Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Attribution”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in situ and remote sensing data sets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

Cheng, Yanyan↗
DOE OSTI2016

YSZ thin films with minimized grain boundary resistivity

In recent years, interface engineering of solid electrolytes has been explored to increase their ionic conductivity and improve the performance of solid oxide fuel cells and other electrochemical power sources. It has been observed that the ionic conductivity of epitaxially grown thin films of some electrolytes is dramatically enhanced, which is often attributed to effects (e.g. strain-induced mobility changes) at the heterophase boundary with the substrate. Still largely unexplored is the possibility of manipulation of grain boundary resistivity in polycrystalline solid electrolyte films, clearly a limiting factor in their ionic conductivity. Here in this paper, we report that the ionic conductivity of yttria stabilized zirconia thin films with nano-columnar grains grown on a MgO substrate nearly reaches that of the corresponding single crystal when the thickness of the films becomes less than roughly 8 nm (smaller by a factor of three at 500 °C). Using impedance spectroscopy, the grain boundary resistivity was probed as a function of film thickness. The resistivity of the grain boundaries near the film–substrate interface and film surface (within 4 nm of each) was almost entirely eliminated. This minimization of grain boundary resistivity is attributed to Mg 2+ diffusion from the MgO substrate into the YSZ grain boundaries, which is supported by time of flight secondary ion mass spectroscopy measurements. We suggest grain boundary “design” as an attractive method to obtain highly conductive solid electrolyte thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Organometallic model complexes elucidate the active gallium species in alkane dehydrogenation catalysts based on ligand effects in Ga K-edge XANES

Gallium-modified zeolites are known catalysts for the dehydrogenation of alkanes, reactivity that finds industrial application in the aromatization of light alkanes by Ga-ZSM5. While the role of gallium cations in alkane activation is well known, the oxidation state and coordination environment of gallium under reaction conditions has been the subject of debate. Edge shifts in Ga K-edge XANES spectra acquired under reaction conditions have long been interpreted as evidence for reduction of Ga(III) to Ga(I). However, a change in oxidation state is not the only factor that can give rise to a change in the XANES spectrum. In order to better understand the XANES spectra of working catalysts, we have synthesized a series of molecular model compounds and grafted surface organometallic Ga species and compared their XANES spectra to those of gallium-based catalysts acquired under reducing conditions. We demonstrate that changes in the identity and number of gallium nearest neighbors can give rise to changes in XANES spectra similar to those attributed in literature to changes in oxidation state. Specifically, spectral features previously attributed to Ga(I) may be equally well interpreted as evidence for low-coordinate Ga(III) alkyl or hydride species. Furthermore, these findings apply both to gallium-impregnated zeolite catalysts and to silica-supported single site gallium catalysts, the latter of which is found to be active and selective for dehydrogenation of propane and hydrogenation of propylene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Magnetic iron oxide and manganese-doped iron oxide nanoparticles for the collection of alpha-emitting radionuclides from aqueous solutions

Magnetic nanoparticles are well known to possess chemically active surfaces and high surface areas that can be employed to extract a range of ions from aqueous solutions. Additionally, their paramagnetic property provides a convenient means for bulk collection of the material from solution after the targeted ions have been adsorbed. Herein, two nanoscale amphoteric metal oxides, each possessing useful magnetic attributes, were evaluated for their ability to collect both naturally occurring radioactive isotopes (polonium (Po), radium (Ra), and uranium (U)) as well as the transuranic element americium (Am) from a suite of naturally occurring aqueous matrices. The nanomaterials include commercially available paramagnetic magnetite (Fe3O4) and magnetite that was modified to incorporate manganese (Mn) into the crystal structure. The chemical stability of these nanomaterials was evaluated in Hanford Site, WA ground water between the natural pH (~8) and pH 1 (acidified with HCl). Whereas the magnetite was observed to have good stability over the pH range, the Mn-doped material was observed to leach Mn at low pH. The materials were evaluated in parallel to characterize their uptake performance of the aforementioned alpha-emitting radionuclide spikes from Hanford Site ground water across a range of pH (from ~8 down to 2). In addition, radiotracer uptake experiments were performed on Columbia River water, seawater, and human urine at their natural pH and at pH 2. Despite the observed leaching of Mn from the Mn-doped nanomaterial in the lower pH range, it exhibited generally superior analyte extraction performance compared to the magnetite, and analyte uptake was observed across a broader pH range. The uptake behavior of the various radiotracers on these two materials at different pH levels can generally be explained by the amphoteric nature of the nanoparticle surfaces. Finally, the rate of sorption of the radiotracers on the two materials in unacidified groundwater was evaluated. The uptake curves generally indicate that equilibrium is obtained within a few minutes, which is attributed to the high surface areas of the nanomaterials and the high level of dispersion in the liquids. Overall, the results indicate that these nanomaterials may have the potential to be employed for a range of applications to extract radionuclides from aqueous solutions. These applications may include analytical chemistry, waste water treatment and remediation, mining, and in vitro radiobioassay.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Computational design of CO-tolerant Pt 3 M anode electrocatalysts for proton-exchange membrane fuel cells

CO is a typical contaminant in hydrogen fuel produced via steam reforming. Development of a CO-tolerant Pt-based anode for the proton-exchange membrane fuel cells (PEMFC) is of great interest to avoid catalyst deactivation caused by strong CO adsorption on Pt. Pt–Ru, Pt–Mo, and Pt 3 Sn are three known Pt-based bimetallic CO-tolerant anode materials. The enhanced CO-tolerance of these alloys has been generally attributed to a bifunctional effect enabling oxidative CO removal at low applied potential and a ligand effect between the two metals which weakens CO bonding and reduces CO surface coverage. In this study, we use density functional theory to calculate surface adsorption states under reaction conditions for Pt-based alloys. Our calculations provide a molecular-level understanding of the enhanced CO-tolerance of Pt alloys induced by the second metal. We find that Mo and Sn dopants promote CO-tolerance by reducing CO surface coverage and enabling CO oxidative removal at low applied potential. In contrast, Ru is not able to promote CO electro-oxidation at the low applied potential of the operating conditions of a PEMFC anode. The CO-tolerance induced by Ru is attributed to the reduction of CO coverage on Pt sites. Based on these mechanisms, we carried out a computational screening of Pt 3 M electrocatalysts for CO-tolerant PEMFC anodes. A number of promising candidates have been identified for experimental examination.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Enhanced quantum efficiency from hybrid cesium halide/copper photocathodes

The quantum efficiency (QE) of Cu is found to increase dramatically when coated by a CsI film and then irradiated by a UV laser. Over three orders of magnitude quantum efficiency enhancement at 266 nm is observed in CsI/Cu(100), indicating potential application in future photocathode devices. Upon laser irradiation, a large work function reduction to a value less than 2 eV is also observed, significantly greater than for similarly treated CsBr/Cu(100). The initial QE enhancement, prior to laser irradiation, is attributed to interface interaction and the intrinsic properties of the Cs halide film. Further QE enhancement following activation is attributed to formation of inter-band states and Cs metal accumulation at the interface induced by laser irradiation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Structural instability of the CoO 4 tetrahedral chain in SrCoO 3-δ thin films

Raman scattering experiments together with detailed lattice dynamic calculations are performed to elucidate crystallographic and electronic peculiarities of SrCoO 3-δ films. We observe that the 85 cm -1 phonon mode involving the rotation of a CoO 4 tetrahedron undergoes a hardening by 21 cm -1 when the temperature is decreased. In addition, new phonon modes appear at 651.5 and 697.6 cm -1 . The latter modes are attributed to the Jahn-Teller activated modes. Upon cooling from room temperature, all phonons exhibit an exponential-like increase of intensity with a characteristic energy of about 103–107 K. We attribute this phenomenon to an instability of the CoO 4 tetrahedral chain structure, which constitutes a key ingredient to understand the electronic and structural properties of the brownmillerite SrCoO 2.5 .

36 MATERIALS SCIENCE↗
DOE OSTI2016

The magnetic ordering in high magnetoresistance Mn-doped ZnO thin films

Here, we studied the nature of magnetic ordering in Mn-doped ZnO thin films that exhibited ferromagnetism at 300 K and superparamagnetism at 5 K. We directly inter-related the magnetisation and magnetoresistance by invoking the polaronpercolation theory and variable range of hopping conduction below the metal-to-insulator transition. By obtaining a qualitative agreement between these two models, we attribute the ferromagnetism to the s-d exchange-induced spin splitting that was indicated by large positive magnetoresistance (~40 %). Low temperature superparamagnetism was attributed to the localization of carriers and non-interacting polaron clusters. This analysis can assist in understanding the presence or absence of ferromagnetism in doped/un-doped ZnO.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Outer membrane vesicles catabolize lignin-derived aromatic compounds in Pseudomonas putida KT2440

Lignin is an abundant and recalcitrant component of plant cell walls. While lignin degradation in nature is typically attributed to fungi, growing evidence suggests that bacteria also catabolize this complex biopolymer. However, the spatiotemporal mechanisms for lignin catabolism remain unclear. Improved understanding of this biological process would aid in our collective knowledge of both carbon cycling and microbial strategies to valorize lignin to value-added compounds. Here, we examine lignin modifications and the exoproteome of three aromatic–catabolic bacteria: Pseudomonas putida KT2440, Rhodoccocus jostii RHA1, and Amycolatopsis sp. ATCC 39116. P. putida cultivation in lignin-rich media is characterized by an abundant exoproteome that is dynamically and selectively packaged into outer membrane vesicles (OMVs). Interestingly, many enzymes known to exhibit activity toward lignin-derived aromatic compounds are enriched in OMVs from early to late stationary phase, corresponding to the shift from bioavailable carbon to oligomeric lignin as a carbon source. In vivo and in vitro experiments demonstrate that enzymes contained in the OMVs are active and catabolize aromatic compounds. Taken together, this work supports OMV-mediated catabolism of lignin-derived aromatic compounds as an extracellular strategy for nutrient acquisition by soil bacteria and suggests that OMVs could pote

biological funneling↗
DOE OSTI2015

THE SPECTRUM OF ISOTROPIC DIFFUSE GAMMA-RAY EMISSION BETWEEN 100 MeV AND 820 GeV

The γ-ray sky can be decomposed into individually detected sources, diffuse emission attributed to the interactions of Galactic cosmic rays with gas and radiation fields, and a residual all-sky emission component commonly called the isotropic diffuse γ-ray background (IGRB). The IGRB comprises all extragalactic emissions too faint or too diffuse to be resolved in a given survey, as well as any residual Galactic foregrounds that are approximately isotropic. The first IGRB measurement with the Large Area Telescope (LAT) on board the Fermi Gamma-ray Space Telescope (Fermi) used 10 months of sky-survey data and considered an energy range between 200 MeV and 100 GeV. Improvements in event selection and characterization of cosmic-ray backgrounds, better understanding of the diffuse Galactic emission (DGE), and a longer data accumulation of 50 months allow for a refinement and extension of the IGRB measurement with the LAT, now covering the energy range from 100 MeV to 820 GeV. The IGRB spectrum shows a significant high-energy cutoff feature and can be well described over nearly four decades in energy by a power law with exponential cutoff having a spectral index of 2.32 ± 0.02 and a break energy of (279 ± 52) GeV using our baseline DGE model. The total intensity attributed to the IGRB is (7.2 ± 0.6) × 10{sup –6} cm{sup –2} s{sup –1} sr{sup –1} above 100 MeV, with an additional +15%/–30% systematic uncertainty due to the Galactic diffuse foregrounds.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data

Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global long short-term memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-vision transformer (ViT)-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a ViT architecture. Applied to 531 basins across the Contiguous United States, our method demonstrated superior prediction accuracy in both temporal and spatiotemporal extrapolation scenarios. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for better understanding the environment responses to climate change.

Tayal, Kshitij↗
DOE OSTI2021

How different power plant types contribute to electric grid reliability, resilience, and vulnerability: a comparative analytical framework

Abstract This work explores the dependability tradeoffs provided by the most common types of central power plants in the United States. Historically, the electricity sector has lacked consensus on how reliability , resilience , and vulnerability differ and how those metrics change depending on the power plant fleet composition. We propose distinct definitions for these metrics and an analytical framework to evaluate power plant fleet dependability. Using data analysis and literature review, we identify fifteen dependability attributes across which we rank eleven power plant types relative to natural gas combined-cycle (NGCC) plants. We use NGCC as the benchmark because it is common to many locations and is of relatively recent vintage. The framework shows that each power plant type has unique dependability benefits and drawbacks. We provide examples of how researchers may use the framework to evaluate grid dependability qualitatively under different scenarios. We find that assuming all attributes that contribute to grid dependability are equally important and additive, electric grid dependability is best supported when power plant fleets include a mixture of power generation technologies. Then, we discuss scenario characteristics that could alter the prioritization and relationships of attributes. We also find that if current capacity installation trends continue to favor low- and zero-carbon power plants, US power grids may benefit from increased resilience and reduced vulnerability at the cost of decreased reliability. We conclude by recommending methods for adapting the framework and quantifying relationships between attributes in individual scenarios.

Ramirez-Meyers, K. (ORCID:0000000291216952)↗
DOE OSTI2023

Laboratory investigation of hydraulic fracturing in granitic rocks using active and passive seismic monitoring

SUMMARY Knowledge of the fracturing processes can be important for the optimization of pressurized fluid injection operations in the deep underground rock mass. Active and passive seismic monitoring techniques have been used in the field for tracking or mapping the propagating hydraulic fracture. Although both these monitoring techniques provide valuable information about the generated fracture network, it is difficult for either technique to comprehensibly identify the different processes associated with hydraulic fracturing. The combined active and passive monitoring has the potential for better characterization of the complex hydraulic fracturing phenomena. In this study, laboratory hydraulic fracturing experiments with combined active and passive seismic monitoring were conducted on true triaxially loaded Barre granite cubes with different fluid injection rates. The seismic inelastic fracturing was detected by 16 passive acoustic emission sensors, where 3678 and 2370 seismic source events were detected for the high and low injection rate experiments, respectively. For active monitoring, strong variations in the attributes of signals were observed which were transmitted through four source–receiver pairs, placed both perpendicular and parallel to the generated hydraulic fracture. Positive velocity changes were observed for active sensor pairs with ray paths passing through the generated hydraulic fracture indicating fluid permeation, whereas isolated dry deformation was characterized by a slight but permanent velocity decrease. Compared to velocity, the energy of the active signals was 1–2 orders of magnitude more sensitive to different hydraulic fracturing processes. However, the sensitivity and signatures of the active signal attributes were found to be dependent on the frequency range and direction of ray path with respect to the location of the generated fracture network. Using the coupled evaluation of the active and passive signals we were able to systematically identify various hydraulic fracturing processes including: (1) aseismic deformation, (2) fracture initiation and fluid permeation, (3) pressure build-up, (4) fracture propagation and (5) pressure release and leak-off. The results of this study showed that combining the respective advantages of active and passive seismic techniques and using both of them to monitor the failure processes can facilitate a more comprehensive understanding and better control of the hydraulic stimulations in subsurface operations.

Geochemistry & Geophysics↗
DOE OSTI2024

Efficient graph representation framework for chemical molecule similarity tasks

Graph data has emerged in numerous scientific domains and machine learning techniques have been widely used for analysis and learning of diverse data for prediction and decision. Machine learning techniques can readily address complex problems by leveraging their structural information. But graphs cannot be directly used for existing machine learning algorithms unless encoded as vectors. The problem of efficient representation of graphs is a substantial challenge in graph machine learning. In this paper, we propose a novel two-stage framework for the representation of chemical molecule graphs based on the strengths of Graph Isomorphism Networks (GINs) and Siamese autoencoders. In the first stage, the GIN model is constructed and trained using the structural information of chemical molecule graphs. Node attributes, edge attributes, and edge indices are used as input data, while graph attributes are used as labels. The GIN model effectively captures the structural characteristics of graphs and can accurately predict graph attributes, i.e., molecular properties. It also generates Graph Embeddings, represented as vectors that encode the structural information of graphs. In the second stage, Graph Embedding vectors are further optimized for downstream similarity tasks while preserving the graph structural information. The Siamese autoencoder is constructed and trained, which reduces the dimensionality of the Graph Embedding vectors, while maximizing the preservation of structural information in the original high-dimensional vectors. The resulting low-dimensional Graph Embeddings can be effectively utilized for tasks such as approximate nearest neighbor search. The experimental results demonstrate the effectiveness of our proposed framework in accurately predicting graph similarity.

Ma, Jiaji↗
DOE OSTI2020

FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets

This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN. FeederGAN digests real feeder models represented by directed graphs via a deep learning framework powered by GAN and graph convolutional networks (GCN). Information of a distribution feeder circuit is extracted from its model input files so that the device connectivity is mapped onto the adjacency matrix and the device characteristics, such as circuit types (i.e., 3-phase, 2-phase, and 1-phase) and component attributes (e.g., length and current ratings), are mapped onto the attribute matrix. Then, Wasserstein distance is used to optimize the GAN and GCN is used to discriminate the generated graphs from the actual ones. A greedy method based on graph theory is developed to reconstruct the feeder using the generated adjacency and attribute matrices. Furthermore, our results show that the GAN generated feeders resemble the actual feeder in both topology and attributes verified by visual inspection and by empirical statistics obtained from actual distribution feeders.

42 ENGINEERING↗
DOE OSTI2015

Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT

Purpose: To investigate the importance of presurgical computed tomography (CT) intensity and texture information from ground-glass opacities (GGO) and solid nodule components for the prediction of adenocarcinoma recurrence. Methods: For this study, 101 patients with surgically resected stage I adenocarcinoma were selected. During the follow-up period, 17 patients had disease recurrence with six associated cancer-related deaths. GGO and solid tumor components were delineated on presurgical CT scans by a radiologist. Computational texture models of GGO and solid regions were built using linear combinations of steerable Riesz wavelets learned with linear support vector machines (SVMs). Unlike other traditional texture attributes, the proposed texture models are designed to encode local image scales and directions that are specific to GGO and solid tissue. The responses of the locally steered models were used as texture attributes and compared to the responses of unaligned Riesz wavelets. The texture attributes were combined with CT intensities to predict tumor recurrence and patient hazard according to disease-free survival (DFS) time. Two families of predictive models were compared: LASSO and SVMs, and their survival counterparts: Cox-LASSO and survival SVMs. Results: The best-performing predictive model of patient hazard was associated with a concordance index (C-index) of 0.81 ± 0.02 and was based on the combination of the steered models and CT intensities with survival SVMs. The same feature group and the LASSO model yielded the highest area under the receiver operating characteristic curve (AUC) of 0.8 ± 0.01 for predicting tumor recurrence, although no statistically significant difference was found when compared to using intensity features solely. For all models, the performance was found to be significantly higher when image attributes were based on the solid components solely versus using the entire tumors (p < 3.08 × 10{sup −5}). Conclusions: This study constitutes a novel perspective on how to interpret imaging information from CT examinations by suggesting that most of the information related to adenocarcinoma aggressiveness is related to the intensity and morphological properties of solid components of the tumor. The prediction of adenocarcinoma relapse was found to have low specificity but very high sensitivity. Our results could be useful in clinical practice to identify patients for which no recurrence is expected with a very high confidence using a presurgical CT scan only. It also provided an accurate estimation of the risk of recurrence after a given duration t from surgical resection (i.e., C-index = 0.81 ± 0.02)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Effects of forest degradation classification on the uncertainty of aboveground carbon estimates in the Amazon

Tropical forests are critical for the global carbon budget, yet they have been threatened by deforestation and forest degradation by fire, selective logging, and fragmentation. Existing uncertainties on land cover classification and in biomass estimates hinder accurate attribution of carbon emissions to specific forest classes. In this study, we used textural metrics derived from PlanetScope images to implement a probabilistic classification framework to identify intact, logged and burned forests in three Amazonian sites. We also estimated biomass for these forest classes using airborne lidar and compared biomass uncertainties using the lidar-derived estimates only to biomass uncertainties considering the forest degradation classification as well. Our classification approach reached overall accuracy of 0.86, with accuracy at individual sites varying from 0.69 to 0.93. Logged forests showed variable biomass changes, while burned forests showed an average carbon loss of 35%. We found that including uncertainty in forest degradation classification significantly increased uncertainty and decreased estimates of mean carbon density in two of the three test sites. Our findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Seismic response to paleo-sand dunes in the Nugget Sandstone Formation, southwestern Wyoming

We have analyzed a 3D seismic survey acquired for a carbon sequestration project on top of the Moxa Arch in southwestern Wyoming. We observed a zone of discontinuous reflectors on vertical slices of seismic amplitude volume, whereas, the northwest–southeast lineations were observed on the time slices. We performed a seismic to well tie that suggested that the lineations occur within the Nugget Sandstone. The Nugget Sandstone is an eolian sandstone deposit of Early Jurassic age, deposited as a subtropical dune field, and equivalent to the Navajo Sandstone of southwestern Utah. Petrophysical analysis indicates that the Nugget Sandstone is dominated by clean sandstone (70%–80%), whereas evaporites, including halite and anhydrite, are present in certain zones. Previous outcrop studies on the Navajo Sandstone indicate the wind direction to be predominantly northeast–southwest. Seismic attributes, including coherence and curvature, displayed on stratal slices within the Nugget Sandstone interval indicate the presence of lineations in the northwest–southeast direction with irregular spacing. These lineations are approximately perpendicular to the inferred dominant wind direction. We computed the dominant wind direction from the average azimuth of the lineations as seen on the curvature attribute in the Nugget Sandstone interval. Geological feature: Eolian sand dunes with interdunal evaporites Seismic appearance: Parallel lineations with irregular spacing on seismic attribute horizon slices Alternative interpretations: Canyons at continental slopes; slope failures Features with a similar appearance: Marine bars; contourites Formation: The Nugget Sandstone — equivalent to the Navajo Sandstone Age: Early Jurassic Location: Moxa Arch, Wyoming Seismic data: Obtained by the University of Wyoming with U.S. DOE funding Contributors: Dhruv Agrawal, Brady Lujan, Sumit Verma, Shuvajit Bhattacharya, and Subhashis Mallick Analysis tools: Coherence and curvature attributes; seismic inversion; petrophysical inversion

Geochemistry & Geophysics↗