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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Space Geodesy Monitoring Mass Transport in Global Geophysical Fluids

Mass transports occurring in the atmosphere-hydrosphere-cryosphere-solid Earth-core system (the 'global geophysical fluids') are important geophysical phenomena. They occur on all temporal and spatial scales. Examples include air mass and ocean circulations, oceanic and solid tides, hydrological water and idsnow redistribution, mantle processes such as post-glacial rebound, earthquakes and tectonic motions, and core geodynamo activities. The temporal history and spatial pattern of such mass transport are often not amenable to direct observations. Space geodesy techniques, however, have proven to be an effective tool in monitorihg certain direct consequences of the mass transport, including Earth's rotation variations, gravitational field variations, and the geocenter motion. Considerable advances have been made in recent years in observing and understanding of these geodynamic effects. This paper will use several prominent examples to illustrate the triumphs in research over the past years under a 'Moore's law' in space geodesy. New space missions and projects promise to further advance our knowledge about the global mass transports. The latter contributes to our understanding of the geophysical processes that produce and regulate the mass transports, as well as of the solid Earth's response to such changes in terms of Earth's mechanical properties.

Chao, Benjamin F.↗
NASA NTRS2010

Payload-Directed Control of Geophysical Magnetic Surveys

Using non-navigational (e.g. imagers, scientific) sensor information in control loops is a difficult problem to which no general solution exists. Whether the task can be successfully achieved in a particular case depends highly on problem specifics, such as application domain and sensors of interest. In this study, we investigate the feasibility of using magnetometer data for control feedback in the context of geophysical magnetic surveys. An experimental system was created and deployed to (a) assess sensor integration with autonomous vehicles, (b) investigate how magnetometer data can be used for feedback control, and (c) evaluate the feasibility of using such a system for geophysical magnetic surveys. Finally, we report the results of our experiments and show that payload-directed control of geophysical magnetic surveys is indeed feasible.

Lee, Ritchie↗
NASA NTRS2019

Assessing Uncertainties of a Geophysical Approach to Estimate Surface Fine Particulate Matter Distributions from Satellite-Observed Aerosol Optical Depth

Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2(exp) resolution for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2(exp) horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5∕AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5∕AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5∕AOD lead to an error of 11 µg m−3(exp) in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 µg m−3(exp). Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5∕AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.

aerosol optical depth↗
NASA NTRS2024

Geologic Provinces Beneath the Greenland Ice Sheet Constrained by Geophysical Data Synthesis

Present understanding of Greenland's subglacial geology is derived mostly from interpolation of geologic mapping of its ice-free margins and unconstrained by geophysical data. Here we refine the extent of its geologic provinces by synthesizing geophysical constraints on subglacial geology from seismic, gravity, magnetic and topographic data. North of 72°N, no province clearly extends across the whole island, leaving three distinct subglacial regions yet to be reconciled with margin geology. Geophysically coherent anomalies and apparent province boundaries are adjacent to the onset of faster ice flow at both Petermann Glacier and the Northeast Greenland Ice Stream. Separately, based on their subaerial expression, dozens of unusually long, straight and sub-parallel subglacial valleys cross Greenland's interior and are not yet resolved by current syntheses of its subglacial topography.

Joseph A. MacGregor↗
DOE OSTI2022

Sensitivity of geophysical techniques for monitoring secondary CO 2 storage plumes

For geologic carbon storage, the ability to detect secondary CO 2 plumes—defined as those CO 2 plumes accumulating outside the intended storage reservoir—is fundamental to preventing unexpected CO 2 migration into groundwater resources and for risk and liability management. Understanding the sensitivity of various geophysical methods to secondary plumes is crucial for designing cost-effective monitoring schemes. We use several modeling scenarios to demonstrate the process of assessing sensitivities and detection thresholds of three primary geophysical techniques—surface seismic, borehole-to-surface electromagnetic (EM), and surface and borehole gravity—for early detection of secondary CO 2 plumes in the post-injection phase. While seismic reflection methods are often considered in monitoring strategies to track the evolution of CO 2 plumes, they are also the most expensive. Due to cost considerations, especially for long-term post-injection monitoring, other techniques complement seismic monitoring when designing an adaptive monitoring network. Borehole-to-surface EM or surface gravity surveys are feasible for time-lapse monitoring of deep secondary CO 2 plumes. Furthermore, these surveys could be carried at intervals defined by site-specific conditions. If time-lapse EM and/or gravity surveys detect any signal responses beyond the expected change, it would trigger a need for the higher resolution seismic survey.

58 GEOSCIENCES↗
DOE OSTI2022

Correction to: Investigating the rheology of fluidized and non-fluidized gas-particle beds: implications for the dynamics of geophysical flows and substrate entrainment

We report natural geophysical mass flows are among the most complex granular systems and their dynamics are often modified by the presence of an interstitial fluid. Prediction of their runout requires the development of models estimating the solid stresses in these hazardous currents wherein excess pore-fluid pressure can develop. We use discrete element modelling (DEM-CFD) with a Coarse-Graining post-processing technique (CG) to investigate the rheology of unsteady gas-particle fluidized to non-fluidized granular beds placed on horizontal and inclined planes. Similar to fluidized beds immersed in viscous fluids, the effective friction coefficient of air-fluidized beds can be defined as a function of the classic μ(I)-rheology and the non-dimensional fluid or solid pressure to explain the failure and dynamics of granular flows with excess pore pressure on inclines. However, dilation imposed by fluid drag and particle collisions in gas-particle fluidized beds can drastically change its effective frictional properties. In contrast with the common assumption in water-particle flows that granular temperature is negligible, in our gas-particle simulations, the contribution of the velocity fluctuations to the stress tensor is significant. Hence, the shear stress is found to be non-zero even when the flow is fully fluidized in the inertial regime. These results suggest the need to better understand velocity fluctuations to predict the effective viscosity of sheared fluidized mixtures and are relevant for many applications. Notably, a unified approach is useful for many geophysical flows that encompass a range of fluidization conditions in a single flow such as pyroclastic density currents and snow avalanches.

58 GEOSCIENCES↗
DOE OSTI2024

Geophysical methods reveal the soil architecture and subsurface stratigraphic heterogeneities across land-lake interfaces along Lake Erie

Abstract Purpose The land-lake interface is a unique zone where terrestrial and aquatic ecosystems meet, forming part of the Earth’s most geochemically and biologically active zones. The unique characteristics of this interface are yet to be properly understood due to the inherently high spatiotemporal variability of subsurface properties, which are difficult to capture with the traditional soil sampling methods. Geophysical methods offer non-invasive techniques to capture variabilities in soil properties at a high resolution across various spatiotemporal scales. Methods We combined electromagnetic induction (EMI), electrical resistivity tomography (ERT), and ground penetrating radar (GPR) with data from soil cores and in situ sensors to investigate hydrostratigraphic heterogeneities across land-lake interfaces along the western basin of Lake Erie. Results EMI revealed high spatial heterogeneities in ECa distribution across the land-lake interfaces, with higher values in the wetland and transition zones compared to the upland zone. Soil ECa maps matched soil maps from a public database with the hydric soil units delineated as high conductivity zones (ECa > 40 mS/m). ERT and GPR showed vertical variation in soil properties with clear stratigraphic boundaries, and correlation of ERT profiles with lithologs from piezometers revealed the stratigraphic units of silt–clay and till sequence down to 3.5 m depth which are consistent with the surficial geology of the study area. Conclusions These results validate the use of multiple geophysical methods for extrapolating soil properties and mapping stratigraphic structures at land-lake interfaces, thereby providing the missing information required to improve the earth system model (ESM) of coastal interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advances in geophysical forensic event monitoring

Forensic analysis of man-made, non-nuclear events (such as industrial accidents, explosion experiments and mine collapses) has become more frequent and detailed owing to advancements in geophysical monitoring. Here, in this Technical Review, we demonstrate how geophysical forensic monitoring using seismic, infrasound and hydroacoustic recordings provides insights on events in the solid earth, atmosphere and underwater. Advanced techniques, including machine-learning-based models, have been developed to detect, identify and investigate these events, providing information on location, subevents, sources and explosive yield. The increase in data availability, application of advanced methods and computation and the growth of multitechnology approaches have increased the accuracy of forensic event analysis and enabled more realistic characterization of uncertainties. For example, the 2020 Beirut explosion in Lebanon demonstrated that various seismic, acoustic and other methods could be used to estimate explosive yield (and yield uncertainties) of about 1 ktonne, providing confidence in the application of these methods to smaller events where data are available. However, forensic investigations remain largely limited to known events with identified sources. Increased access to data, sophisticated analysis methods and high-resolution earth models will improve forensic event analysis further, enabling civil and scientific applications, such as localization in the search for the lost ARA San Juan submarine.

geophysics↗
DOE OSTI2026

Geophysical survey associated with NEON AOP survey, East River, CO 2018

The package contains data layers developed and used in Falco et al. 2024: “EcoImaging: Advanced Sensing to Investigate Plant and Abiotic Hierarchical Spatial Patterns in Mountainous Watersheds". The package is part of the DOE Watershed Function Science Focus Area (SFA) project and includes geophysical measurements collected at the East River, Colorado, in conjunction with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey conducted in June 2018. This dataset provide soil geophysical information and were used to investigate soil-plant relationships. The dataset consists of: - NEON_2018_EMI_survey.zip: the electromagnetic induction (EMI) survey as shape-file; - NEON_plot_TDR.csv: plot‑level data from Time‑Domain Reflectometry (TDR) measurements, providing: * volumetric water content (VWC) in percent (%); * soil temperature in degrees Celsius (°C); - file level metadata (flmd.csv) - data dictionary (dd.csv) file This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS2003

A method of hidden Markov model optimization for use with geophysical data sets

Geophysics research has been faced with a growing need for automated techniques with which to process large quantities of data. A successful tool must meet a number of requirements: it should be consistent, require minimal parameter tuning, and produce scientifically meaningful results in reasonable time. We introduce a hidden Markov model (HMM)-based method for analysis of geophysical data sets that attempts to address these issues.

Markov model optimization geophysical data sets↗
DOE OSTI2021

Parallel exponential time differencing methods for geophysical flow simulations

Two ocean models are considered for geophysical flow simulations: the multilayer shallow water equations and the multilayer primitive equations. For the former, we investigate the parallel performance of exponential time differencing (ETD) methods, including exponential Rosenbrock–Euler, ETD2wave, and B-ETD2wave. For the latter, we take advantage of the splitting of barotropic and baroclinic modes and propose a new two-level method in which an ETD method is applied to solve the fast barotropic mode. Furthermore, these methods could improve the computational efficiency of numerical simulations because ETD methods allow for much larger time step sizes than traditional explicit time-stepping techniques that are commonly used in existing computational ocean models. Several standard benchmark tests for ocean modeling are performed and comparison of the numerical results demonstrates a great potential of applying the parallel ETD methods for simulating real-world geophysical flows.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Rapid Coal-Ash Characterization using Geophysical Methods & Machine Learning

Coal combustion products (CCP) are challenging to delineate in heterogeneous field settings. Conventional methods (test pits, coring, and laboratory analyses) are labor-intensive, slow, invasive, and provide sparse spatial coverage. This study evaluates whether rapid non-invasive geophysical screening methods—induced polarization (IP), magnetic susceptibility, and nuclear magnetic resonance (NMR) —combined with surface colorimetry (RGB_24), can discriminate CCP-soil mixtures and provide reliable estimates of CCP content. Laboratory measurements were collected on five CCP-soil mixtures (series) and modeled using (i) a linear baseline, (ii) a calibrated non-linear (power-mean) model, and (iii) a machine-learning (ML) Random Forest approach, with validation via leave-one-series-out and site-specific tests. Across the five series, individual signals—particularly IP and magnetic susceptibility—were strongly predictive of ash content but were consistently outperformed by combined models. The pooled calibrated non-linear and ML models captured the observed non-linearity and achieved high accuracy and precision, improving on linear fits. Colorimetry showed the weakest direct relationship with ash content for the tested samples but improved performance when included in multi-signal models. At pre-selected 3.5% decision threshold, calibrated and ML approaches yielded near-perfect classification (Matthews correlation coefficient ˜ 1), suggesting strong practical operability for field screening. Additionally, field-analog tests highlighted the role of endmembers—accuracy declined without access to end-member measurements but was largely recovered by collecting a minimal labeled pair for local recalibration. With end members, accuracy remained high. Globally trained models performed well on three operational unknowns; however, series-specific refits provided the most accurate predictions. Overall, these results highlight the potential of combining rapid geophysics and minimal local calibration for improved coal-ash delineation.

Peshtani, Klaudio↗
DOE OSTI2023

Geophysical signatures of soil AFFF contamination from spectral induced polarization and low field nuclear magnetic resonance methods

Few field methods are available for characterizing source zones contaminated with aqueous film forming foam (AFFF). Non-invasive geophysical characterization of AFFF source zone contamination in situ could assist with the delineation and characterization of these sites, allowing for more informed sampling regimes aimed at quantifying subsurface poly- and perfluoroalkyl substances (PFAS) contamination. In this study, we present initial results from the investigation of the sensitivity of two existing surface and borehole-deployable geophysical technologies, spectral induced polarization (SIP), and low field nuclear magnetic resonance (NMR), to soils impacted with AFFF. To investigate the sensitivity of these methods to AFFF-impacted soil, bench-scale column experiments were conducted on samples consisting of natural and synthetic soils and groundwater. While our findings do not show strong evidence of NMR sensitivity to soil PFAS contamination, we do find evidence that SIP has sufficient sensitivity to detect PFAS sorption to soils. This finding is based on evidence that AFFF constituents associated with the pore surface produce a measurable polarization response in both freshly contaminated synthetic soils and in soils historically impacted with AFFF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Achievements and Prospects of Global Broadband Seismographic Networks After 30 Years of Continuous Geophysical Observations

Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid- to late-twentieth century saw the creation of the World-Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global geographic coverage as seismometer bandwidth increased greatly allowing for the recording of the Earth's principal seismic spectrum. The modern era of global observations and rapid data access began during the 1980s, and notably included the inception of the GEOSCOPE initiative (1982) and GSN (1988). Through continual improvements, GEOSCOPE and the GSN have realized near-real time recording of ground motion with state-of-art data quality, dynamic range, and timing precision to encompass 180 seismic stations, many in very remote locations. Data from GSNs are increasingly integrated with other geophysical data (e.g., space geodesy, infrasound and Interferometric Synthetic Aperture Radar). Globally distributed seismic data are critical to resolving crust, mantle, and core structure; illuminating features of the plate tectonic and mantle convection system; rapid characterization of earthquakes; identification of potential tsunamis; global nuclear test verification; and provide sensitive proxies for environmental changes. As the global geosciences community continues to advance our understanding of Earth structure and processes controlling elastic wave propagation, GSN infrastructure offers a springboard to realize increasingly multi-instrument geophysical observatories. Here, we review the historical, scientific, and monitoring heritage of GSNs, summarize key discoveries, and discuss future associated opportunities for Earth Science.

58 GEOSCIENCES↗
DOE OSTI2022

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

58 GEOSCIENCES↗
DOE OSTI2022

Deep Learning At Depth: Estimating subsurface parameters from geophysical monitoring data

Geophysical imaging techniques are a non-invasive way to image the subsurface and understand both subsurface solid (rock/soil) and fluid property distributions and their evolution in time. Inversions of the geophysical data, such as Electrical Resistance Tomography (ERT) data, are solved to estimate the subsurface property distributions, such as conductivity, and many inversion techniques smooth out sharp gradients in rock or fluid property distributions. Sharp gradients in subsurface properties tend to be present in situations with complex subsurface structures, which are common in many subsurface applications. We have successfully demonstrated that it is possible to inform, or constrain, inversions with neural networks trained on synthetic data with complex subsurface structures. Initial results suggest this process may be optimizable to yield property distributions that better represent the true property distributions than the same inversion process without the neural network constraint. Future work would optimize the neural network performance for this application and then apply the synthetic-data trained neural network to real data to understand the utility and performance of this technique for real data sets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Geophysical Observations of the 2023 September 24 OSIRIS-REx Sample Return Capsule Reentry

Sample return capsules (SRCs) entering Earth's atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 2023 September 24 arrival of the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing, and Global Positioning System technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the data set collected during this campaign will improve entry and propagation models and augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Geophysical and Environmental Monitoring Data, Lower Watershed, Teller Road Mile Marker 27, Seward Peninsula, Alaska, 2017-2019

This data set contains geophysical and environmental monitoring data acquired between September 2017 and 2019 at the lower Teller watershed, Seward Peninsula, Alaska. Geophysical data comprises processed resistivity data, acquired daily between Spring and Fall of 2018 and 2019, and a baseline measurement of Fall 2017. The environmental monitoring data comprises depth resolved, distributed soil moisture and soil temperature data. These measurements were obtained used Decagon 5TE sensors placed at 0.1 m, 0.2 m , 0.3 m, and 0.4 m depth. In addition, temperature data at 0.5 m, 1.0 m, and 1.5 m were acquired using Hobo temperature sensors. Data were collected to improve our understanding of the hydrological response of discontinuous permafrost systems, in particular focusing on multi-annual dynamics and short term disturbances, such as snowmelt or precipitation events. The electrical resistivity tomography (ERT) monitoring data are included as processed resistivity models, with model cells x dimension equal to the distance along the profile, and model z dimension being elevation. The start and end point of the transect are (UTM Zone 3N): E 454881.66 m, N 7178949.45 m, and E 454772.60, N 7178885.81. Locations of the soil moisture and temperature sensors are provided in the data package. This dataset is discussed in detail in the Uhlemann, S. et al 2021 paper listed in the references. This dataset includes one *.pdf user guide and 315 *.csv data files included within four zipped files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗