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

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At least 145 records · Page 8Linked to original sources

USGS2016

An index of floodplain surface complexity

Floodplain surface topography is an important component of floodplain ecosystems. It is the primary physical template upon which ecosystem processes are acted out, and complexity in this template can contribute to the high biodiversity and productivity of floodplain ecosystems. There has been a limited appreciation of floodplain surface complexity because of the traditional focus on temporal variability in floodplains as well as limitations to quantifying spatial complexity. An index of floodplain surface complexity (FSC) is developed in this paper and applied to eight floodplains from different geographic settings. The index is based on two key indicators of complexity, variability in surface geometry (VSG) and the spatial organisation of surface conditions (SPO), and was determined at three sampling scales. FSC, VSG, and SPO varied between the eight floodplains and these differences depended upon sampling scale. Relationships between these measures of spatial complexity and seven geomorphological and hydrological drivers were investigated. There was a significant decline in all complexity measures with increasing floodplain width, which was explained by either a power, logarithmic, or exponential function. There was an initial rapid decline in surface complexity as floodplain width increased from 1.5 to 5 km, followed by little change in floodplains wider than 10 km. VSG also increased significantly with increasing sediment yield. No significant relationships were determined between any of the four hydrological variables and floodplain surface complexity.

Hydrology and Earth System Sciences↗

USGS2010

Seismic hazard assessment: Issues and alternatives

Seismic hazard and risk are two very important concepts in engineering design and other policy considerations. Although seismic hazard and risk have often been used inter-changeably, they are fundamentally different. Furthermore, seismic risk is more important in engineering design and other policy considerations. Seismic hazard assessment is an effort by earth scientists to quantify seismic hazard and its associated uncertainty in time and space and to provide seismic hazard estimates for seismic risk assessment and other applications. Although seismic hazard assessment is more a scientific issue, it deserves special attention because of its significant implication to society. Two approaches, probabilistic seismic hazard analysis (PSHA) and deterministic seismic hazard analysis (DSHA), are commonly used for seismic hazard assessment. Although PSHA has been pro-claimed as the best approach for seismic hazard assessment, it is scientifically flawed (i.e., the physics and mathematics that PSHA is based on are not valid). Use of PSHA could lead to either unsafe or overly conservative engineering design or public policy, each of which has dire consequences to society. On the other hand, DSHA is a viable approach for seismic hazard assessment even though it has been labeled as unreliable. The biggest drawback of DSHA is that the temporal characteristics (i.e., earthquake frequency of occurrence and the associated uncertainty) are often neglected. An alternative, seismic hazard analysis (SHA), utilizes earthquake science and statistics directly and provides a seismic hazard estimate that can be readily used for seismic risk assessment and other applications. ?? 2010 Springer Basel AG.

Pure and Applied Geophysics↗

USGS2021

Mean squared error, deconstructed

As science becomes increasingly cross-disciplinary and scientific models become increasingly cross-coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are “good” or “bad.” This apparent weakness has led to a myriad of specialized error metrics, which are sometimes aggregated to form a composite score. Such scores are inherently subjective, however, and while their components may be interpretable, the composite itself is not. We contend that, a better approach to model benchmarking and interpretation is to decompose MSE into interpretable components. To demonstrate the versatility of this approach, we outline some fundamental types of decomposition and apply them to predictions at 1,021 streamgages across the conterminous United States from three streamflow models. Through this demonstration, we hope to show that each component in a decomposition represents a distinct concept, like “season” or “variability,” and that simple decompositions can be combined to represent more complex concepts, like “seasonal variability,” creating an expressive language through which to interrogate models and data.

Journal of Advances in Earth Systems Modeling↗

USGS2011

Characterization of the Sonoran desert as a radiometric calibration target for Earth observing sensors

To provide highly accurate quantitative measurements of the Earth's surface, a comprehensive calibration and validation of the satellite sensors is required. The NASA Moderate Resolution Imaging Spectroradiometer (MODIS) Characterization Support Team, in collaboration with United States Geological Survey, Earth Resources Observation and Science Center, has previously demonstrated the use of African desert sites to monitor the long-term calibration stability of Terra MODIS and Landsat 7 (L7) Enhanced Thematic Mapper plus (ETM+). The current study focuses on evaluating the suitability of the Sonoran Desert test site for post-launch long-term radiometric calibration as well as cross-calibration purposes. Due to the lack of historical and on-going in situ ground measurements, the Sonoran Desert is not usually used for absolute calibration. An in-depth evaluation (spatial, temporal, and spectral stability) of this site using well calibrated L7 ETM+ measurements and local climatology data has been performed. The Sonoran Desert site produced spatial variability of about 3 to 5% in the reflective solar regions, and the temporal variations of the site after correction for view-geometry impacts were generally around 3%. The results demonstrate that, barring the impacts due to occasional precipitation, the Sonoran Desert site can be effectively used for cross-calibration and long-term stability monitoring of satellite sensors, thus, providing a good test site in the western hemisphere.

Sonoran Desert↗

USGS2003

Density of the continental roots: Compositional and thermal contributions

The origin and evolution of cratonic roots has been debated for many years. Precambrian cratons are underlain by cold lithospheric roots that are chemically depleted. Thermal and petrologic data indicate that Archean roots are colder and more chemically depleted than Proterozoic roots. This observation has led to the hypothesis that the degree of depletion in a lithospheric root depends mostly on its age. Here we test this hypothesis using gravity, thermal, petrologic, and seismic data to quantify differences in the density of cratonic roots globally. In the first step in our analysis we use a global crustal model to remove the crustal contribution to the observed gravity. The result is the mantle gravity anomaly field, which varies over cratonic areas from -100 to +100 mGal. Positive mantle gravity anomalies are observed for cratons in the northern hemisphere: the Baltic shield, East European Platform, and the Siberian Platform. Negative anomalies are observed over cratons in the southern hemisphere: Western Australia, South America, the Indian shield, and Southern Africa. This indicates that there are significant differences in the density of cratonic roots, even for those of similar age. Root density depends on temperature and chemical depletion. In order to separate these effects we apply a lithospheric temperature correction using thermal estimates from a combination of geothermal modeling and global seismic tomography models. Gravity anomalies induced by temperature variations in the uppermost mantle range from -200 to +300 mGal, with the strongest negative anomalies associated with mid-ocean ridges and the strongest positive anomalies associated with cratons. After correcting for thermal effects, we obtain a map of density variations due to lithospheric compositional variations. These maps indicate that the average density decrease due to the chemical depletion within cratonic roots varies from 1.1% to 1.5%, assuming the chemical boundary layer has the same thickness as the thermal boundary layer. The maximal values of the density drop are in the range 1.7-2.5%, and correspond to the Archean portion of each craton. Temperatures within cratonic roots vary strongly, and our analysis indicates that density variations in the roots due to temperature are larger than the variations due to chemical differences.

Earth and Planetary Science Letters↗

USGS2003

Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems

Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision‐making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.

Journal of Geophysical Research E: Planets↗

USGS2025

Characterizing the scale of regional landslide triggering from storm hydrometeorology

Rainfall strongly affects landslide triggering; however, understanding how storm characteristics relate to the severity of landslides at the regional scale has thus far remained unclear, despite the societal benefits that would result from defining this relationship. As mapped landslide inventories typically cover a small region relative to a storm system, here we develop a dimensionless index for landslide-inducing rainfall, A * , based on extremes of modeled soil water relative to its local climatology. We calibrate A * using four landslide inventories, comprising over 11 000 individual landslides over four unique storm events, and find that a common threshold can be applied to estimate regional shallow-landslide-triggering potential across diverse climatic regimes in California (USA). We then use the spatial distribution of A * , along with topography, to calculate the landslide potential area (LPA) for nine landslide-inducing storm events over the past 20 years, and we test whether atmospheric metrics describing the strength of landfalling storms, such as integrated water vapor transport, correlate with the magnitude of hazardous landslide-inducing rainfall. We find that although the events with the largest LPA do occur during exceptional atmospheric river (AR) storms, the strength of landfalling atmospheric rivers does not scale neatly with landslide potential area, and even exceptionally strong ARs may yield minimal landslide impacts. Other factors, such as antecedent soil moisture driven by storm frequency and mesoscale precipitation features within storms, are instead more likely to dictate the patterns of landslide-generating rainfall throughout the state.

California↗

USGS2014

Crustal migration of CO2-rich magmatic fluids recorded by tree-ring radiocarbon and seismicity at Mammoth Mountain, CA, USA

Unrest at Mammoth Mountain over the past several decades, manifest by seismicity, ground deformation, diffuse CO 2 emissions, and elevated 3 He/ 4 He ratios in fumarolic gases has been driven by the release of CO 2 -rich fluids from basaltic intrusions in the middle to lower crust. Recent unrest included the occurrence of three lower-crustal (32–19 km depth) seismic swarms beneath Mammoth Mountain in 2006, 2008 and 2009 that were consistently followed by peaks in the occurrence rate of shallow (≤10 km depth) earthquakes. We measured 14 C in the growth rings (1998–2012) of a tree growing in the largest (∼0.3 km 2 ) area of diffuse CO 2 emissions on Mammoth Mountain (the Horseshoe Lake tree kill; HLTK) and applied atmospheric CO 2 concentration source area modeling to confirm that the tree was a reliable integrator of magmatic CO 2 emissions over most of this area. The tree-ring 14 C record implied that magmatic CO 2 emissions from the HLTK were relatively stable from 1998 to 2009, nearly doubled from 2009 to 2011, and then declined by the 2012 growing season. The initial increase in CO 2 emissions was detected during the growing season that immediately followed the largest (February 2010) peak in the occurrence rate of shallow earthquakes. Migration of CO 2 -rich magmatic fluids may have driven observed patterns of elevated deep, then shallow seismicity, while the relationship between pore fluid pressures within a shallow (upper 3 km of crust) fluid reservoir and permeability structure of the reservoir cap rock may have controlled the temporal pattern of surface CO 2 emissions.

California↗

USGS2020

A hybrid approach for predictive soil property mapping using conventional soil survey data

Soil property maps are important for land management and earth systems modeling. A new hybrid point-disaggregation predictive soil property mapping strategy improved mapping in the Colorado River Basin, and can be applied to other areas with similar data (e.g. conterminous United States). This new approach increased sample size ~6-fold over past efforts. Random forests related environmental raster layers representing soil forming factors to samples to predict 15 soil properties (pH, texture fractions, rock, electrical conductivity, gypsum, CaCO3, sodium adsorption ratio, available water capacity, bulk density, erodibility, organic matter) at 7 depths, depth to restrictive layer, and surface rock size and cover. Cross-validations resulted in coefficient of determinations averaging 0.52, with a range of 0.20 to 0.76; and mean absolute errors ranged from 3% to 98% of training data averages with a mean of 41%. Uncertainty estimates were also developed by creating relative prediction intervals (RPIs) for the entire study area, which allow end users to evaluate uncertainty relative to original data distributions. Average error increased with higher RPI values (higher uncertainty), and areas with the highest RPI are consistently under-sampled, suggesting that additional sampling in these areas may improve prediction accuracy. Greater uncertainty was also observed in areas with shale parent materials and physiographic settings uncommon relative to the broader study area.

New Mexico, Colorado, Wyoming, Utah, Nevada, Arizo↗

USGS2026

Adaptation and response in drylands: A dryland research agenda and campaign strategy

Dryland ecosystems cover 41% of Earth's land surfaces, account for 44% of cultivated lands and 60% of food sources, and make large contributions to the global water and carbon cycles. However, these ecosystems are experiencing unprecedented extremes including heatwaves, floods, and droughts as well as hotter temperatures and often declining water resource availability. These ecosystems are some of the most challenging to monitor given their high temporal variability with rapid response to their environment as well as vast spatial variability with intermixing of different plant species and life forms amongst bare soil coverage. The Adaptation and Response in Drylands (ARID) campaign was selected by National Aeronautics and Space Administration (NASA) as a scoping study to develop a research agenda for a dryland field campaign. Here, we detail our ARID science research agenda and implementation plan that were developed based on an extensive community engagement effort in over 160 events with over a thousand scientists, land managers, and Tribal communities between 2023 and 2024. The selected science themes cover drought and climate variability, ecosystem structure, function, and biodiversity, carbon cycle interannual variability and trends, and social ecological systems (land management and adaptation). We then detail our remote sensing, modeling, and field-based strategies to capture high temporal and high spatial resolution processes. Finally, our implementation strategy is presented which includes focus area selections in a core intensive western U.S. domain and distributed international domains. This strategy includes our overarching guiding principles of using multi-temporal airborne acquisitions and super sites as well as enhancing land management in co-development with end-user partners. While originally developed for NASA, our ARID report creates a blueprint for any future dryland field campaign, at any scale, that can be implemented widely for foundational and applied science objectives.

Global Change Biology↗

USGS2022

HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool

Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and diagnostic efforts will benefit from standardized methods and ready-to-use toolkits. Using the Jupyter platform, this work presents HydroBench, a model-agnostic benchmarking tool consisting of three sets of metrics: 1) common statistical predictive measures, 2) hydrological signature-based process metrics, including a new time-linked flow duration curve and 3) information-theoretic diagnostics that measure the flow of information among model variables. As a test case, HydroBench was applied to compare two model products (calibrated and uncalibrated) of the National Hydrologic Model - Precipitation Runoff Modeling System (NHM-PRMS) at the Cedar River watershed, WA, United States. Although the uncalibrated model has the highest predictive performance, particularly for high flows, the signature-based diagnostics showed that the model overestimates low flows and poorly represents the recession processes. Elucidating why low flows may have been overestimated, the information-theoretic diagnostics indicated a higher flow of information from precipitation to snowmelt to streamflow in the uncalibrated model compared to the calibrated model, where information flowed more directly from precipitation to streamflow. This test case demonstrated the capability of HydroBench in process diagnostics and model predictive and functional performance evaluations, along with their tradeoffs. Having such a model benchmarking tool not only provides modelers with a comprehensive model evaluation system but also provides an open-source tool that can further be developed by the hydrological community.

Frontiers in Earth Sciences↗

USGS2018

Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves

In many simulations of historical daily streamflow distributional bias arising from the distributional properties of residuals has been noted. This bias often presents itself as an underestimation of high streamflow and an overestimation of low streamflow. Here, 1168 streamgages across the conterminous USA, having at least 14 complete water years of daily data between 1 October 1980 and 30 September 2013, are used to explore a method for rescaling simulated streamflow to correct the distributional bias. Based on an existing approach that separates the simulated streamflow into components of temporal structure and magnitude, the temporal structure is converted to simulated nonexceedance probabilities and the magnitudes are rescaled using an independently estimated flow duration curve (FDC) derived from regional regression. In this study, this method is applied to a pooled ordinary kriging simulation of daily streamflow coupled with FDCs estimated by regional regression on basin characteristics. The improvement in the representation of high and low streamflows is correlated with the accuracy and unbiasedness of the estimated FDC. The method is verified by using an idealized case; however, with the introduction of regionally regressed FDCs developed for this study, the method is only useful overall for the upper tails, which are more accurately and unbiasedly estimated than the lower tails. It remains for future work to determine how accurate the estimated FDCs need to be to be useful for bias correction without unduly reducing accuracy. In addition to its potential efficacy for distributional bias correction, this particular instance of the methodology also represents a generalization of nonlinear spatial interpolation of daily streamflow using FDCs. Rather than relying on single index stations, as is commonly done to reflect streamflow timing, this approach to simulation leverages geostatistical tools to allow a region of neighbors to reflect streamflow timing.

Hydrology and Earth System Sciences↗

USGS2000

Volcano geodesy: Challenges and opportunities for the 21st century

Intrusions of magma beneath volcanoes deform the surrounding rock and, if the intrusion is large enough, the overlying ground surface. Numerical models generally agree that, for most eruptions, subsurface volume changes are sufficient to produce measurable deformation at the surface. Studying this deformation can help to determine the location, volume, and shape of a subsurface magma body and thus to anticipate the onset and course of an eruption. This approach has been successfully applied at many restless volcanoes, especially basaltic shields and silicic calderas, using various geodetic techniques and sensors. However, its success at many intermediate-composition strato-volcanoes has been limited by generally long repose intervals, steep terrain, and structural influences that complicate the history and shape of surface deformation. These factors have made it difficult to adequately characterize deformation in space and time at many of the world's dangerous volcanoes. Recent technological advances promise to make this task easier by enabling the acquisition of geodetic data of high spatial and temporal resolution from Earth-orbiting satellites. Synthetic aperture radar interferometry (InSAR) can image ground deformation over large areas at metre-scale resolution over time-scales of a month to a few years. Global Positioning System (GPS) stations can provide continuous information on three-dimensional ground displacements at a network of key sites -information that is especially important during volcanic crises. By using InSAR to determine the shape of the displacement field and GPS to monitor temporal changes at key sites, scientists have a much better chance to capture geodetic signals that have so far been elusive at many volcanoes. This approach has the potential to provide longer-term warnings of impending volcanic activity than is possible with other monitoring techniques.

Philosophical Transactions of the Royal Society A:↗

USGS2018

Findings and lessons learned from the assessment of the Mexico-United States transboundary San Pedro and Santa Cruz aquifers: The utility of social science in applied hydrologic research

Study Region This study region encompasses the Transboundary San Pedro and Santa Cruz aquifers which are shared between the states of Sonora (Mexico) and Arizona (US). Special regional considerations include a semi-arid climate, basin-fill aquifers with predominantly montane recharge areas, economic drivers in the mining, trade, and military sectors, groundwater-dependent cities with expanding cones of depression, interbasin groundwater transfers, ground- and surface-water contamination, and protected aquatic and riparian habitats that act as significant migration corridors for hundreds of species, including some that are threatened and endangered. Study Focus We focus on lessons learned from the hydrologic assessment of the Transboundary San Pedro and Santa Cruz aquifers. We conducted the work, in two phases: (1) laying the groundwork and (2) implementation. The “laying the groundwork” phase consisted of binational meetings with stakeholders and key actors (agencies and individuals), and the development of an understanding of the physical, institutional, historical, and socio-political context. This led to signing of the binational Transboundary Aquifer Assessment Program (TAAP) agreement in 2009 and detailed the process for cooperation and coordination in the assessment of shared aquifers. The implementation phase began with an agreement to proceed with the study of four “focus” aquifers (Santa Cruz, San Pedro, Mesilla (Conejos-Médanos in Mexico), and Hueco Bolson (Bolsón del Hueco in Mexico)) and development of associated technical teams. Though we do include a brief discussion of the lessons learned from the physical science portion of the study, the results have been described and published elsewhere. The bulk of the paper instead focuses on the findings and lessons learned from the integration of social-science perspectives into a largely physical-science based program, since there is a growing recognition of the need for this type of approach especially in the management and assessment of transboundary aquifers. New Hydrological Insights for the Region The Sonora-Arizona effort succeeded because both countries were adequately represented, and because of flexibility of skills and ability of teams comprising both university and government scientists. Teams included social and earth scientists. Including the social sciences was critical to research design and implementation, and to addressing the cultural, institutional, and socio-political contexts of transboundary aquifer assessment. Significant components of the continuing implementation phase include strategic planning, data compilation and analysis, cross-border integration of datasets, geophysical and geochemical surveys, and internal, peer, and stakeholder engagement.

Arizona, Sonora↗

USGS2021

A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic phenology learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same geographical region as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection.

Remote Sensing of Environment↗

USGS2023

Hydrologic compartmentalization and analytic-element groundwater-flow simulations for a draining mine tunnel

Draining mine tunnels contribute contaminants to groundwater and surface water, but remediation strategies may be hindered as hydrogeologic characterization and modeling of these heterogeneous features generally relies on sparse data sets. The Captain Jack mine site in Colorado, USA, presents a unique data set allowing for temporal evaluation of groundwater connectivity in the vicinity of an abandoned mine, where a hydraulic bulkhead is impounding water within the mine workings. This study applied statistical analysis of system pressure responses to bulkheading and used an analytic-element modeling approach to characterize heterogeneity and groundwater flow. Groundwater-level elevation data collected over a period of 4 years, both prior to and after bulkheading, indicate that the mine workings act as a sink to the local groundwater system. Despite groundwater flow being generally oriented towards the mine workings, there are also large vertical and horizontal hydraulic gradients which persist through time. Although the groundwater system is highly compartmentalized, statistical analysis using Kendall’s Tau indicates correlations between hydraulic head changes in the mine workings and several wells completed in crystalline bedrock, indicating the influence of fracture flow. An analytic-element model was parameterized to account for uncertainty in hydraulic conductivity, recharge, and discharge. Model results reproduced the range of observed hydraulic heads in the mine workings and adjacent igneous dikes but failed to closely simulate hydraulic heads in several wells located distal from the mine workings in granitic bedrock. The modeling approach shows potential promise, however, for conducting preliminary modeling to guide data collection at other similar mine sites.

Colorado↗

USGS2021

Local explosion detection and infrasound localization by reverse time migration using 3-D finite-difference wave propagation

Infrasound data are routinely used to detect and locate volcanic and other explosions, using both arrays and single sensor networks. However, at local distances (<15 km) topography often complicates acoustic propagation, resulting in inaccurate acoustic travel times leading to biased source locations when assuming straight-line propagation. Here we present a new method, termed Reverse Time Migration-Finite-Difference Time Domain (RTM-FDTD), that integrates numerical modeling into the standard RTM back-projection process. Travel time information is computed across the entire potential source grid via FDTD modeling to incorporate the effects of topography. The waveforms are then back-projected and stacked at each grid point, with the stack maximum corresponding to the likely source. We apply our method to three volcanoes with different network configurations, source-receiver distances, and topography. At Yasur Volcano, Vanuatu, RTM-FDTD locates explosions within ∼20 m of the source and differentiates between multiple vents. RTM-FDTD produces a more accurate location for the two Yasur subcraters than standard RTM and doubles the number of detected events. At Sakurajima Volcano, Japan, RTM-FDTD locates the source within 50 m of the active vent despite notable topographic blocking. The RTM-FDTD location is similar to that from the Time Reversal Mirror method, but is more computationally efficient. Lastly, at Shishaldin Volcano, Alaska, RTM and RTM-FDTD both produce realistic source locations (<50 m) for ground-coupled airwaves recorded on a four-station seismic network. We show that RTM is an effective method to detect and locate infrasonic sources across a variety of scenarios, and by integrating numerical modeling, RTM-FDTD produces more accurate source locations and increases the detection capability.

Alaska↗

USGS2014

Seismic structure of the central US crust and upper mantle: Uniqueness of the Reelfoot Rift

Using seismic surface waves recorded with Earthscope's Transportable Array, we apply surface wave imaging to determine 3D seismic velocity in the crust and uppermost mantle. Our images span several Proterozoic and early Cambrian rift zones (Mid-Continent Rift, Rough Creek Graben—Rome trough, Birmingham trough, Southern Oklahoma Aulacogen, and Reelfoot Rift). While ancient rifts are generally associated with low crustal velocity because of the presence of thick sedimentary sequences, the Reelfoot Rift is unique in its association with low mantle seismic velocity. Its mantle low-velocity zone (LVZ) is exceptionally pronounced and extends down to at least 200 km depth. This LVZ is of variable width, being relatively narrow ( ∼ 50 km "> ∼50km wide) within the northern Reelfoot Rift, which hosts the New Madrid Seismic Zone (NMSZ). We hypothesize that this mantle volume is weaker than its surroundings and that the Reelfoot Rift consequently has relatively low elastic plate thickness, which would tend to concentrate tectonic stress within this zone. No other intraplate ancient rift zone is known to be associated with such a deep mantle low-velocity anomaly, which suggests that the NMSZ is more susceptible to external stress perturbations than other ancient rift zones.

Reelfoot Rift↗