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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Mineralogy of Layered Outcrops at Mawrth Vallis and Implications for Early Aqueous Geochemistry on Mars

Recently developed CRISM parameters and newly available DTMs are enabling refined characterization of the mineralogy at Mawrth Vallis. A stratigraphy including 5 units is mapped using HRSC DTMs across 100s of kms and using HiRISE DTMs across 100s of meters. Transitions in mineralogic units were characterized using spectral properties and surface morphology. The observations point to an ancient wet and warm geologic record that formed the thick nontronite unit, a period of wet/dry cycling to create acid alteration, followed by leaching or pedogenesis to result in Al-phyllosilicates, and finally a drier, colder climate that left the altered ash in the form of nanophase aluminosilicates, rather than crystalline clays.

Bishop, J. L.↗
NASA NTRS1975

Application of satellite photographic and MSS data to selected geologic and natural resource problems in Pennsylvania. 1: Lineaments and mineral occurrences in Pennsylvania. 2: Relation of lineaments to sulfide deposits: Bald Eagle Mountain, Centre County, Pennsylvania. 3: Comparison of Skylab and LANDSAT lineaments with joint orientations in north central Pennsylvania

Those metallic mineral occurrences in Pennsylvania are reported which lie near lineaments mapped from LANDSAT-1 satellite imagery and verified from Skylab photography where available. The lineaments were categorized by degree of expression and type of expression; the mineral occurrences were classified by host rock age, mineralization type, and value. The accompanying tables and figure document the mineral occurrences geographically associated with lineaments and serve as a base for a mineral exploration model.

Kowalik, W. S.↗
DOE OSTI2020

Comparison of four learning-based methods for predicting groundwater redox status

Knowing the location where groundwater denitrification occurs, or by proxy the groundwater redox status (oxic, mixed, and anoxic), is valuable information for assessing and managing potential agricultural land-use impacts on freshwater quality. We compare the efficacy of supervised (Linear Discriminant Analysis LDA; Boosted Regression Trees, BRT; and Random Forest, RF) and unsupervised (Modified Self-Organizing Map, MSOM) learning-based methods to predict groundwater redox status in the agriculturally dominated Tasman, Waikato, and Wellington regions of New Zealand. Thresholds applied to regional groundwater-quality samples provide redox status variables and learn heuristics constrained by these variables and applied to spatial factors (climate, elevation, geologic, hydrology soils, and well depth) identify optimal sets of regional predictor variables. A split-sample approach is used to train and test the learning methods ability to predict redox status using the optimal predictor variables. Overall, the supervised methods demonstrate a prediction bias toward oxic conditions and inability to perform statistically well when using independent regional data; for example, consider kappa statistics for BRT (Tasman: 0.42, Waikato: 0.38, Wellington: 0.17), RF (Tasman: 0.42, Waikato: 0.47, Wellington: 0.17 and LDA (Tasman: 0.46, Waikato: 0.32, Wellington: 0.17). By contrast, the unsupervised method performs statistically well when predicting oxic, mixed, and anoxic conditions and corresponding depths when using independent regional data; for example, consider MSOM kappa statistics for Tasman: 0.78, Waikato: 0.80, Wellington: 0.76. The unsupervised learning method provides the added benefits of being (1) able to combine predictions into 3D regional anoxic probability plots for interpreting the spatial influence of paleosols and groundwater flowpaths on redox status, and (2) readily extended to map 3D redox status across New Zealand and other countries despite data bias and sparsity.

machine learning algorithms, Random Forest, booste↗
NASA NTRS1981

Application of HCMM data to regional geologic analysis for mineral and energy resource evaluation

Experimentation with several potentially promising techniques led to the selection of a fairly simple scheme for registration of data from the HCMM using an affine transformation. A method based solely on remote sensing was developed to estimate those meteorological effects which are required for thermal inertia mapping. It assumes that the atmospheric fluxes are spatially invariant and that the solar, sky and sensible heat fluxes can be approximated by a simple mathematical form. Coefficients are determined from least squares method by fitting observational data to the thermal model.

Watson, K.↗
NASA NTRS1987

The geology of Pine and Crater Buttes: Two basaltic constructs on the far eastern Snake River Plain

The emplacement history and petrochemical evolution of the volcanics associated with Pine Butte, Crater Butte, and other nearby vents are developed and described. Four major vents were identified in the study area and their associated eruptive products were mapped. All of the vents show a marked physical elongation or linear orientation coincident with the observed rift set. Planetary exploration has revealed the importance of volcanic processes in the genesis and modification of extraterrestrial surfaces. Interpretation of surface features has identified plains-type basaltic volcanism in various mare regions of the Moon and the volcanic provinces of Mars. Identification of these areas with features that appear analogous to those observed in the Pine Butte area suggests similar styles of eruption and mode of emplacement. Such terrestrial analogies serve as a method to interpret the evolution of volcanic planetary surfaces on the inner planets.

Mazierski, Paul F.↗
NASA NTRS1972

Photogrammetry using Apollo 16 orbital photography, part B

Discussion is made of the Apollo 15 and 16 metric and panoramic cameras which provided photographs for accurate topographic portrayal of the lunar surface using photogrammetric methods. Nine stereoscopic models of Apollo 16 metric photographs and three models of panoramic photographs were evaluated photogrammetrically in support of the Apollo 16 geologic investigations. Four of the models were used to collect profile data for crater morphology studies; three models were used to collect evaluation data for the frequency distributions of lunar slopes; one model was used to prepare a map of the Apollo 16 traverse area; and one model was used to determine elevations of the Cayley Formation. The remaining three models were used to test photogrammetric techniques using oblique metric and panoramic camera photographs. Two preliminary contour maps were compiled and a high-oblique metric photograph was rectified.

Wu, S. S. C.↗
NASA NTRS1982

Effects of spatial resolution

Studies of the effects of spatial resolution on extraction of geologic information are woefully lacking but spatial resolution effects can be examined as they influence two general categories: detection of spatial features per se; and the effects of IFOV on the definition of spectral signatures and on general mapping abilities.

Abrams, M.↗
NASA NTRS2017

PHYS: Division of Physical Chemistry 258 - Properties and Origins of Cometary and Asteroidal Organic Matter Delivered to the Early Earth

Comets and asteroids may have contributed much of the Earth's water and organic matter. The Earth accretes approximately 4x10(exp 7) Kg of dust and meteorites from these sources every year. The least altered meteorites contain complex assemblages of organic compounds and abundant hydrated minerals. These carbonaceous chondrite meteorites probably derive from asteroids that underwent hydrothermal processing within the first few million years after their accretion. Meteorite organics show isotopic and chemical signatures of low-T ion-molecule and grain-surface chemistry and photolysis of icy grains that occurred in cold molecular clouds and the outer protoplanetary disk. These signatures have been overprinted by aqueously mediated chemistry in asteroid parent bodies, forming amino acids and other prebiotic molecules. Comets are much richer in organic matter but it is less well characterized. Comet dust collected in the stratosphere shows larger H and N isotopic anomalies than most meteorites, suggesting better preservation of primordial organics. Rosetta studies of comet 67P coma dust find complex organic matter that may be related to the macromolecular material that dominates the organic inventory of primitive meteorites. The exogenous organic material accreting on Earth throughout its history is made up of thousands of molecular species formed in diverse processes ranging from circumstellar outflows to chemistry at near absolute zero in dark cloud cores and the formative environment within minor planets. NASA and JAXA are currently flying sample return missions to primitive, potentially organic-rich asteroids. The OSIRIS-REx and Hayabusa2 missions will map their target asteroids, Bennu and Ryugu, in detail and return regolith samples to Earth. Laboratory analyses of these pristine asteroid samples will provide unprecedented views of asteroidal organic matter relatively free of terrestrial contamination within well determined geological context. Studies of extraterrestrial materials and returned samples are essential to understand the origins of Solar System organic material and the roles of comets and asteroids to providing the starting materials for the emergence of life.

Messenger, Scott↗
NASA NTRSDate not supplied

THUNDER: A New Frontiers-Class Titan Orbiter Mission Concept From the NASA JPL Planetary Science Summer School

Titan’s Hydrocarbons: Uncovering New Dimensions of Evolutionary pRocesses (THUNDER) is a dedicated geology and geophysics mission to trace Titan’s hydrocarbons from the inside out, assessing whether hydrocarbons move through Titan’s water ice bedrock to cycle from the subsurface ocean to the atmosphere. This Titan orbiter will use surface mapping (via radar and spectrometry) and gravity science to advance geophysical expertise for Saturn’s most Earth-like moon.

mission concept↗
DOE OSTI2018

A learning-based data-driven forecast approach for predicting future reservoir performance

Quantification of the predictive uncertainty of subsurface models has long been investigated. The traditional workflow is to calibrate prior models to match observed data, and then use the posterior models to simulate future system performance. Not only are these procedures computationally expensive, but they also have issues in maintaining geological model constraints during the calibration step. Data space inversion (DSI) was introduced recently to predict future system performance without the iterative history matching or model calibration step. In general, DSI approaches seek to establish a statistical relationship between the observed and forecast variables, as well as to quantify the predictive uncertainty of the forecast variables, by using an ensemble of uncalibrated prior models. Existing DSI approaches all require a number of complex transformation and mapping operations, which may deter their widespread use. Here in this study, we introduce a new and simpler DSI approach, the learning-based, data-driven forecast approach (LDFA), by combining dimension reduction and machine learning techniques to quickly provide accurate forecast results and reliably quantify corresponding uncertainty in the results. Our LDFA framework is demonstrated using two supervised learning algorithms, artificial neural network (ANN) and support vector regression (SVR), on two representative examples from reservoir engineering and geological carbon storage. Results suggest that our approach provides accurate forecast results (e.g., future oil production rate or cumulative injected CO 2 ) and reasonable predictive uncertainty intervals. Our framework is generic and may be applied to other surface and subsurface problems.

42 ENGINEERING↗
NASA NTRS2005

Geological Evolution of the Ganiki Planitia Quadrangle (V14) on Venus

The Ganiki Planitia quadrangle (25-50degN, 180-210degE) is located north of Atla Regio, south of Vinmara Planitia, and southeast of Atalanta Planitia. The region contains a diverse array of volcanic-, tectonic- and impact-derived features, and the objectives for the ongoing mapping effort are fivefold: 1) explore the formation and evolution of radiating dike swarms within the region, 2) use the diverse array of volcanic deposits to further test the neutral buoyancy hypothesis proposed to explain the origin of reservoir-derived features, 3&4) unravel the volcanic and tectonic evolution in this area, and 5) explore the implications of 1-4 for resurfacing mechanisms. Here we summarize our onging analysis of the material unit stratigraphy in the quadrangle, data central to meeting the aforementioned objectives successfully.

Grosfils, E. B.↗
NASA NTRS2015

FINESSE: Field Investigations to Enable Solar System Science and Exploration

The FINESSE (Field Investigations to Enable Solar System Science and Exploration) team is focused on a science and exploration field-based research program aimed at generating strategic knowledge in preparation for the human and robotic exploration of the Moon, near-Earth asteroids (NEAs) and Phobos and Deimos. We follow the philosophy that "science enables exploration and exploration enables science." 1) FINESSE Science: Understand the effects of volcanism and impacts as dominant planetary processes on the Moon, NEAs, and Phobos & Deimos. 2) FINESSE Exploration: Understand which exploration concepts of operations (ConOps) and capabilities enable and enhance scientific return. To accomplish these objectives, we are conducting an integrated research program focused on scientifically-driven field exploration at Craters of the Moon National Monument and Preserve in Idaho and at the West Clearwater Lake Impact Structure in northern Canada. Field deployments aimed at reconnaissance geology and data acquisition were conducted in 2014 at Craters of the Moon National Monument and Preserve. Targets for data acquisition included selected sites at Kings Bowl eruptive fissure, lava field and blowout crater, Inferno Chasm vent and outflow channel, North Crater lava flow and Highway lava flow. Field investigation included (1) differential GPS (dGPS) measurements of lava flows, channels (and ejecta block at Kings Bowl); (2) LiDAR imaging of lava flow margins, surfaces and other selected features; (3) digital photographic documentation; (4) sampling for geochemical and petrographic analysis; (5) UAV aerial imagery of Kings Bowl and Inferno Chasm features; and (6) geologic assessment of targets and potential new targets. Over the course of the 5-week field FINESSE campaign to the West Clearwater Impact Structure (WCIS) in 2014, the team focused on several WCIS research topics, including impactites, central uplift formation, the impact-generated hydrothermal system, multichronometer dating of impact products, and using WCIS as an analog test site for crew studies of sampling protocols. The FINESSE team visited and mapped all of the major islands within West Clearwater Lake. Excellent cliff exposures around the coasts of many of the islands allowed a general stratigraphy of impactites to be defined. Notable differences to previous work includes the discovery of a monomict lithic breccia and a medium to coarse grained impact melt rock. In addition, ample rock samples were returned from West Clearwater for geochronology study. Geochronology work centers around laboratory analyses of these samples (and samples collected in the future or obtained from archives housed at the Canadian Geological Survey). Samples returned from the FINESSE field season have been evaluated for suitability for geochronologic analysis, and selected samples have been crushed for mineral separation and/or sawed for the preparation of polished petrologic thin sections. Heavy minerals (e.g., zircon, titanite, and apatite) will be separated from the crushed material for (U-Th)/He geochronology. The sections will be used for laser ablation 40Ar/39Ar research after neutron irradiation. This presentation will highlight the exciting science and exploration work conducted by FINESSE, as well as future plans for continued research.

Phobos & Deimos↗
DOE OSTI2024

Carbon Storage Planning Inquiry Tool (CS PlanIT)

The CS PlanIT provides stakeholders with easy access to geographically explore, query, and evaluate multiple data layers for a user-defined area of interest, with the goal to accelerate carbon storage resource, feasibility assessments, and planning efforts. The data within CS PlanIT are arranged and categorized to assess each stage of the Carbon Capture and Storage supply chain, from CO2 (Carbon Dioxide) emission sources, critical energy infrastructure (including transport), to CO2 geologic storage and sink locations. Furthermore, stakeholders can gain insights into community and environmental impacts, as CS PlanIT leverages Environmental Justice and Social Justice (EJSJ) and natural hazard datasets. Additionally, documentation in the form of an interactive ArcGIS story map and data catalog are included within the EDX publication to provide user’s information to better understand and explain CS PlanIT’s layout, functions, and associated data sets.

Justman, Devin M.↗
DOE OSTI2024

Hunting for Polluted White Dwarfs and Other Treasures with Gaia XP Spectra and Unsupervised Machine Learning

White dwarfs (WDs) polluted by exoplanetary material provide the unprecedented opportunity to directly observe the interiors of exoplanets. However, spectroscopic surveys are often limited by brightness constraints, and WDs tend to be very faint, making detections of large populations of polluted WDs difficult. In this paper, we aim to increase considerably the number of WDs with multiple metals in their atmospheres. Using 96,134 WDs with Gaia DR3 BP/RP (XP) spectra, we constructed a 2D map using an unsupervised machine-learning technique called Uniform Manifold Approximation and Projection (UMAP) to organize the WDs into identifiable spectral regions. The polluted WDs are among the distinct spectral groups identified in our map. We have shown that this selection method could potentially increase the number of known WDs with five or more metal species in their atmospheres by an order of magnitude. Such systems are essential for characterizing exoplanet diversity and geology.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1978

Role of remote sensing in Bay measurements

Remote measurements of a number of surface or near surface parameters for baseline definition and specialized studies, remote measurements of episodic events, and remote measurements of the Bay lithosphere are considered in terms of characterizing and understanding the ecology of the Chesapeake Bay. Geologic processes and features best suited for information enhancement by remote sensing methods are identified. These include: (1) rates of sedimentation in the Bay; (2) rates of erosion of Bay shorelines; (3) spatial distribution and geometry of aquifers; (4) mapping of Karst terrain (sinkholes); and (5) mapping of fracture patterns. Recommendations for studying problem areas identified are given.

Mugler, J. P., Jr.↗
DOE OSTI2022

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗
DOE OSTI2024

Prospective Seal Unit Spatial Extent Database for U.S. Sedimentary Basins

The Prospective Seal Unit Spatial Extent Database for U.S. Sedimentary Basins contains a series of spatial datasets representing spatial extents of publicly available data for caprock and seal rock units within the Appalachian Basin, Denver-Julesburg Basin, Great Valley Basin (Sacramento and San Joaquin Basins), Illinois Basin, Michigan Basin, San Juan Basin, U.S. Gulf Coast Basin, and Williston Basin. The database is designed to support carbon storage feasibility and resources assessment for carbon transport and storage (CTS) projects while displaying the spatial extent of prospective seal units and provide a guide to the original data source. This database leverages publicly available data resources from authoritative sources (e.g. U.S. Geological Survey, State Geologic Surveys, and published reports), and aims to help guide users to understand the seal unit's spatial coverage and data gaps from the regional to sub-basin/field scale. The database is organized by seal unit/formation, including the spatial extent for data found to be available for the seal unit. The various datasets represented include spatial extents of the lithologic formation, depth to top structural contour maps, and thickness/isopach maps. Included in this submission are the following resources: 1. Geodatabase/Dataset: “prospective-seal-unit-extents-2025.gdb” 2. ReadMe: “readme-prospective-seal-unit-spatial-extent-dataset-2025.pdf” 3. Data Catalog: “prospective-seal-unit-spatial-extents-data-catalog-2025.xlsx” 4. Data Sources Key: “data-source.csv” Please see NETL disclaimers here: https://netl.doe.gov/home/disclaimer

Basin↗