published 3D models that incorporate additional constraints and interpretations ( Plesch et al. , 2020 ; Aagaard and Hirakawa, 2021 ). This workflow provides a low‐user‐input solution for estimating fault geometries at depth from earthquake catalogs.

California↗

USGS2022

An assessment of uncertainties in VS profiles obtained from microtremor observations in the phased 2018 COSMOS blind trials

Site response is a critical consideration when assessing earthquake hazards. Site characterization is key to understanding site effects as influenced by seismic site conditions of the local geology. Thus, a number of geophysical site characterization methods were developed to meet the demand for accurate and cost-effective results. As a consequence, a number of studies have been administered periodically as blind trials to evaluate the state-of-practice on-site characterization. We present results from the Consortium of Organizations for Strong Motion Observation Systems (COSMOS) blind trials, which used data recorded from surface-based microtremor array methods (MAM) at four sites where geomorphic conditions vary from deep alluvial basins to an alpine valley. Thirty-four invited analysts participated. Data were incrementally released to 17 available analysts who participated in all four phases: (1) two-station arrays, (2) sparse triangular arrays, (3) complex nested triangular or circular arrays, and (4) all available geological control site information including drill hole data. Another set of 17 analysts provided results from two sites and two phases only. Although data from one site consisted of recordings from three-component sensors, the other three sites consisted of data recorded only by vertical-component sensors. The sites cover a range of noise source distributions, ranging from one site with a highly directional microtremor wave field to others with omni-directional (azimuthally distributed) wave fields. We review results from different processing techniques (e.g., beam-forming, spatial autocorrelation, cross-correlation, or seismic interferometry) applied by the analysts and compare the effectiveness between the differing wave field distributions. We define the M index as a quality index based on estimates of the time-averaged shear-wave velocity of the upper 10 ( V S10 ), 30 ( V S30 ), 100 ( V S100 ), and 300 ( V S300 ) meters and show its usefulness in quantitative comparisons of V S profiles from multiple analysts. Our findings are expected to aid in building an evidence-based consensus on preferred cost-effective arrays and processing methodology for future studies of seismic site effects.

Seismological Research Letters↗

USGS2023

Maps of active layer thickness in northern Alaska by upscaling P-band polarimetric synthetic aperture radar retrievals

Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5–9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11–12 cm and bias of 2.7–6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.

Environmental Research Letters↗

USGS2019

Investigating lake-area dynamics across a permafrost-thaw spectrum using airborne electromagnetic surveys and remote sensing time-series data in Yukon Flats, Alaska

Lakes in boreal lowlands cycle carbon and supply an important source of freshwater for wildlife and migratory waterfowl. The abundance and distribution of these lakes are supported, in part, by permafrost distribution, which is subject to change. Relationships between permafrost thaw and lake dynamics remain poorly known in most boreal regions. Here, new airborne electromagnetic (AEM) data collected during June 2010 and February 2016 were used to constrain deep permafrost distribution. AEM data were coupled with Landsat-derived lake surface-area data from 1979 through 2011 to inform temporal lake behavior changes in the 35 500- km 2 Yukon Flats ecoregion of Alaska. Together, over 1500 km of AEM data, and roughly 30 years of Landsat data were used to explore processes that drive lake dynamics across a variety of permafrost thaw states not possible in studies conducted with satellite imagery or field measurements alone. Clustered time-series data identified lakes with similar temporal dynamics. Clusters possessed similarities in lake permanence (i.e. ephemeral versus perennial), subsurface permafrost distribution, and proximity to rivers and streams. Of the clustered lakes, ~66% are inferred to have at least intermittent connectivity with other surface-water features, ~19% are inferred to have shallow subsurface connectivity to other surface water features that served as a low-pass filter for hydroclimatic fluctuations, and ~15% appear to be isolated by surrounding permafrost (i.e. no connectivity). Integrated analysis of AEM and Landsat data reveals a progression from relatively synchronous lake dynamics among disconnected lakes in the most spatially continuous, thick permafrost to quite high spatiotemporal heterogeneity in lake behavior among variably-connected lakes in regions with notably less continuous permafrost. Variability can be explained by the preferential development of thawed permeable gravel pathways for lateral water redistribution in this area. The general spatial progression in permafrost thaw state and lake area behavior may be extended to the temporal dimension. However, extensive permafrost thaw, beyond what is currently observed, is expected to promote ubiquitous subsurface connectivity, eventually evolving to a state of increased lake synchronicity.

Alaska↗

USGS2024

On algorithmically determined versus traditional macroseismic intensity assignments

The utility of macroseismic data, defined as the effects of earthquakes on humans and the built environment, has been increasingly recognized following the advent of online systems that now produce unprecedented volumes of macroseismic intensity information. Contributed reports from the U.S. Geological Survey “Did You Feel It?” (DYFI) system ( Wald et al ., 1999 ) are used to generate intensity values with an algorithm based on seminal work by Dengler and Dewey (1998) . The algorithm was developed initially to reproduce intensity values assigned by expert opinion using questionnaire results collected by telephone survey. In this article, I discuss reasons why intensity values from (self‐selected) DYFI responses can differ from values that would be assigned by expert opinion given more complete data from randomly selected participants. For example, with the data used by Dengler and Dewey (1998) , intensities near 4 could be determined from the percentage of people who felt shaking in each town. With less spatially rich data from self‐selected participants, this percentage often cannot be determined reliably. Audible noises are key additional diagnostic criteria for modified Mercalli intensity (MMI) 4, but, although the DYFI system includes a question about noise, following Dengler and Dewey (1998) , the DYFI algorithm does not include a noise indicator. At the upper end of the scale, as defined the DYFI algorithm yields a maximum intensity value of 9.05, nominally corresponding to peak ground acceleration of 75% g . These and other factors can result in DYFI values that are low compared to traditional MMI values assigned using expert opinion, even absent factors that can bias traditional MMI assignments. Modern ground‐motion intensity conversion equations determined using DYFI intensities are expected to be appropriate for DYFI intensities, but the results of this study suggest that biases may be introduced if DYFI and traditional intensities are assumed to be interchangeable.

Seismological Research Letters↗

USGS2016

Thumbnail‐based questionnaires for the rapid and efficient collection of macroseismic data from global earthquakes

The collection of earthquake testimonies (i.e., qualitative descriptions of felt shaking) is essential for macroseismic studies (i.e., studies gathering information on how strongly an earthquake was felt in different places), and when done rapidly and systematically, improves situational awareness and in turn can contribute to efficient emergency response. In this study, we present advances made in the collection of testimonies following earthquakes around the world using a thumbnail‐based questionnaire implemented on the European‐Mediterranean Seismological Centre (EMSC) smartphone app and its website compatible for mobile devices. In both instances, the questionnaire consists of a selection of thumbnails, each representing an intensity level of the European Macroseismic Scale 1998. We find that testimonies are collected faster, and in larger numbers, by way of thumbnail‐based questionnaires than by more traditional online questionnaires. Responses were received from all seismically active regions of our planet, suggesting that thumbnails overcome language barriers. We also observed that the app is not sufficient on its own, because the websites are the main source of testimonies when an earthquake strikes a region for the first time in a while; it is only for subsequent shocks that the app is widely used. Notably though, the speed of the collection of testimonies increases significantly when the app is used. We find that automated EMSC intensities as assigned by user‐specified thumbnails are, on average, well correlated with “Did You Feel It?” (DYFI) responses and with the three independently and manually derived macroseismic datasets, but there is a tendency for EMSC to be biased low with respect to DYFI at moderate and large intensities. We address this by proposing a simple adjustment that will be verified in future earthquakes.

Seismological Research Letters↗

USGS2021

Which earthquake accounts matter?

Earthquake observations contributed by human observers provide an invaluable source of information to investigate both historical and modern earthquakes. Commonly, the observers whose eyewitness accounts are available to scientists are a self‐selected minority of those who experience a given earthquake. As such these may not be representative of the overall population that experienced shaking from the event. Eyewitness accounts can contribute to modern science only if they are recorded in the first place and archived in an accessible repository. In this study, we explore the extent to which geopolitics and socioeconomic disparities can limit the number of earthquake observers whose observations can contribute to science. We first revisit a late nineteenth‐century earthquake in the central United States in 1882 that provides an illustrative example of an event that has been poorly characterized due to a reliance on English‐language archival materials. For modern earthquakes, we analyze data collected for recent earthquakes in California and India via the online “Did You Feel It?” (DYFI) system. In California, online data‐collection systems appear to be effective in gathering eyewitness accounts from a broad range of socioeconomic groups. In India, however, responses to the DYFI system reveal a strong bias toward responses from urban areas as opposed to rural settlements, as well a bias with literacy rate. The dissimilarity of our results from modern earthquakes in the United States and India provides a caution that, in some parts of the world, contributed felt reports can still potentially provide an unrepresentative view of earthquake effects, especially if online data collection systems are not designed to be broadly accessible. This limitation can in turn potentially shape our understanding of an earthquake’s impact and the characterization of seismic hazard.

Arkansas, Oklahoma, Texas↗

USGS2017

2017 One‐year seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes

We produce a one‐year 2017 seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes that updates the 2016 one‐year forecast; this map is intended to provide information to the public and to facilitate the development of induced seismicity forecasting models, methods, and data. The 2017 hazard model applies the same methodology and input logic tree as the 2016 forecast, but with an updated earthquake catalog. We also evaluate the 2016 seismic‐hazard forecast to improve future assessments. The 2016 forecast indicated high seismic hazard (greater than 1% probability of potentially damaging ground shaking in one year) in five focus areas: Oklahoma–Kansas, the Raton basin (Colorado/New Mexico border), north Texas, north Arkansas, and the New Madrid Seismic Zone. During 2016, several damaging induced earthquakes occurred in Oklahoma within the highest hazard region of the 2016 forecast; all of the 21 moment magnitude ( M ) ≥4 and 3 M ≥5 earthquakes occurred within the highest hazard area in the 2016 forecast. Outside the Oklahoma–Kansas focus area, two earthquakes with M ≥4 occurred near Trinidad, Colorado (in the Raton basin focus area), but no earthquakes with M ≥2.7 were observed in the north Texas or north Arkansas focus areas. Several observations of damaging ground‐shaking levels were also recorded in the highest hazard region of Oklahoma. The 2017 forecasted seismic rates are lower in regions of induced activity due to lower rates of earthquakes in 2016 compared with 2015, which may be related to decreased wastewater injection caused by regulatory actions or by a decrease in unconventional oil and gas production. Nevertheless, the 2017 forecasted hazard is still significantly elevated in Oklahoma compared to the hazard calculated from seismicity before 2009.

Seismological Research Letters↗

USGS2008

Comments on potential geologic and seismic hazards affecting proposed liquefied natural gas site in Santa Monica Bay, California

In a letter to the U.S. Geological Survey (USGS) dated March 25, 2008, Representative Jane Harman (California 36th district) requested advice on geologic hazards that should be considered in the review of a proposed liquefied natural gas (LNG) facility off the California coast in Santa Monica Bay. In 2004, the USGS responded to a similar request from Representative Lois Capps, regarding two proposed LNG facilities offshore Ventura County, Calif., with a report summarizing potential geologic and seismic hazards (Ross and others, 2004). The proposed LNG Deepwater Port (DWP) facility includes single point moorings (SPMs) and 35 miles of underwater pipelines. The DWP submersible buoys, manifolds, and risers would be situated on the floor of the southern Santa Monica Basin, in 3,000 feet of water, about 23 miles offshore of the Palos Verdes Peninsula. Twin 24-inch diameter pipelines would extend northeastward from the buoys across the basin floor, up the basin slope and across the continental shelf, skirting north around the Santa Monica submarine canyon. Figure 1 provides locations of the project and geologic features. Acronyms are defined in table 1. This facility is being proposed in a region of known geologic hazards that arise from both the potential for strong earthquakes and geologic processes related to sediment transport and accumulation in the offshore environment. The probability of a damaging earthquake (considered here as magnitude 6.5 or greater) in the next 30 years within about 30 miles (50 km) of the proposed pipeline ranges from 16% at the pipeline's offshore end to 48% where it nears land (Petersen, 2008). Earthquakes of this magnitude are capable of producing strong shaking, surface fault offsets, liquefaction phenomena, landslides, underwater turbidity currents and debris flow avalanches, and tsunamis. As part of the DWP license application for the Woodside Natural Gas proposal in Santa Monica Bay (known as the OceanWay Secure Energy Project), Fugro West, Inc., had already prepared a document discussing geologic hazards in the area, titled 'Exhibit B Topic Report 6 - Geological Resources' (Fugro West, Inc., 2007); hereafter, this will be called the 'Geological Resources document'. The USGS agreed to evaluate the information in the Geological Resources document regarding (1) proximity of active faults to the proposed project, (2) potential magnitude of seismic events from nearby faults, (3) thoroughness of the assessment of earthquake hazards in general, (4) potential hazards from ground rupture and strong shaking, (5) potential hazards from tsunamis, and (6) other geologic hazards including landslides and debris flows. Because two new earthquake probability reports were scheduled to be released in mid-April, 2008, by the USGS and the California Geological Survey (CGS), the USGS suggested a 6-month review period to enable a thorough incorporation of this new information. Twenty-seven scientists from the USGS and the CGS reviewed various sections of the Geological Resources document. This report outlines our major conclusions. The appendix is a longer list of comments by these reviewers, grouped by section of the Geological Resources document. Before discussing our reviews, we first provide a brief overview of geologic hazards in the proposed site area. This report is a snapshot in time and any future work in the area will need to take into account ongoing research efforts. For example, USGS scientists collected seismic reflection data in the spring of 2008 to study the structure and seismic potential of several faults in the area. Their interpretations (Conrad and others, 2008a and 2008b) are too preliminary to be included in this report, but their final results, along with other researchers' studies in the project area, should be considered in any future work on the Deepwater Port project.

California↗

USGS2019

Understanding tidal marsh trajectories: Evaluation of multiple indicators of marsh persistence

Robust assessments of ecosystem stability are critical for informing conservation and management decisions. Tidal marsh ecosystems provide vital services, yet are globally threatened by anthropogenic alterations to physical and biological processes. A variety of monitoring and modeling approaches have been undertaken to determine which tidal marshes are likely to persist into the future. Here, we conduct the most robust comparison of marsh metrics to date, building on two foundational studies that had previously and independently developed metrics for marsh condition. We characterized pairs of marshes with contrasting trajectories (of marsh cover ) across six regions of the United States, using a combination of remote-sensing and field-based metrics. We also quantified decadal trends in marsh conversion to mudflat/open water at these twelve marshes. Our results suggest that metrics quantifying the distribution of vegetation across an elevational gradient represent the best indicators of marsh trajectories. The unvegetated to vegetated ratio and flood-ebb sediment differential also served as valuable indicators. No single metric universally predicted marsh trajectories, and therefore a more robust approach includes a suite of spatially integrated, landscape-scale metrics that are mostly obtainable from remote sensing. Data from surface elevation tables and marker horizons revealed that degrading marshes can have higher rates of vertical accretion and elevation gain than more intact counterparts, likely due to longer inundation times potentially combined with internal recycling of material. A high rate of elevation gain relative to local sea-level rise has been considered critical to marsh persistence, but our results suggest that it also may serve as a signature of degradation in marshes that have already begun to deteriorate. This investigation, with rigorous comparison and integration of metrics initially developed independently, tested at a broad geographic scale, provides a model for collaborative science to develop management tools for improving conservation outcomes.

Environmental Research Letters↗

USGS2021

Seismic monitoring during crises at the NEIC in support of the ANSS

Over the past two decades, the U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) has overcome many operational challenges. These range from minor disruptions, such as power outages, to significant operational changes, including system reconfiguration to handle unique earthquake sequences and the need to handle distributed work during a pandemic. Our ability to overcome crises is built on the development and implementation of a continuity of operations plan, well‐designed infrastructure, adaptive software systems, experienced staff, and extensive collaboration. The NEIC does not operate in a vacuum but benefits from contributions of United States and international seismic networks. Similarly, the overall resilience of earthquake monitoring in the United States and around the globe benefits from the NEIC’s role as the national center for the Advanced National Seismic System (ANSS). Here, we highlight significant adaptations the NEIC has made in the face of crises. We discuss the COVID‐19 pandemic, which represents the most significant operational crisis to impact the NEIC. The NEIC has maintained continuous operations during the ongoing COVID‐19 pandemic by shifting from a fully onsite operations center to a distributed hybrid of onsite and telework staffing. We then discuss cases in which the NEIC has supported regional monitoring in the face of significant crises. In 2018, the NEIC assisted the Hawaiian Volcano Observatory with the Kīlauea volcano eruption by responding to large events, implementing contingency monitoring procedures, and calculating moment magnitudes for the low‐frequency caldera collapses. Impacts of a crisis extend beyond the immediate response and often require a significant postevent assessment and a rebuilding phase. After the 2017 Hurricane Maria, the NEIC, the USGS National Strong‐Motion Program, and the USGS Albuquerque Seismological Laboratory worked with the Puerto Rico Seismic Network and the Puerto Rico Strong‐Motion program to assess, plan, and implement upgrades at sites that experienced storm damage.

Seismological Research Letters↗