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At least 397 records · Page 22Linked to original sources
USGS2006

Fast simulated annealing inversion of surface waves on pavement using phase-velocity spectra

The conventional inversion of surface waves depends on modal identification of measured dispersion curves, which can be ambiguous. It is possible to avoid mode-number identification and extraction by inverting the complete phase-velocity spectrum obtained from a multichannel record. We use the fast simulated annealing (FSA) global search algorithm to minimize the difference between the measured phase-velocity spectrum and that calculated from a theoretical layer model, including the field setup geometry. Results show that this algorithm can help one avoid getting trapped in local minima while searching for the best-matching layer model. The entire procedure is demonstrated on synthetic and field data for asphalt pavement. The viscoelastic properties of the top asphalt layer are taken into account, and the inverted asphalt stiffness as a function of frequency compares well with laboratory tests on core samples. The thickness and shear-wave velocity of the deeper embedded layers are resolved within 10% deviation from those values measured separately during pavement construction. The proposed method may be equally applicable to normal soil site investigation and in the field of ultrasonic testing of materials. ?? 2006 Society of Exploration Geophysicists.

Geophysics↗
USGS2013

Application and evaluation of electromagnetic methods for imaging saltwater intrusion in coastal aquifers: Seaside Groundwater Basin, California

Developing effective resource management strategies to limit or prevent saltwater intrusion as a result of increasing demands on coastal groundwater resources requires reliable information about the geologic structure and hydrologic state of an aquifer system. A common strategy for acquiring such information is to drill sentinel wells near the coast to monitor changes in water salinity with time. However, installation and operation of sentinel wells is costly and provides limited spatial coverage. We studied the use of noninvasive electromagnetic (EM) geophysical methods as an alternative to installation of monitoring wells for characterizing coastal aquifers. We tested the feasibility of using EM methods at a field site in northern California to identify the potential for and/or presence of hydraulic communication between an unconfined saline aquifer and a confined freshwater aquifer. One-dimensional soundings were acquired using the time-domain electromagnetic (TDEM) and audiomagnetotelluric (AMT) methods. We compared inverted resistivity models of TDEM and AMT data obtained from several inversion algorithms. We found that multiple interpretations of inverted models can be supported by the same data set, but that there were consistencies between all data sets and inversion algorithms. Results from all collected data sets suggested that EM methods are capable of reliably identifying a saltwater-saturated zone in the unconfined aquifer. Geophysical data indicated that the impermeable clay between aquifers may be more continuous than is supported by current models.

California↗
USGS2019

Bringing Bayesian models to life

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models.

Book↗
USGS2025

Leveraging an observed-data likelihood improves the use of machine learning labels in a Bayesian hierarchical model for bioacoustic data

Classification of massive datasets by machine learning (ML) algorithms is promising for many scientific domains, especially wildlife monitoring programs that rely on passive acoustic surveys for detecting species. However, treating ML-predicted class labels (e.g., species identity) as truth biases inferences of focal parameters within common modeling frameworks. One solution is to model the misclassification process explicitly using human-validated true-class labels for a subset of observations. Validation by experts can present a substantial bottleneck in otherwise efficient workflows that use ML predictions. Bioacoustics practitioners seek guidance on both the quantity and process for selecting ML-labeled data to validate by an expert. We derive an alternative model formulation that jointly models human-validated and ML-predicted class labels with an observed-data likelihood (ODL) and use empirically informed simulations motivated by a real-data application to explore different probability designs for selecting class labels for validation. Simulation results suggest that with smaller validation sets the ODL formulation increases computational speed and reduces estimation error compared to a default MCMC data augmentation routine. Our methodology is transferable to applications that treat predictions from classification algorithms as the response variable of interest.

Annals of Applied Statistics↗
USGS2008

Simplified Surface Energy Balance (SSEB) approach for estimating actual ET: An evaluation with lysimeter data

Evapotranspiration (ET) is an essential component of the water balance and a major consumptive use of irrigation water and precipitation on cropland. Numerous energy balance (EB) algorithms have been developed to make use of remote sensing data to estimate ET regionally. However, a review of different ET mapping algorithms shows that most EB models are complex to use and may not be suitable for operational ET remote sensing. Efforts are being made to simplify procedures to estimate regional ET mainly through the scaling of reference ET. The Simplified Surface Energy Balance (SSEB) is one such method. In this study, the SSEB approach was applied to six Landsat TM images covering a major portion of the Southern High Plains (parts of the Texas Panhandle and northeastern New Mexico) that were acquired during the 2007 cropping season. Performance of the SSEB was evaluated by comparing estimated ET with measured daily ET from four large monolithic lysimeters, with each lysimeter located in the center of a 210 by 225 m field at the USDA_ARS Conservation and Production Research Laboratory, Bushland, Tex. [350 11' N, 1020 06' W; 1,170 m elevation MSL]. Results indicated that the SSEB can provide ET estimates with reasonable accuracy for the Bushland location. However, more evaluation is needed for different agroclimatological conditions in the region.

Conference Paper↗
USGS2018

Performance metrics for the assessment of satellite data products: An ocean color case study

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r 2 ), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities.

Optics Express↗
USGS2017

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle ( Aquila chrysaetos ) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

PLoS ONE↗
USGS2004

The characteristics and interpretability of land surface change and implications for project design

The need for comprehensive, accurate information on land-cover change has never been greater. While remotely sensed imagery affords the opportunity to provide information on land-cover change over large geographic expanses at a relatively low cost, the characteristics of land-surface change bring into question the suitability of many commonly used methodologies. Algorithm-based methodologies to detect change generally cannot provide the same level of accuracy as the analyses done by human interpreters. Results from the Land Cover Trends project, a cooperative venture that includes the U.S. Geological Survey, Environmental Protection Agency, and National Aeronautics and Space Administration, have shown that land-cover conversion is a relatively rare event, occurs locally in small patches, varies geographically and temporally, and is spectrally ambiguous. Based on these characteristics of change and the type of information required, manual interpretation was selected as the primary means of detecting change in the Land Cover Trends project. Mixtures of algorithm-based detection and manual interpretation may often prove to be the most feasible and appropriate design for change-detection applications. Serious examination of the expected characteristics and measurability of change must be considered during the design and implementation phase of any change analysis project.

Photogrammetric Engineering and Remote Sensing↗
USGS2007

Geometric correction and digital elevation extraction using multiple MTI datasets

Digital Elevation Models (DEMs) are traditionally acquired from a stereo pair of aerial photographs sequentially captured by an airborne metric camera. Standard DEM extraction techniques can be naturally extended to satellite imagery, but the particular characteristics of satellite imaging can cause difficulties. The spacecraft ephemeris with respect to the ground site during image collects is the most important factor in the elevation extraction process. When the angle of separation between the stereo images is small, the extraction process typically produces measurements with low accuracy, while a large angle of separation can cause an excessive number of erroneous points in the DEM from occlusion of ground areas. The use of three or more images registered to the same ground area can potentially reduce these problems and improve the accuracy of the extracted DEM. The pointing capability of some sensors, such as the Multispectral Thermal Imager (MTI), allows for multiple collects of the same area from different perspectives. This functionality of MTI makes it a good candidate for the implementation of a DEM extraction algorithm using multiple images for improved accuracy. Evaluation of this capability and development of algorithms to geometrically model the MTI sensor and extract DEMs from multi-look MTI imagery are described in this paper. An RMS elevation error of 6.3-meters is achieved using 11 ground test points, while the MTI band has a 5-meter ground sample distance.

Photogrammetric Engineering and Remote Sensing↗
USGS2006

Fault parameter constraints using relocated earthquakes: A validation of first-motion focal-mechanism data

We estimate the strike and dip of three California fault segments (Calaveras, Sargent, and a portion of the San Andreas near San Jaun Bautistia) based on principle component analysis of accurately located microearthquakes. We compare these fault orientations with two different first-motion focal mechanism catalogs: the Northern California Earthquake Data Center ( NCEDC ) catalog, calculated using the FPFIT algorithm (Reasenberg and Oppenheimer, 1985), and a catalog created using the HASH algorithm that tests mechanism stability relative to seismic velocity model variations and earthquake location (Hardebeck and Shearer, 2002). We assume any disagreement (misfit >30° in strike, dip, or rake) indicates inaccurate focal mechanisms in the catalogs. With this assumption, we can quantify the parameters that identify the most optimally constrained focal mechanisms. For the NCEDC/FPFIT catalogs, we find that the best quantitative discriminator of quality focal mechanisms is the station distribution ratio ( STDR ) parameter, an indicator of how the stations are distributed about the focal sphere. Requiring STDR > 0.65 increases the acceptable mechanisms from 34%–37% to 63%–68%. This suggests stations should be uniformly distributed surrounding, rather than aligning, known fault traces. For the HASH catalogs, the fault plane uncertainty ( FPU ) parameter is the best discriminator, increasing the percent of acceptable mechanisms from 63%–78% to 81%–83% when FPU ≤ 35°. The overall higher percentage of acceptable mechanisms and the usefulness of the formal uncertainty in identifying quality mechanisms validate the HASH approach of testing for mechanism stability.

Bulletin of the Seismological Society of America↗
USGS2016

Seismic envelope-based detection and location of ground-coupled airwaves from volcanoes in Alaska

Volcanic explosions and other infrasonic sources frequently produce acoustic waves that are recorded by seismometers. Here we explore multiple techniques to detect, locate, and characterize ground‐coupled airwaves (GCA) on volcano seismic networks in Alaska. GCA waveforms are typically incoherent between stations, thus we use envelope‐based techniques in our analyses. For distant sources and planar waves, we use f ‐ k beamforming to estimate back azimuth and trace velocity parameters. For spherical waves originating within the network, we use two related time difference of arrival (TDOA) methods to detect and localize the source. We investigate a modified envelope function to enhance the signal‐to‐noise ratio and emphasize both high energies and energy contrasts within a spectrogram. We apply these methods to recent eruptions from Cleveland, Veniaminof, and Pavlof Volcanoes, Alaska. Array processing of GCA from Cleveland Volcano on 4 May 2013 produces robust detection and wave characterization. Our modified envelopes substantially improve the short‐term average/long‐term average ratios, enhancing explosion detection. We detect GCA within both the Veniaminof and Pavlof networks from the 2007 and 2013–2014 activity, indicating repeated volcanic explosions. Event clustering and forward modeling suggests that high‐resolution localization is possible for GCA on typical volcano seismic networks. These results indicate that GCA can be used to help detect, locate, characterize, and monitor volcanic eruptions, particularly in difficult‐to‐monitor regions. We have implemented these GCA detection algorithms into our operational volcano‐monitoring algorithms at the Alaska Volcano Observatory.

Alaska↗
USGS2019

Directivity of M 3.1 earthquake near Anza, California and the effect on peak ground motion

We show the effect of rupture directivity on peak ground‐motion values for a moderate magnitude event at Anza, California, and neighboring stations at the Imperial Valley. The event was located near Borrego Springs on the west side of the Salton Sea and was well recorded at broadband stations near Anza, California, and at stations on the west side of the Imperial Valley. After correcting for regional attenuation, an anomalously large residual in peak motion was observed at station ERR just to the southeast of the epicenter. Using the algorithm from Boatwright (2007) , peak motions from the regional seismic networks in southern California were inverted to determine directivity, which was to the southeast along the trend of the San Jacinto fault toward station ERR. This algorithm uses peak values compiled for the ShakeMap system mostly at regional distances. It does not capture the main features of the source time function (STF) predicted by directivity. Consequently, we determined the second‐degree moments for this earthquake, which confirmed that station ERR has a shorter and higher STF compared to stations to the northwest suggesting rupture propagated to the southeast. The azimuthal distribution of local stations is sparse, but nevertheless the largest amplitudes (such as at station ERR) correlate well with the maximum in the radiation pattern and smaller values with the minima, which is the radiation pattern for SH plus the effect of directivity. Using the data from the analysis of the second‐degree moments, the characteristic length of the fault is 0.58 km, assuming an idealized unilateral extended rupture with a rupture time of 0.09 s. This yields an apparent rupture velocity of 6.4    km / s "> 6.4 km / s for an idealized model, which is super shear. This value is model dependent and would change if, for example, the rupture was bilateral. Although this value is even greater than the P ‐wave velocity, it supports the idea that the rupture velocity is super shear and would enhance the correlation between the peak motions and the radiation pattern.

California↗
USGS2024

Preliminary implications of viscoelastic ray theory for anelastic seismic tomography models

The recent developments in general viscoelastic ray theory provide a rigorous mathematical framework for anelastic seismic tomography. They provide closed‐form solutions of forward ray‐tracing and simple inverse problems for anelastic horizontal and spherical layered media with material gradients. They provide ray‐tracing computation algorithms valid for all angles of incidence that account for changes in wave speed, attenuation, and trajectory of anelastic P and S body waves induced by anelastic boundaries. They account for theoretical predictions that seismic waves refract as inhomogeneous waves across anelastic boundaries for all angles of incidence, which in turn accounts for energy carried by plane waves along seismic boundaries at head wave critical angles and wide‐angle refracted (WAR) ray paths that are not predicted by elastic models. Exact viscoelastic ray‐tracing numerical results for various models provide examples that illustrate the effects of anelastic boundaries on the travel times and amplitudes of seismic waves. They show the effects are strongly dependent on angle of incidence. For near‐critical and wide angles of incidence the anelastic effects on travel times and amplitudes can be large and are not explained by elastic ray theory, but the effects on travel times can be relatively small and difficult to distinguish from those for elastic media for pre‐near‐critical angles of incidence. The results for some models indicate that reflected anelastic WAR waves may be observable at the surface and possibly account for some prominent seismic arrivals not explained by elasticity. These preliminary results suggest that the application of exact viscoelastic ray‐tracing computation algorithms to exploration and teleseismic data sets can reveal new insights regarding the properties and distribution of anelastic materials in the Earth.

Bulletin of the Seismological Society of America↗
USGS2026

ShakeAlert Earthquake Early Warning System performance during the Mw 7.0 offshore Cape Mendocino earthquake

The 5 December 2024 M w 7.0 Offshore Cape Mendocino earthquake was a challenging test of the U.S. West Coast ShakeAlert earthquake early warning system due to its offshore epicenter and limited near‐source station coverage. We analyzed real‐time performance of all components of the ShakeAlert system, including the seismic algorithms (earthquake point‐source integrated code [EPIC] and Finite‐fault rupture Detector [FinDer]), the geodetic algorithm (Geodetic First Approximation of Size and Time–peak ground displacement [GFAST‐PGD]), and network telemetry during the event. EPIC created the first solution for this earthquake 15 s after origin time with an initial magnitude estimate of M 5.6 and location error of 10 km from the Advanced National Seismic System epicenter. An early spurious trigger from station CE.89101 fortuitously maintained location accuracy and, correspondingly, magnitude accuracy. FinDer contributed its first solution at 18 s with a location estimate closer to the seismic network and produced two distinct rupture geometries, leading to minor fluctuations in estimated intensity contours. GFAST‐PGD did not meet alerting thresholds but otherwise performed as expected. Network latencies were <2 s for most stations, supporting the rapid detection of this earthquake by the system. Roughly five million alerts were delivered to cell phone devices in California and Oregon during this event. This was also the first instance of a school district‐wide ShakeAlert‐powered system being activated. Comparisons to recorded seismograms demonstrate that the maximum warning times before potentially damaging shaking (intensity 6+) were in the range of 5–55 s. Although the ShakeAlert system provided accurate solutions and useful alert delivery, this earthquake raised awareness of potential issues within the system, including the need for improved offshore location estimates, a combination of solutions from ShakeAlert servers, and handling of spurious triggers.

California↗
USGS2018

A repeating event sequence alarm for monitoring volcanoes

A major challenge in volcanology is forecasting eruptions. Repeating earthquake sequences may precede volcanic eruptions or lava dome growth and collapse, providing an opportunity for short-term eruption forecasting. I develop an automated repeating earthquake sequence detector and near real-time alarm to send alerts when an in-progress sequence is identified. The algorithm is based on a standard event detector (e.g., STA/LTA) and subsequent correlation-matching procedure that identifies repeating event sequences. A notification algorithm determines when a sequence is in progress and sends alerts. I use eruptions of three Alaskan volcanoes as case studies to test the alarm, implementing it both in retrospect and in real-time during the 2016-2017 Bogoslof eruption. These case studies show that the alarm can successfully be used to detect and alert on sequences of repeating events in a timely manner.

Seismological Research Letters↗
USGS2024

SKHASH: A python package for computing earthquake focal mechanisms

We introduce a Python package for computing focal mechanism solutions. This algorithm, which we refer to as SKHASH, is largely based on the HASH algorithm originally written in Fortran over 20 yr ago. HASH innovated the use of suites of solutions, spanning the expected errors in polarities and takeoff angles, to estimate focal mechanism uncertainty. SKHASH benefits from new features with flexible input formats and allows users to take advantage of recent advances in constraining focal mechanisms for small magnitude or poorly recorded earthquakes. The 3D locations of earthquakes and the velocity models used are varied when finding acceptable solutions. As a result, source–receiver azimuths are reflective of errors from the earthquake locations and velocity models, in addition to the takeoff angles. Users can consider weighted P ‐wave first‐motion polarities derived from traditional or machine‐learning picks, cross‐correlation consensus, and/or imputation techniques using SKHASH. Focal mechanism solutions can also be further constrained using traditional, machine learning, and/or cross‐correlation consensus S / P amplitude ratios. With improved reporting of individual and collective P polarity and S / P amplitude misfits, users can better evaluate the success of the solutions and the quality of the measurements. The reporting also makes it easier to identify potential issues with metadata, including incorrectly reported station polarity reversals. In addition, by leveraging vectorized operations, taking advantage of an efficient backend Python C Application Programming Interface, and the use of a parallel environment, the Python SKHASH routine may compute mechanisms quicker than the HASH routine.

Seismological Research Letters↗
USGS2025

The GorDAS Distributed Acoustic Sensing experiment above the Cascadia locked zone and subducted Gorda Slab

The southernmost portion of the Cascadia Subduction zone in Northern California produces high rates of moderate and large earthquakes owing to subduction of the Gorda slab and deformation associated with the Mendocino Triple Junction. Distributed Acoustic Sensing (DAS) is rapidly advancing as a method for detecting earthquakes and imaging crustal structure. We have begun a long-term DAS monitoring experiment on buried telecom fiber in Arcata, California, with the goal of increasing the available recordings of moderate to large earthquakes as well as imaging seismogenic structures. We have recorded over a year's worth of data, including most aftershocks of the 2022 M w 6.4 Ferndale earthquake, though not the mainshock itself. The dataset includes numerous magnitude 3.5 and larger earthquakes including the 2023/01/01 M w 5.4 Rio Dell earthquake. Here we present initial results comparing an earthquake detection algorithm, run in real-time on the processing unit of the interrogator system, with both the ShakeAlert earthquake early warning system as well as a post-processed earthquake catalog developed with deep-learning phase-picker algorithms. The rapid onboard processing of the detector demonstrates the potential utility of DAS-based edge computing for earthquake early warning. We also verify the quality of the strain waveforms both in terms of peak amplitudes and waveform similarity using about five months of nodal seismometer data. These instruments were deployed roughly every 300 m along the ~15km long cable and validate large variations in peak strain over short distances that are seen in the DAS data. All data from time windows surrounding both the local and teleseismic earthquakes are publicly available, which will improve our understanding of both the performance of DAS systems in moderate earthquakes and earthquake hazards associated with the Gorda subduction zone.

California↗
USGS2013

Spatial occupancy models for large data sets

Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km 2 ) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.

Ecology↗