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At least 343 records · Page 19Linked to original sources
USGS2011

Spatiotemporal evolution of dike opening and décollement slip at Kīlauea Volcano, Hawai'i

Rapid changes in ground tilt and GPS positions on Kīlauea Volcano, Hawai'i, are interpreted as resulting from a shallow, two‐segment dike intrusion into the east rift zone that began at 1217 UTC (0217 HST) on 17 June 2007 and lasted almost 3 days. As a result of the intrusion, a very small volume of basalt (about 1500 m 3 ) erupted on 19 June. Northward tilt at a coastal tiltmeter, subsidence of south flank GPS sites, southeastward displacements at southwestern flank GPS sites, and a swarm of flank earthquakes suggest that a slow slip event occurred on the décollement beneath Kīlauea's south flank concurrent with the rift intrusion. We use 4 min GPS positions that include estimates of time‐dependent tropospheric gradients and ground tilt data to study the spatial and temporal relationships between the two inferred shallow, steeply dipping dike segments extending from the surface to about 2 km depth and décollement slip at 8 km depth. We invert for the temporal evolution of distributed dike opening and décollement slip in independent inversions at each time step using a nonnegative least squares algorithm. On the basis of these inversions, the intrusion occurred in two stages that correspond spatially and temporally with concentrated rift zone seismicity. The dike opening began on the western of the two segments before jumping to the eastern segment, where the majority of opening accumulated. Dike opening preceded the start of décollement slip at an 84% confidence level; the latter is indicated by the onset of northward tilt of a coastal tiltmeter. Displacements at southwest flank GPS sites began about 18 h later and are interpreted as resulting from slow slip on the southwestern flank. Additional constraints on the evolution of the intrusion and décollement slip come from inversion of an Envisat interferogram that spans the intrusion until 0822 UTC on 18 June 2007, combined with GPS and tilt data. This inversion shows that up to 0822 UTC on 18 June, décollement slip is only required in a limited region offshore of Ka'ena Point. A similar inversion of the complete event, which includes GPS and tilt data up to 21 June and a second Envisat interferogram spanning the complete intrusion until 21 June, shows décollement slip spread westward across the south flank. This may suggest westward migration of the décollement slip as the event progressed.

Hawaii↗
USGS2020

Partly cloudy with a chance of lava flows: Forecasting volcanic eruptions in the 21st century

A primary goal of volcanology is forecasting hazardous eruptive activity. Despite much progress over the last century, however, volcanoes still erupt with no detected precursors, lives and livelihoods are lost to eruptive activity, and forecasting the onsets of eruptions remains fraught with uncertainty. Long‐term forecasts are generally derived from the geological and historical records, from which recurrence intervals and styles of activity can be inferred, while shorter‐term forecasts are derived from patterns in monitoring data. Information from geology and monitoring data can be evaluated and combined using statistical analysis, expert elicitation, and conceptual and or mathematical models. Integrative frameworks, such as event trees, combine this diversity of information to produce probabilistic forecasts that can inform the style and scale of the societal response to a potential future eruption. Several developments show promise to revolutionize the utility and accuracy of these forecasts. These include growth in the quantity and quality of multidisciplinary monitoring data, coupled with increases in computing power; machine learning algorithms, which will allow far better utilization of this growing volume of data; and new physiochemical volcano models and data assimilation algorithms, which take advantage of a wide range of monitoring data and realistic physics to better predict the evolution of a given physical state. Although eruption forecasts may never be as generally reliable as weather forecasts, and great caution must be exercised when attempting to predict highly complex volcanic behavior, these and other innovations—particularly when combined in integrative, fully probabilistic forecasting frameworks—should help volcanologists to better issue warnings of volcanic activity on societally relevant time frames.

Hawaii↗
USGS2022

Human-in-the-Loop segmentation of earth surface imagery

Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time-consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed-pixels, -textures, and -spectral signatures, can be especially error-prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi-automated, method for interactive segmentation of N-dimensional (x, y, N) images into two-dimensional (x, y) label images. It uses human-in-the-loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non-photographic imagery consist of 1- and 3-bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation.

Earth and Space Science↗
USGS2023

Improved calculation of hydraulic conductivity for small-disk tension infiltrometers

Because tension infiltrometers apply water through a disk of finite size, the infiltrated water moves laterally as well as downward. Only the vertical component of this flow is indicative of the hydraulic conductivity K , so the algorithm for computing K must include a way of isolating that component from the total flow. Some commonly used formulas correct for the multidimensional effects by subtracting an estimate of the laterally spreading flow. For disks smaller than about 200 mm in diameter, however, lateral spreading constitutes so much of the total flow that these subtractive formulas lose considerable accuracy, and sometimes overcorrect so severely as to produce a negative number for K . Other methods rely on empiricisms that are not completely consistent with unsaturated-flow theory and that require prior knowledge of certain soil properties. We developed a new formula that uses a multiplicative factor instead of a subtracted term to achieve the needed correction. For testing we conducted numerical experiments with synthetic data produced by solving the Richardson-Richards equation using the code VS2DRTI, for diverse media and a range of disk sizes, including the widely used 45-mm diameter. We compared K values calculated from our formula to the actual K used to generate the simulated data, as well as to results from other published formulas. This comparison shows that our method provides an algorithm based in unsaturated-flow theory that produces more reliable values for small disks without requiring prior knowledge of soil properties.

Water Resources Research↗
USGS2025

Uncertainty quantification of geophysical and hydrologic parameters estimated from borehole nuclear magnetic resonance data

Borehole nuclear magnetic resonance (bNMR) data are typically used to infer in situ hydrologic properties. Partial water content as a function of pore size is estimated by fitting the measured NMR response to a multi-exponential T 2 distribution, and the sum of estimated T 2 amplitudes equals the total volumetric water content. From these estimated parameters, several empirical relationships are commonly used to infer hydraulic conductivity from the NMR-estimated water content and T 2 distribution. Often, parameters are estimated through deterministic inversion methods that produce a single best-fit estimate, but do not reflect uncertainties in model parameters. Here, a Bayesian Markov chain Monte Carlo (McMC) approach for analyzing bNMR data is developed that allows for comprehensive uncertainty quantification of NMR parameters and derived hydrologic properties. The underlying model that describes the T 2 distribution is defined by a set of spline interpolation points. The number of interpolation points is allowed to vary in a trans-dimensional algorithm that naturally favors simple models with fewer interpolation points, allowing the data to inform the necessary level of model complexity. Additionally, data error is estimated as an unknown parameter. Analysis of the ensemble of models output from the McMC algorithm provides useful details on the range of plausible T 2 distributions that can fit a measured bNMR decay curve, as well as uncertainty estimates of total water content. The ensemble of NMR parameters can also be propagated through commonly used relationships to produce uncertainty estimates on derived parameters such as bound/capillary/mobile water content or hydraulic conductivity.

JGR Machine Learning and Computation↗
USGS2026

An entropic explanation for Gutenberg-Richter scaling

We develop a simple explanation for Gutenberg-Richter (G-R) size scaling of earthquakes on a single fault. We discretize the fault and consider all possible contiguous ruptures at that level of discretization. In this static model, we assume that slip scales with rupture length, and that the rupture rates at each point along the fault are consistent with an a priori long-term slip rate. These simple assumptions define an (under-determined) non-negative least-squares inverse problem. Each solution to this inverse problem is a set of earthquake rates that matches the slip-rate constraint. We use a Markov Chain Monte Carlo (MCMC) algorithm to uniformly sample the solution space assuming constant slip rates along the fault. At finer discretizations, deviations from G-R behavior decrease, which is consistent with an entropic pressure towards G-R solutions. When the fault is discretized into 10 or more segments, random solutions found by the MCMC algorithm have G-R size scaling, even though there are trivial solutions that, for example, have earthquakes of only one size. This is because there are simply far more solutions that have G-R scaling; as the problem size increases, the strong degeneracy of GR solutions results in other solutions becoming improbably rare. Also, the entropically favored G-R distribution has a b -value of approximately 1, which agrees with measured b -values in real earthquake catalogs.

JGR Solid Earth↗
USGS2026

Global performance of remote sensing-based and reanalysis-driven models to estimate open water evaporation

Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, we analyze the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. We compare 3 remote sensing-based models, 1 reanalysis-driven model and 1 ensemble approach, using in situ observations from 27 lakes representing a diverse range of geographic and climatic regions. Our results demonstrate that, overall, the ensemble outperformed any individual model in terms of accuracy, with a RMSE and a bias of 1.3 and 0.3 mm day −1 , respectively. These findings highlight the benefits of using an ensemble approach to estimate open water evaporation with satellite-based models at the global scale, leveraging the unique strengths of each model. For the individual models, differences in the representation of heat storage changes and advection effects led to lower values of RMSE and bias, depending on the location and depth of the lakes. This study sets the path for future improvement of open water evaporation algorithms globally, while remote sensing techniques are proven satisfactory to monitoring of water loss in lakes globally, an essential step toward effective large-scale water resources management.

Water Resources Research↗
USGS2026

Constraining sources of mid-Pleistocene to present explosive volcanism in the Gulf of Alaska using machine learning and compositional data analysis

The long-term eruptive record of a region helps better elucidate magmatic processes at depth as well as volcanic hazards at the surface. Typically, reconstructing such records is done using proximal tephrostratigraphy and linking individual tephras to source volcanoes. These records, however, can only be accurately constructed if they are both well preserved and correctly linked to source volcanoes—not a trivial task, especially in areas such as Alaska that have experienced numerous glaciation events since the Pleistocene. This ultimately necessitates another way of assessing the long-term volcanic record such that these histories may be better discerned. Here we present data from 70 marine core tephras from the Gulf of Alaska, which have a virtually uninterrupted depositional record going back through the mid-Pleistocene. We utilize compositional data analysis techniques to quantify 37 eruptions over the span of eight cores, machine learning classification and conformal prediction algorithms to assign the most probable volcanic source(s) to each eruption, and multivariate distance-based metrics when machine learning classification algorithms are inappropriate. We find that the Mount Katmai magmatic system is the most probable volcanic source for analyzed tephras and that large volcanic centers such as Mount Katmai, Fisher Caldera, and Emmons Lake volcanic center have produced nearly invariant incompatible trace element ratio magmas, allowing for them to be confidently identified in long-term tephra records. This highlights the utility of trace elements for tephra studies, especially when paired with compositional data analysis, multivariate statistical tests, and petrologically informed discriminants.

Alaska↗
USGS2008

Convergent radial dispersion: A note on evaluation of the Laplace transform solution

A numerical inversion algorithm for Laplace transforms that is capable of handling rapid changes in the computed function is applied to the Laplace transform solution to the problem of convergent radial dispersion in a homogeneous aquifer. Prior attempts by the author to invert this solution were unsuccessful for highly advective systems where the Peclet number was relatively large. The algorithm used in this note allows for rapid and accurate inversion of the solution for all Peclet numbers of practical interest, and beyond. Dimensionless breakthrough curves are illustrated for tracer input in the form of a step function, a Dirac impulse, or a rectangular input.

Water Resources Research↗
USGS1996

The 1994 Northridge, California, earthquake: Investigation of rupture velocity, risetime, and high-frequency radiation

A hybrid global search algorithm is used to solve the nonlinear problem of calculating slip amplitude, rake, risetime, and rupture time on a finite fault. Thirty-five strong motion velocity records are inverted by this method over the frequency band from 0.1 to 1.0 Hz for the Northridge earthquake. Four regions of larger-amplitude slip are identified: one near the hypocenter at a depth of 17 km, a second west of the hypocenter at about the same depth, a third updip from the hypocenter at a depth of 10 km, and a fourth updip from the hypocenter and to the northwest. The results further show an initial fast rupture with a velocity of 2.8 to 3.0 km/s followed by a slow termination of the rupture with velocities of 2.0 to 2.5 km/s. The initial energetic rupture phase lasts for 3 s, extending out 10 km from the hypocenter. Slip near the hypocenter has a short risetime of 0.5 s, which increases to 1.5 s for the major slip areas removed from the hypocentral region. The energetic rupture phase is also shown to be the primary source of high-frequency radiation (1–15 Hz) by an inversion of acceleration envelopes. The same global search algorithm is used in the envelope inversion to calculate high-frequency radiation intensity on the fault and rupture time. The rupture timing from the low- and high-frequency inversions is similar, indicating that the high frequencies are produced primarily at the mainshock rupture front. Two major sources of high-frequency radiation are identified within the energetic rupture phase, one at the hypocenter and another deep source to the west of the hypocenter. The source at the hypocenter is associated with the initiation of rupture and the breaking of a high-stress-drop asperity and the second is associated with stopping of the rupture in a westerly direction.

Journal of Geophysical Research B: Solid Earth↗
USGS2019

Measurement of cyanobacteria bloom magnitude using satellite remote sensing

Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatio-temporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape-based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Ocean Land Color Imager (OLCI) on Sentinel-3 data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision-making for recreational and drinking water sources.

Florida, Ohio↗
USGS2015

The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.

Scientific Data↗
USGS2002

Dynamic fuzzy modeling of storm water infiltration in urban fractured aquifers

In an urban fractured-rock aquifer in the Mt. Eden area of Auckland, New Zealand, disposal of storm water is via "soakholes" drilled directly into the top of the fractured basalt rock. The dynamic response of the groundwater level due to the storm water infiltration shows characteristics of a strongly time-varying system. A dynamic fuzzy modeling approach, which is based on multiple local models that are weighted using fuzzy membership functions, has been developed to identify and predict groundwater level fluctuations caused by storm water infiltration. The dynamic fuzzy model is initialized by the fuzzy clustering algorithm and optimized by the gradient-descent algorithm in order to effectively derive the multiple local models-each of which is associated with a locally valid model that represents the groundwater level state as a response to different intensities of rainfall events. The results have shown that even if the number of fuzzy local models derived is small, the fuzzy modeling approach developed provides good prediction results despite the highly time-varying nature of this urban fractured-rock aquifer system. Further, it allows interpretable representations of the dynamic behavior of the groundwater system due to storm water infiltration.

Journal of Hydrologic Engineering↗
USGS2026

Benchmark dataset of historical annual peak floods classified by causal mechanisms for select US river basins

Considering the causal mechanisms of floods can improve estimates of flood recurrence intervals given that certain flood types can be associated with higher magnitude and more damaging floods. However, few verified datasets of flood types are available to validate the semiautomated and automated classification algorithms needed to apply flood-typing across large hydrologically diverse regions. To address this gap, a benchmark dataset of manually classified flood types was compiled for 1,763 annual maximum flood peaks from 18 stream gauges in six different river basins across the conterminous United States from 1851 to 2022. Within each basin, three representative stream gauges were selected for manual flood typing. A flexible classification framework is introduced that facilitates flood typing across hydrologically diverse regions and accommodates unique combinations of weather and antecedent watershed conditions specific to each region. Floods were manually typed by domain experts using multiple lines of evidence to identify a primary surface water input of each flood (rainfall, snowmelt, or both) and, if relevant, associated storm type and secondary causal mechanisms characterizing antecedent watershed conditions. Across all the study basins, 49% of historical annual maximum flood peaks were attributable to rainfall, 28% to snowmelt, 22% to mixed precipitation, and 1% could not be assigned to a mechanism due to missing or incomplete data. The proposed flood-typing schema supports varying levels of flood typing specificity required for mixed population flood-frequency analysis, flood-type-specific design hydrographs, water quality response studies, and additional applications. This detailed, manually determined benchmark dataset serves as a resource that can be used developing and validating automated or machine learning-based algorithms capable of operationalizing expanded flood peak information.

conterminous United States↗
USGS2010

An assessment of support vector machines for land cover classification

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

International Journal of Remote Sensing↗
USGS2010

A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover

A stepwise regression tree (SRT) algorithm was developed for approximating complex nonlinear relationships. Based on the regression tree of Breiman et al . (BRT) and a stepwise linear regression (SLR) method, this algorithm represents an improvement over SLR in that it can approximate nonlinear relationships and over BRT in that it gives more realistic predictions. The applicability of this method to estimating subpixel forest was demonstrated using three test data sets, on all of which it gave more accurate predictions than SLR and BRT. SRT also generated more compact trees and performed better than or at least as well as BRT at all 10 equal forest proportion interval ranging from 0 to 100%. This method is appealing to estimating subpixel land cover over large areas.

International Journal of Remote Sensing↗
USGS2011

Assessing long-term variations in sagebrush habitat: characterization of spatial extents and distribution patterns using multi-temporal satellite remote-sensing data

An approach that can generate sagebrush habitat change estimates for monitoring large-area sagebrush ecosystems has been developed and tested in southwestern Wyoming, USA. This prototype method uses a satellite-based image change detection algorithm and regression models to estimate sub-pixel percentage cover for five sagebrush habitat components: bare ground, herbaceous, litter, sagebrush and shrub. Landsat images from three different months in 1988, 1996 and 2006 were selected to identify potential landscape change during these time periods using change vector (CV) analysis incorporated with an image normalization algorithm. Regression tree (RT) models were used to estimate percentage cover for five components on all change areas identified in 1988 and 1996, using unchanged 2006 baseline data as training for both estimates. Over the entire study area (24 950 km 2 ), a net increase of 98.83 km 2 , or 0.7%, for bare ground was measured between 1988 and 2006. Over the same period, the other four components had net losses of 20.17 km 2 , or 0.6%, for herbaceous vegetation; 30.16 km 2 , or 0.7%, for litter; 32.81 km 2 , or 1.5%, for sagebrush; and 33.34 km 2 , or 1.2%, for shrubs. The overall accuracy for shrub vegetation change between 1988 and 2006 was 89.56%. Change patterns within sagebrush habitat components differ spatially and quantitatively from each other, potentially indicating unique responses by these components to disturbances imposed upon them.

International Journal of Remote Sensing↗
USGS2019

Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring

Audio sampling of the environment can provide long-term, landscape-scale presence-absence data to model populations of sound-producing wildlife. Automated detection systems allow researchers to avoid manually searching through large volumes of recordings, but often produce unacceptable false positive rates. We developed methods that allow researchers to improve template-based automated detection using a suite of statistical learning algorithms when false positive rates are problematic. To test our method, we acquired 668 hours of recordings in the Sonoran Desert, California USA between March 2016 and May 2017, and created spectrogram cross-correlation templates for three target avian species. We trained and tested five classification algorithms and four performance-weighted ensemble classifier methods on target signals and false alarms from March 2016, and then selected high-performing ensemble classifiers from the train/test phase to predict the class of new detections thereafter. For three target species, our ensemble classifiers were able to identify 98%, 81%, and 100% of false alarms compared with the baseline template detection system, and comparative positive predictive values improved from 6% to 69%, 87% to 95%, and 2% to 77%. We show that statistical learning approaches can be implemented to mitigate false detections acquired via template-based automated detection in automated acoustic wildlife monitoring.

California↗