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At least 883 records · Page 49Linked to original sources
USGS2018

Induced earthquake families reveal distinctive evolutionary patterns near disposal wells

The timing of events in seismic sequences can provide insights into the physical processes controlling fault slip. In southern Kansas, the rate of earthquakes rose rapidly starting in 2013 following expansion of energy production into the area, demanding the disposal of large volumes of wastewater into deep wells. Seismicity catalogs that are complete to low magnitudes can provide insights into the physical processes that induce seismicity near wastewater disposal. We develop a catalog of over 130,000 earthquakes recorded in southern Kansas from mid‐March 2014 through December 2017 by applying a matched filter algorithm to an original catalog of 5,831 template earthquakes. Detections have nearly identical waveforms to their associated template event and represent slip on nearly co‐located sections of a fault. We select template events with at least 100 associated detections and examine the characteristics of these prolific families of earthquakes. We find that families located close (<10 km) to areas with significant volumes of injected fluids have near‐Poissonian interevent times and the families remain active over longer durations. Families farther from high‐volume injection wells show strong clustering of interevent times and shorter sequence durations. We conclude that increasing pore fluid pressures from nearby disposal of large volumes of wastewater is the primary driver of these long duration episodes, with earthquake‐earthquake interactions driving sequences at greater distance from the wells.

Kansas↗
USGS2019

Peak ground displacement saturates exactly when expected: Implications for earthquake early warning

The scaling of rupture properties with magnitude is of critical importance to earthquake early warning (EEW) systems that rely on source characterization using limited snapshots of waveform data. ShakeAlert, a prototype EEW system that is being developed for the western United States, provides real-time estimates of earthquake magnitude based on P-wave peak ground displacements measured at stations triggered by the event. The algorithms used in ShakeAlert assume that the displacement measurements at each station are statistically independent and that there exists a linear and time-independent relation between log peak ground displacement and earthquake magnitude. Here we challenge this basic assumption using a comprehensive database of more than 130,000 vertical component waveforms from M4.5-M9 earthquakes occurring near Japan from 1997 through 2017 and recorded by the K-NET and KiK-net strong-motion networks. By analyzing the time-evolution of P-wave peak ground displacements for these earthquakes, we show that there is a break, or saturation, in the magnitude-displacement scaling that depends on the length of the measurement time window. We demonstrate that the magnitude at which this saturation occurs is well-explained by a simple and non-deterministic model of earthquake rupture growth. We then use the predictions of this saturation model to develop a Bayesian framework for estimating posterior uncertainties in real-time magnitude estimates which incorporates the expected time-dependence of the peak displacement measurements.

Journal of Geophysical Research B: Solid Earth↗
USGS2019

Vertical coseismic offsets from differential high-resolution stereogrammetric DSMs: The 2013 Baluchistan, Pakistan earthquake

The recent proliferation of high-resolution (< 3-m spatial resolution) digital topography datasets opens a spectrum of geodetic applications in differential topography, including the quantification of coseismic vertical displacement fields. Most investigations of coseismic vertical displacements to date rely, in part, on pre- or post-event lidar surveys that are intractable or non-existent in many locales. Stereogrammetric digital surface models (DSMs) derived from high-resolution satellite optical imagery provide a new avenue for the retrieval of spatially-dense vertical coseismic displacements on a global scale. In this study, we generated 2-m resolution pre- and post-seismic DSMs from satellite optical imagery spanning the 2013 Mw7.7 Baluchistan strike-slip earthquake that occurred on the Hoshab fault in southern Pakistan. We applied the Iterative Closest Point algorithm to the DSMs to quantify the coseismic vertical displacement field at a spatial resolution of 10-30 m and to generate 3D coseismic strain tensors. We found that across-fault vertical offsets alternated between uplift and subsidence and varied between ~1-3 m in a non-systematic manner along the Hoshab fault. We show that the pre-existing topography and near-fault geomorphology are variably consistent and inconsistent with the displacement kinematics of the 2013 earthquake, and we argue that these relationships highlight varied slip sense history along the Hoshab fault. Notably, topography along the southern extents of the Hoshab fault requires different surface displacement kinematics than occurred in the 2013 earthquake, suggesting that the Hoshab fault accommodates varying senses of slip (bimodal slip) through time.

Baluchistan Province↗
USGS2020

Tidal wetland gross primary production across the continental United States, 2000–2019

We mapped tidal wetland gross primary production (GPP) with unprecedented detail for multiple wetland types across the continental United States (CONUS) at 16‐day intervals for the years 2000–2019. To accomplish this task, we developed the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field‐based eddy covariance tower data into a single Bayesian framework, and used a super computer network and remote sensing imagery (Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index). We found a strong fit between the BC model and eddy covariance data from 10 different towers ( r 2 = 0.83, p < 0.001, root‐mean‐square error = 1.22 g C/m 2 /day, average error was 7% with a mean bias of nearly zero). When compared with NASA's MOD17 GPP product, which uses a generalized terrestrial algorithm, the BC model reduced error by approximately half (MOD17 had r 2 = 0.45, p < 0.001, root‐mean‐square error of 3.38 g C/m 2 /day, average error of 15%). The BC model also included mixed pixels in areas not covered by MOD17, which comprised approximately 16.8% of CONUS tidal wetland GPP. Results showed that across CONUS between 2000 and 2019, the average daily GPP per m 2 was 4.32 ± 2.45 g C/m 2 /day. The total annual GPP for the CONUS was 39.65 ± 0.89 Tg C/year. GPP for the Gulf Coast was nearly double that of the Atlantic and Pacific Coasts combined. Louisiana alone accounted for 15.78 ± 0.75 Tg C/year, with its Atchafalaya/Vermillion Bay basin at 4.72 ± 0.14 Tg C/year. The BC model provides a robust platform for integrating data from disparate sources and exploring regional trends in GPP across tidal wetlands.

Global Biogeochemical Cycles↗
USGS2019

Surface rupture and distributed deformation revealed by optical satellite imagery: The intraplate 2016 Mw 6.0 Petermann Ranges earthquake, Australia

High-resolution optical satellite imagery is used to quantify vertical surface deformation associated with the intraplate 20 May 2016 Mw 6.0 Petermann Ranges earthquake, Northern Territory, Australia. The 21 ╓ 1 km long NW-trending rupture resulted from reverse motion on a northeast-dipping fault. Vertical surface offsets of up to 0.7 ╓ 0.1 m distributed across a 0.5-to-1 km wide deformation zone are measured using the Iterative Closest Point (ICP) algorithm to compare pre- and post-earthquake digital elevation models (DEMs) derived from Worldview imagery. The results are validated by comparison with field-based observations and interferometric synthetic aperture radar (InSAR). The pattern of surface uplift is consistent with distributed shear above the propagating tip of a reverse fault, leading to both an emergent fault and folding proximal to the rupture. This study demonstrates the potential for quantifying modest (<1 m) vertical deformation on a reverse fault using optical satellite imagery.

Geophysical Research Letters↗
USGS2020

Methods for rapidly estimating velocity precision from GNSS time series in the presence of temporal correlation: A new method and comparison of existing methods

Time series of position estimates from Global Navigational Satellite System (GNSS) are used to measure the velocities of points on the surface of the Earth. Along with the velocity estimates, a measure of the precision is needed to assess the quality of the velocity measurement. Here, I evaluate rate uncertainties provided by four different methods that have been applied to geodetic time series. The most rigorous approach uses a data covariance that incorporates a variety of noise processes relevant to geodetic time series but is computationally demanding. Two other approaches are efficient algorithms and are used widely, but both can provide less rigorous estimates of the rate uncertainty. I propose and evaluate a fourth method, which provides estimates of rate uncertainty closer to the rigorous approach but is significantly less computationally demanding. I have evaluated all three methods against the more rigorous method using both simulations and time series from 190 GNSS sites. For data best characterized as having a flicker type noise process, one of the widely used methods overestimates the uncertainty by up to a factor of 2, while the other widely used method underestimates the uncertainty by less than a factor of 2. For a random-walk process, both methods underestimate the rate uncertainty by a factor of 3 to 5.

Journal of Geophysical Research↗
USGS2020

Some experiments in extreme-value statistical modeling of magnetic superstorm intensities

In support of projects for forecasting and mitigating the deleterious effects of extreme space-weather storms, an examination is made of the intensities of magnetic superstorms recorded in the Dst index time series (1957-2016). Modified peak-over-threshold and solar-cycle, block-maximum sampling of the Dst time series are performed to obtain compi-lations of storm-maximum −Dstm intensity values. Lognormal, upper-limit lognormal, generalized Pareto, and generalized extreme-value model distributions are fitted to the−Dstm data using a maximum-likelihood algorithm. All four candidate models provide good representations of the data. Comparisons of the statistical significance and good-ness of fits of the various models gives no clear indication as to which model is best. The statistical models are used to extrapolate to extreme-value intensities, such as would be expected (on average) to occur once per century. An upper-limit lognormal fit to peak-over-threshold −Dstm data above a superstorm threshold of 283 nT gives a 100-year ex-trapolated intensity of 542 nT and a 68% confidence interval (obtained by bootstrap re-sampling) of [466, 583] nT. An upper-limit lognormal fit to solar-cycle, block-maximum−DstBM data gives a 9-solar-cycle (approximately 100-year) extrapolated intensity of 553 nT. The Dst data are found to be insufficient for providing usefully accurate esti-mates of a statistically theoretical upper limit for magnetic storm intensity. Secular change in storm intensities is noted, as is a need for improved estimates of pre-1957 magnetic storm intensities.

Space Weather↗
USGS2021

Extreme-event magnetic storm probabilities derived from rank statistics of historical Dst intensities for solar cycles 14-24

A compilation is made of the largest and second-largest magnetic-storm-maximum intensities, − Dst 1 and − Dst 2 , for solar cycles 14–24 (1902–2016) by sampling Oulu Dcx for cycles 19–24, using published − Dst m values for 4 intense storms in cycles 14, 15, and 18 (1903, 1909, 1921, 1946), and calculating 15 new storm-maximum − Dst m values (reported here) for cycles 14–18. Three different models are fitted to the cycle-ranked − Dst 1 and − Dst 2 values using a maximum-likelihood algorithm: A Gumbel model, an unconstrained Generalized-Extreme-Value model, and a Weibull model constrained to have a physically justified maximum storm intensity of − Dst m = 2500 nT. All three models are good descriptions of the data. Since the best model is not clearly revealed with standard statistical tests, inference is precluded of the source process giving rise to storm-maximum − Dst m values. Of the three candidate models, the constrained Weibull gives the lowest superstorm occurrence probabilities. Using the compiled data and the constrained Weibull model, a once-per-century storm intensity is estimated to be − Dst 1 = 663 nT, with a bootstrap 68% confidence interval of [497, 694] nT. Similarly, the probability that a future storm will have an intensity exceeding that of the March 1989 superstorm, − Dst m > 565 nT, is 0.246 per cycle with a 68% confidence interval of [0.140, 0.311] per cycle. Noting (possibly slight) ambiguity in the rankings of storm intensities, using the same methods, but storms more intense than those identified for cycles 14–16, would yield a higher once-per-century intensity and a higher probability for a − Dst m > 565 nT storm.

Space Weather↗
USGS2021

Estimating and forecasting time-varying groundwater recharge in fractured rock: A state-space formulation with preferential and diffuse flow to the water table

Rapid infiltration following precipitation may result in groundwater contamination from surface contaminants or pathogens. In fractured rock, contaminants can migrate rapidly to points of groundwater withdrawals. In contrast to the temporal availability of groundwater quality chemical indicators, meteorological and groundwater level observations are available in real-time to estimate time-varying recharge, which can act as a surrogate to identify periods of rapid infiltration that may indicate contamination susceptibility. Estimating recharge using methods, such as base-flow recession, unsaturated infiltration models, or Water-Table Fluctuations (WTF), cannot capitalize on currently available technologies and telecommunication infrastructure to conduct real-time recharge estimation at scales relevant to characterizing rapid infiltration. We present a linear, physics-based State-Space (SS) model of one-dimensional infiltration to estimate recharge, which includes preferential and diffuse-flow to the water table. The model can take advantage of real-time data for water-table altitude, precipitation, and evapotranspiration. Model parameters are calibrated over an observation period, and the Kalman Filter (KF) is subsequently applied to continuously update the observed (water-table altitude) and unobserved (groundwater recharge) system states and predict future states as new data become available. The SS/KF algorithm is demonstrated at the Masser Groundwater Recharge Site in Pennsylvania, USA and comparisons are made with recharge estimates from WTF methods. Model results indicate that the frequency of observations (daily versus sub-daily) dictates the allocation between preferential and diffuse flow. Additionally, because infiltration processes encompass many nonlinearities, model parameters estimated from observation periods need to be updated at least seasonally to account for changing recharge conditions.

Water Resources Research↗
USGS2021

Robust earthquake early warning at a fraction of the cost: ASTUTI Costa Rica

We show that a fixed smartphone network can provide robust Earthquake Early Warning for at least two orders of magnitude less cost than scientific-grade networks. Our software and cloud-based data architecture that we have constructed for the Alerta Sismica Temprana Utilizando Teléfonos Inteligentes (ASTUTI; Earthquake Early Warning Utilizing Smartphones) network in Costa Rica is easily scaled and exported. Implementation comprises provisioning and installing modern smartphones in judicious locations. Stand-up time for regionally operational networks can be on the order of days. We evaluated a non-parametric ground-motion detection and alerting strategy that would alert the entire Costa Rican population of any event with a ground motion detection threshold of 0.55–0.65 %g at four neighboring stations. During a 6-month evaluation period ASTUTI detected and alerted on five of 13 earthquakes with M w 4.8–5.3 that caused felt Modified Mercalli Intensity shaking levels of 4.3–6. The system did not produce any false alerts and the undetected events did not produce wide-spread or significant felt shaking. System latencies were less than or similar to scientific-grade latencies. Alerts for all five detected events would have reached the capital city, San Jose, before strong S -wave shaking. This would have afforded time for Drop Cover Hold On actions by most residents. Two of the five alerts were triggered by P -waves suggesting that smartphone-based networks could approach the fastest theoretical EEW performance, especially with future expected improvements in smartphone sensors and processing algorithms.

AGU Advances↗
USGS2021

Evidence for latent crustal fluid injection transients in southern California from long-duration earthquake swarms

Earthquake swarms are manifestations of aseismic driving processes deep in the crust. We examine the spatiotemporal distribution of aseismic processes in Southern California using a 12-years catalog of swarms derived with deep learning algorithms. In a core portion of the plate boundary region, which is not associated with elevated heat flow, we identify 92 long-duration swarms ranging from 6 months to 7 years that constitute 26.4% of the total seismicity. We find that 53% of the swarms exhibit ultra-slow diffusive patterns with propagating backfronts, consistent with expectations for natural fluid injection processes. The chronology of the swarms indicates that the aseismic driving processes were active at all times during 2008–2020. The observations challenge common views about the nature of swarms, which would characterize any one of these sequences as anomalous. The regional prevalence of these sequences suggests that transient fluid injection processes play a key role in crustal fluid transport.

California↗
USGS2021

Clays are not created equal: How clay mineral type affects soil parameterization

Clay minerals dominate the soil colloidal fraction and its specific surface area. Differences among clay mineral types significantly influence their effects on soil hydrological and mechanical behavior. Presently, the soil clay content is used to parameterize soil hydraulic and mechanical properties (SHMP) for land surface models while disregarding the type of clay mineral. This undifferentiated use of clay leads to inconsistent parameterization, particularly between tropical and temperate soils, as shown herein. We capitalize on recent global maps of clay minerals that exhibit strong climatic and spatial segregation of active and inactive clays to consider spatially resolved clay mineral types in SHMP estimation. Clay mineral-informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes.

Geophysical Research Letters↗
USGS2022

Application of recursive estimation to heat tracing for groundwater/surface-water exchange

We present and demonstrate a recursive-estimation framework to infer groundwater/surface-water exchange based on temperature time series collected at different vertical depths below the sediment/water interface. We formulate the heat-transport problem as a state-space model (SSM), in which the spatial derivatives in the convection/conduction equation are approximated using finite differences. The SSM is calibrated to estimate time-varying specific discharge using the Extended Kalman Filter (EKF) and Extended Rauch-Tung-Striebel Smoother (ERTSS). Whereas the EKF is suited to real-time (“online”) applications and uses only the past and current measurements for estimation (filtering), the ERTSS is intended for near-real time or batch-processing (“offline”) applications and uses a window of data for batch estimation (smoothing). The two algorithms are demonstrated with synthetic and field-experimental data and are shown to be efficient and rapid for the estimation of time-varying flux over seasonal periods; further, the recursive approaches are effective in the presence of rapidly changing flux and (or) nonperiodic thermal boundary conditions, both of which are problematic for existing approaches to heat tracing of time-varying groundwater/surface-water exchange.

Water Resources Research↗
USGS2022

Remote sensing of visible dye concentrations during a tracer experiment on a large, turbid river

Understanding dispersion in rivers is critical for numerous applications, such as characterizing larval drift for endangered fish species and responding to spills of hazardous materials. Injecting a visible dye into the river can yield insight on dispersion processes, but conventional field instrumentation yields limited data on variations in dye concentration over time at a few, fixed points. Remote sensing can provide more detailed, spatially distributed information on the dye's motion, but this approach has only been tested in clear-flowing streams. The purpose of this study was to assess the potential of remote sensing to facilitate tracer studies in more turbid rivers. To pursue this objective, we injected Rhodamine WT dye into the Missouri River and collected field spectra from a boat, videos from a small unoccupied aircraft system (sUAS), and orthophotos from an airplane. Applying an optimal band ratio analysis (OBRA) algorithm to the field spectra revealed strong correlations ( R 2 = 0.936) between a spectrally based quantity and in situ concentration measurements. OBRA also performed well for broadband RGB (red, green, blue) images extracted from the sUAS-based videos; the resulting concentration maps were used to produce animations that captured movement of the dye pulse. Spectral mixture analysis of repeat orthophoto coverage yielded relative concentration estimates that provided a synoptic perspective on dispersion of the dye throughout the entire 13.8 km reach over the full 2.5-hr duration of the experiment. The results of this study demonstrate the potential to remotely sense tracer dye concentrations in large, highly turbid rivers.

Missouri↗
USGS2022

Tree rings reveal unmatched 2nd century drought in the Colorado River Basin

The ongoing 22 year drought in the Upper Colorado River Basin (UCRB) has been extremely severe, even in the context of the longest available tree-ring reconstruction of annual flow at Lees Ferry, Arizona, dating back to 762 CE. While many southwestern drought assessments have been limited to the past 1200 years, longer paleorecords of moisture variability do exist for the UCRB. Here, gridded drought-atlas data in the UCRB domain along with naturalized streamflow data from the instrumental period (1906–2021) are used in a K nearest neighbor (KNN) nonparametric algorithm to develop a streamflow reconstruction for the Lees Ferry gage starting in 1 CE. The reconstruction reveals a 2nd century drought unmatched in severity by the current drought or by well-documented medieval period droughts in the UCRB. Although data are sparse, analysis of individual long tree ring records and other paleoclimatic data also support the occurrence of an exceptional 2nd century drought.

Arizona, Colorado, New Mexico, Utah, Wyoming↗
USGS2023

Identification of seismo-volcanic regimes at Whakaari/White Island (New Zealand) via systematic tuning of an unsupervised classifier

We present an algorithm based on Self-Organizing Maps (SOM) and k-means clustering to recognize patterns in a continuous 12.5-year tremor time series recorded at Whakaari/White Island volcano, New Zealand (hereafter referred to as Whakaari). The approach is extendable to a variety of volcanic settings through systematic tuning of the classifier. Hyperparameters are evaluated by statistical means, yielding a combination of “ideal” SOM parameters for the given data set. Extending from this, we applied a Kernel Density Estimation approach to automatically detect changes within the observed seismicity. We categorize the Whakaari seismic time series into regimes representing distinct volcano-seismic states during recent unrest episodes at Whakaari (2012/2013, 2016, and 2019). There is a clear separation in classification results between background regimes and those representing elevated levels of unrest. Onset of unrest is detected by the classifier 6 weeks before the August 2012 eruption, and ca. 3.5 months before the December 2019 eruption, respectively. Regime changes are corroborated by changes in commonly monitored tremor proxies as well as with reported volcanic activity. The regimes are hypothesized to represent diverse mechanisms including: system pressurization and depressurization, degassing, and elevated surface activity. Labeling these regimes improves visualization of the 2012/2013 and 2019 unrest and eruptive episodes. The pre-eruptive 2016 unrest showed a contrasting shape and nature of seismic regimes, suggesting differing onset and driving processes. The 2016 episode is proposed to result from rapid destabilization of the shallow hydrothermal system, while rising magmatic gases from new injections of magma better explain the 2012/2013 and 2019 episodes.

Whakaari, White Island↗
USGS2023

Moving Aircraft River Velocimetry (MARV): Framework and proof-of-concept on the Tanana River

Information on velocity fields in rivers is critical for designing infrastructure, modeling contaminant transport, and assessing habitat. Although non-contact approaches to measuring flow velocity are well established, these methods assume a stationary imaging platform. This study eliminates this constraint by introducing a framework for moving aircraft river velocimetry (MARV). The workflow takes as input images acquired from an airplane and involves orthorectification, frame overlap analysis, image enhancement, particle image velocimetry (PIV), and aggregation of the resulting velocity vectors onto a prediction grid. We also use new metrics to quantify the agreement between image-derived and field-measured velocity vectors in terms of both orientation and magnitude. The potential of MARV was evaluated using data from two Alaskan rivers: a large, highly turbid channel and its smaller, clearer tributary. Sediment boil vortices on the mainstem provided natural features trackable via PIV and estimated velocities corresponded closely with field measurements ( R 2 up to 0.911). We compared an exhaustive approach that evaluates overlap for all frame combinations to a simpler rolling window implementation and found that the more efficient algorithm did not compromise accuracy. Sensitivity analysis suggested that the method was robust to window parameterization. Comparing PIV output from different flying heights and imaging systems indicated that larger pixels led to higher accuracy and that a more advanced dual-camera system provided superior performance. Results from the tributary were less encouraging, presumably due to a lack of trackable features in visible images. Testing across a range of rivers is needed to assess the generality of MARV.

Water Resources Research↗
USGS2023

Predicting daily river chlorophyll concentrations at a continental scale

Eutrophication is one of the largest threats to aquatic ecosystems and chlorophyll a measurements are relevant indicators of trophic state and algal abundance. Many studies have modeled chlorophyll a in rivers but model development and testing has largely occurred at individual sites which hampers creating generalized models capable of making broad-scale predictions. To address this gap, we compiled a large data set of chlorophyll a concentrations matched to other water quality, meteorological, and reach characteristic data for a diverse set of 82 streams and rivers across the United States. We used this data set and extreme gradient boosting, a tree-based machine learning algorithm, to predict daily chlorophyll a concentrations. Furthermore, we tested several practical considerations of broad-scale models, such as making predictions at sites not included in model training or the utility of in situ water quality data versus universally available remotely estimated model inputs. Predictions were very strongly correlated to observations when compared against a randomly withheld subset of days; however, the model had lower accuracy when applied to completely novel sites withheld from model training. Turbidity and total nitrogen were the two most important variables for predicting chlorophyll a . Although in situ variables improved modeled estimates and were identified as more important during model interpretation, using only remote inputs still resulted in highly correlated predictions with small bias. Testing a model across many sites allowed for identification of common variables relevant to chlorophyll a and highlighted several challenges for applying data-driven models to new sites or at larger spatial scales.

Water Resources Research↗