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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion

Partial differential equation (PDE)-governed inverse problems are fundamental across various scientific and engineering applications; yet they face significant challenges due to nonlinearity, ill-posedness, and sensitivity to noise. Here, we introduce a computational framework, regularization by denoising using diffusion models for partial differential equations (RED-DiffEq), by integrating physics-driven inversion and data-driven learning. RED-DiffEq leverages pretrained diffusion models as a regularization mechanism for PDE-governed inverse problems. We apply RED-DiffEq to solve the full waveform inversion problem in geophysics, a challenging seismic imaging technique that seeks to reconstruct high-resolution subsurface velocity models from seismic measurement data. Our method shows enhanced accuracy and robustness compared to benchmark methods. Additionally, it exhibits strong generalization and domain decomposition capacity, enabling the inversion of more complex velocity models with larger domains than those used in training the diffusion model. Our framework can also be directly applied to diverse PDE-governed inverse problems.

Shan, Siming [Yale University, New Haven, CT (Unit↗
DOE OSTI2020

Seismotectonic evidence for subduction beneath the Eastern Greater Caucasus

SUMMARY We generated high-resolution 3-D seismic velocity models as well as a relocated earthquake catalogue across the eastern Greater Caucasus and Kura basins. This work was done using data from the recently upgraded Republic Seismological Survey Center's (RSSC) seismic network. We generated our tomographic images of crustal velocity structure in Azerbaijan using double-difference inversions (i.e. tomoDD and hypoDD). Earthquake catalogues from the RSSC between 2011 and 2016 were used; these catalogues include absolute arrival times of 103 288 P- and 120 952 S-wave traveltime picks for 7574 events recorded at 35 stations in Azerbaijan. Beginning with a layered, 1-D velocity model that was estimated using VELEST, we inverted simultaneously for relative location, Vp and Vs on a 3-D grid with dimensions 670 × 445 × 45 km, with a uniform grid spacing of 55 × 55 × 5 km for all of Azerbaijan. We observe that the relocated hypocentres cluster into two depth ranges, at the surface and at depth, that appear to correspond to major fault zones and the top of a subducting plate. Additionally, we note intermediate depth seismicity (∼50–60 km) beneath the Kura Basin, and a northward deepening of earthquake depths. Seismic velocities vary significantly throughout the study region; we observe very slow velocities throughout the Kura Basin between 5 and 15 km, and elevated velocities at 20–35 km. The wholesale velocity structure and seismic structure of Kura Basin strongly mirrors that of the Caspian Sea, which suggests that the geodynamics of the Caspian continue westwards into Azerbaijan. The key results of this study suggest that the northward subduction observed in the Caspian Sea continues beneath the Eastern Greater Caucasus, as well as provides evidence for active faulting along the southern margin of the mountain range.

Gunnels, Michael↗
NASA NTRS1996

A Velocity Distribution Model for Steady State Heat Transfer

Consider a box that is filled with an ideal gas and that is aligned along Cartesian coordinates (x, y, z) having until length in the 'y' direction and unspecified length in the 'x' and 'z' directions. Heat is applied uniformly over the 'hot' end of the box (y = 1) and is removed uniformly over the 'cold' end (y = O) at a constant rate such that the ends of the box are maintained at temperatures T(sub 0) at y = O and T(sub 1) at y = 1. Let U, V, and W denote the respective velocity components of a molecule inside the box selected at some random time and at some location (x, y, z). If T(sub 0) = T(sub 1), then U, Y, and W are mutually independent and Gaussian, each with mean zero and variance RT(sub 0), where R is the gas constant. When T(sub 0) does not equal T(sub 1) the velocity components are not independent and are not Gaussian. Our objective is to characterize the joint distribution of the velocity components U, Y, and W as a function of y, and, in particular, to characterize the distribution of V given y. It is hoped that this research will lead to an increased physical understanding of the nature of turbulence.

Hall, Eric B.↗
NASA NTRS1993

Mass loading and velocity diffusion models for heavy pickup ions at comet Grigg-Skjellerup

We compare model predictions of cometary water group ion densities and the solar wind slow down with measurements made by the Giotto Johnstone plasma analyzer implanted ion sensor at the encounter with comet Grigg-Skjellerup (G-S) on July 10, 1992. The observed slope of the ion density profile on approach to the comet is unexpectedly steep. Possible explanations for this are discussed. We present also a preliminary investigation of the quasilinear velocity-space diffusion of the implanted heavy ion population at G-S using a transport equation including souce, convection, adiabatic compression, and velocity diffusion terms. Resulting distributions are anisotropic, in agreement with observations. We consider theoretically the waves that may be generated by the diffusion process for the observed solar wind conditions. At initial ion injections, waves are generated at omega approximately Omega(sub i) the ion gyrofrequency, and lower frequencies are predicted for diffusion toward a bispherical shell.

Huddleston, D. E.↗
DOE OSTI2021

Revisiting AMOC Transport Estimates From Observations and Models

Abstract Reference level assumptions used to calculate the Atlantic meridional overturning circulation transports at the RAPID and Meridional Overturning Variability Experiment (MOVE) observing arrays are revisited in an eddying ocean model. Observational transport calculation methods are complemented by several alternative approaches. At RAPID, the model transports from the observational method and the model truth (based on the actual model velocities) agree well in their mean and variability. There are substantial differences among the transport estimates obtained with various methods at the MOVE site. These differences result from relatively large and time‐varying reference velocities at depth in the model, not supporting a level‐of‐no‐motion. The methods that account for these reference velocities properly at MOVE produce transports that are in good agreement with the model truth. In contrast with the observational estimates, the model transport trends at MOVE and RAPID largely agree with each other on pentadal to multi‐decadal time scales.

Danabasoglu, Gokhan↗
NASA NTRS1974

Constraints on lunar structure

A brief review is given of the constraints placed on lunar structure and composition by seismic data and density models. Bounds on the crustal velocity structure in Mare Cognitum are derived using travel-time data from artificial impacts, and a velocity model is determined on the basis of synthetic seismograms. It is shown that the velocities of P- and S-waves in the mantle can be fixed by a least-squares analysis of arrival times from meteor impacts and moonquakes, and that lunar density can be determined from the seismic structure, mean density, and moment of inertia. Olivine-pyroxene mixtures and certain olivine-rich compositions are found to be consistent with the seismic-velocity and density limits. Maximum radii are calculated for pure Fe and pure FeS cores, and it is concluded that the possibility of an ancient lunar magnetic dynamo may have to be reevaluated in the light of these figures.

Dainty, A. M.↗
DOE OSTI2022

An End-To-End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning

Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. Here, in this study, we introduce a new end-to-end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi-station processing of seismic waveforms recorded over a seismic network. This end-to-end architecture consists of three sub-networks: a backbone network that extracts features from raw waveforms, a phase picking sub-network that picks P- and S-wave arrivals based on these features, and an event detection sub-network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub-networks together with a shift-and-stack module based on back-projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end-to-end approach can effectively pick P- and S-wave arrivals and achieve earthquake detection accuracy rivaling that of other state-of-the-art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.

58 GEOSCIENCES↗
NASA NTRS1994

An error covariance model for sea surface topography and velocity derived from TOPEX/POSEIDON altimetry

In order to facilitate the use of satellite-derived sea surface topography and velocity oceanographic models, methodology is presented for deriving the total error covariance and its geographic distribution from TOPEX/POSEIDON measurements. The model is formulated using a parametric model fit to the altimeter range observations. The topography and velocity modeled with spherical harmonic expansions whose coefficients are found through optimal adjustment to the altimeter range residuals using Bayesian statistics. All other parameters, including the orbit, geoid, surface models, and range corrections are provided as unadjusted parameters. The maximum likelihood estimates and errors are derived from the probability density function of the altimeter range residuals conditioned with a priori information. Estimates of model errors for the unadjusted parameters are obtained from the TOPEX/POSEIDON postlaunch verification results and the error covariances for the orbit and the geoid, except for the ocean tides. The error in the ocean tides is modeled, first, as the difference between two global tide models and, second, as the correction to the present tide model, the correction derived from the TOPEX/POSEIDON data. A formal error covariance propagation scheme is used to derive the total error. Our global total error estimate for the TOPEX/POSEIDON topography relative to the geoid for one 10-day period is found tio be 11 cm RMS. When the error in the geoid is removed, thereby providing an estimate of the time dependent error, the uncertainty in the topography is 3.5 cm root mean square (RMS). This level of accuracy is consistent with direct comparisons of TOPEX/POSEIDON altimeter heights with tide gauge measurements at 28 stations. In addition, the error correlation length scales are derived globally in both east-west and north-south directions, which should prove useful for data assimilation. The largest error correlation length scales are found in the tropics. Errors in the velocity field are smallest in midlatitude regions. For both variables the largest errors caused by uncertainty in the geoid. More accurate representations of the geoid await a dedicated geopotential satellite mission. Substantial improvements in the accuracy of ocean tide models are expected in the very near future from research with TOPEX/POSEIDON data.

Tsaoussi, Lucia S.↗
DOE OSTI2020

Effects of Shallow-Velocity Reductions on 3D Propagation of Seismic Waves

Abstract We perform 3D simulations of seismic wavefields to clarify effects of strong reductions of shallow velocities on long-period seismic waves. The simulations use a reference Community Velocity Model of southern California and a modified version with strong velocity reductions in the top 500 m of the Los Angeles basin. Differences between wavefields generated by 10 earthquakes in the reference and perturbed models are analyzed. Velocity changes are estimated by measuring relative time shifts between reference and perturbed seismograms using wavelet cross-correlation spectra. The results indicate that strong localized temporal velocity drops near the surface, such as those observed during strong ground motions, may generate regional perturbations of wavefields at periods up to 20 s. These perturbations may be misinterpreted as generated by temporal changes at seismogenic depths. The results also have important implications for waveform tomography studies.

Geochemistry & Geophysics↗
DOE OSTI2022

Early arrival waveform inversion using data uncertainties and matching filters with application to near-surface seismic refraction data

We develop an early arrival waveform inversion (EAWI) technique for high-resolution near-surface velocity estimation by iteratively updating the P-wave velocity model to minimize the difference between the observed and calculated seismic refraction data. Traditional EAWI uses a least-squares penalty function and an acoustic forward-modeling engine. Conventional least-squares error is sensitive to data with low signal-to-noise ratio (S/N) and iterations of EAWI stop at a local-minimum data misfit or at the preassigned maximum number of iterations. These stopping criteria can result in overfitting the data. In addition, fitting the elastic field data with an acoustic modeling engine can introduce artifacts in velocity estimation, especially in land data with significant elastic effects. To overcome these challenges, we develop a robust EAWI (REAWI) method by (1) incorporating the data uncertainties into the penalty function and (2) mitigating the elastic effects using a matching filter workflow. The data uncertainties are estimated from waveform reciprocal errors. When full-waveform reciprocity is not available, trace interpolation is applied. The proposed method prevents closely fitting data with low S/N, avoids overall overfitting by stopping the iterations when a normalized chi-square ([Formula: see text]) waveform misfit of one is achieved, and is less affected by elastic effects. Numerical examples and application to near-surface refraction data at a groundwater contamination site suggest that the final REAWI models are more accurate than the corresponding EAWI models, at the same level of misfit. This is the first known application of a matching filter workflow to real land data. The final REAWI models satisfy an appropriate misfit between the real data and predicted elastic P-wave data, making this approach in this respect equivalent to elastic waveform inversion. We also develop a method to analyze model constraint by examining the energy of the wavefield Fréchet derivative thereby avoiding the influence of the data residuals in traditional Fréchet kernels.

Geochemistry & Geophysics↗
NASA NTRS1993

The role of the margins in ice stream dynamics

At first glance, it would appear that the bed of the active ice stream plays a much more important role in the overall force balance than do the margins, especially because the ratio of the half-width to depth for a typical ice stream is large (15:1 to 50:1). On the other hand, recent observations indicate that at least part of the ice stream is underlain by a layer of very weak till (shear strength about 2 kPa), and this weak basal layer would then imply that some or all of the resistive drag is transferred to the margins. In order to address this question, a detailed velocity profile near Upstream B Camp, which extends from the center of the ice stream, across the chaotic shear margin, and onto the Unicorn, which is part of the slow-moving ice sheet was measured. Comparison of this observed velocity profile with finite-element models of flow shows several interesting features. First, the shear stress at the margin is on the order of 130 kPa, while the mean value along the bed is about 15 kPa. Integration of these stresses along the boundaries indicates that the margins provide 40 to 50 percent, and the bed, 60 to 40 percent of the total resistive drag needed to balance the gravitational driving stress in this region. (The range of values represents calculations for different values of surface slope.) Second, the mean basal stress predicted by the models shows that the entire bed cannot be blanketed by the weak till observed beneath upstream B - instead there must be a distribution of weak till and 'sticky spots' (e.g., 85 percent till and 15 percent sticky spots of resistive stress equal to 100 kPa). If more of the bed were composed of weak till, then the modeled velocity would not match that observed. Third, the ice must exhibit an increasing enhancement factor as the margins are approached (E equals 10 in the chaotic zone), in keeping with laboratory measurements on ice under prolonged shear strain. Also, there is either a narrow zone of somewhat stiffer ice (E equals 5) outward of the shear margin, or the bed is frozen there. And last, the high shear stress and strain rate found at the margin are likely to cause significant viscous heating (q) in the marginal ice. The increase in temperature is proportional to qX/u, where X is the width of the shear zone and u is the transverse velocity component bringing cold ice in from the ice sheet outside the shear zone. Near upstream B, this heating is likely to cause an increase in temperature of 4 to 10 K. Plans are to measure this temperature increase in a series of bore holes near the margin during the 1992-93 field season, as well as to provide a more detailed description of the velocity field there.

Echelmeyer, Keith↗
NASA NTRS1996

A method for landing gear modeling and simulation with experimental validation

This document presents an approach for modeling and simulating landing gear systems. Specifically, a nonlinear model of an A-6 Intruder Main Gear is developed, simulated, and validated against static and dynamic test data. This model includes nonlinear effects such as a polytropic gas model, velocity squared damping, a geometry governed model for the discharge coefficients, stick-slip friction effects and a nonlinear tire spring and damping model. An Adams-Moulton predictor corrector was used to integrate the equations of motion until a discontinuity caused by a stick-slip friction model was reached, at which point, a Runga-Kutta routine integrated past the discontinuity and returned the problem solution back to the predictor corrector. Run times of this software are around 2 mins. per 1 sec. of simulation under dynamic circumstances. To validate the model, engineers at the Aircraft Landing Dynamics facilities at NASA Langley Research Center installed one A-6 main gear on a drop carriage and used a hydraulic shaker table to provide simulated runway inputs to the gear. Model parameters were tuned to produce excellent agreement for many cases.

Daniels, James N.↗
DOE OSTI2020

Final Report for DTRA Project "High-resolution Velocity and Attenuation Model for Eastern Mediterranean/Mideast Region"

The overall goal of this project is to build a high-resolution velocity and attenuation model for a region spanning from the eastern Mediterranean Sea and extending eastward into the Middle East. Phase I of this project, covered in this report, focuses on the analysis of the SHDAS array, updated dispersion and amplitude measurements, development of surface wave group velocity models, and construction of a preliminary 1-degree, velocity-only model for the study region. These will be discussed in turn.

58 GEOSCIENCES↗
DOE OSTI2023

DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Structure of the Ecuadorian Upper Plate From a Joint Seismic‐Gravity Inversion

The Ecuadorian portion of the South American subduction zone presents an interesting case study in the structure and complex evolution of an upper plate. There are outstanding questions about its tectonic history, composition, and magmatic processes. While previous studies have employed ambient noise tomography to image the Ecuadorian upper plate, surface wave inversions alone often lack sensitivity at relevant shallow depths. This limitation can be overcome with an independent, complementary data set, such as gravity. We have jointly inverted Rayleigh wave phase velocities and Bouguer gravity anomalies to provide a more detailed seismic velocity model of the Ecuadorian upper plate. Our joint inversion has yielded several key improvements from previous models. First, we observe much shallower slow velocities beneath major basins (the Manabí, Progreso, and Gulf of Guayaquil), better aligning with expected basin structure. Second, we identify a high-velocity block beneath the entire forearc, corresponding to the Piñon Terrane, with velocities suggesting the presence of ultramafic material. Third, we highlight a new narrow swath of slow velocities beneath the Ecuadorian Andes, which closely follows the active volcanoes along the Eastern Cordillera. The extent of these slow velocities coincides with the termination of active arc volcanism and the predicted location of the subducted Carnegie Ridge. The predicted compositions for the mid to lower crust in the region preclude a purely compositional explanation for these velocities, suggesting that some level of partial melt is necessary.

Birkey, Andrew [Univ. of Delaware, Newark, DE (Uni↗
DOE OSTI2025

Utah FORGE: Seismic DAS and Geophone Borehole Data Processing and 3D Imaging of Vp/Vs Ratio in the 2024 Stimulated Reservoir

This dataset includes a final report and a 3D velocity model derived from seismic DAS and geophone borehole data collected during the April 2024 stimulation of the reservoir at Utah FORGE. The report details the processing of over 50,000 P- and S-wave travel times used in a tomographic inversion to estimate the Vp/Vs ratio, revealing anomalies adjacent to wells 16A and 16B that may be associated with injected fracturing fluids. Additionally, Wadati analysis of more than 27,000 pairs of differential P- and S-wave travel times supports this interpretation with a high Vp/Vs estimate of 1.86, averaged over the dimensions of the earthquake cluster. The accompanying data file provides a 3D model of P- and S-wave velocities and Vp/Vs ratios, structured with spatial coordinates and velocity values.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗