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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Ocean, Land and Meteorology Studies Using Space-Based Lidar Measurements

CALIPSO's main mission objective is studying the climate impact of clouds and aerosols in the atmosphere. CALIPSO also collects information about other components of the Earth's ecosystem, such as oceans and land. This paper introduces the physics concepts and presents preliminary results for the valueadded CALIPSO Earth system science products. These include ocean surface wind speeds, column atmospheric optical depths, ocean subsurface backscatter, land surface elevations, atmospheric temperature profiles, and A-train data fusion products.

Hu,Yongxiang↗
NASA NTRSDate not supplied

Field Tests With Trident Drill in Bishop Tuff Help Prepare for Future Missions to Moon and Mars

We performed drilling in volcanic deposits near Bishop California using an engineering model of the Honeybee Robotics TRIDENT (The Regolith and Ice Drill for Exploration of New Terrains) drill [1] a rotary percussive 1-meter class drill that is carried on the PRIME1and VIPER (Volatiles Investigating Polar Exploration Rover)[2] missions that launch in 2024. A similar drilling system was planned for the proposed Icebreaker Discovery class mission to Mars [3] and the Mars Life Explorer mission recommended by the 2020 Decadal Survey of planetary science [4]. The objectives of the project were (1) to use data collected by the drill for operational purposes as a probe of subsurface material properties in formations that are analogous to those that may be encountered on planetary surfaces; (2) correlate subsurface structures with those deduced from Ground Penetrating Radar (GPR); and (3) inspect the boreholes after they were drilled to test PERISCOPE (Probe for Exploring Regolith and Ice by Subsurface Classification of Organics, polycyclic aromatic hydrocarbons (PAHs), and Elements), a newly developed downhole UV fluorescence spectrometer [5].

Carol R. Stoker↗
NASA NTRS1981

Bacterial biomass and heterotrophic potential in the waters of the Chesapeake Bay plume and contiguous continental shelf

Seasonal baseline data on bacterial biomass and heterotrophic uptake in the Chesapeake Bay plume and contiguous Atlantic Ocean shelf waters are discussed. Viable count bacterial numbers in surface water samples collected during June 1980 ranged from a maximum of 190,000 MPN (most probable number)/ml at the Bay mouth to a minimum of 7900 MPN/ml offshore. Similarly, direct count densities ranged from 1,800,000 BU (bacterial units)/ml to 24,000 BU/ml. Heterotrophic potential (V max) was largest at the Bay mouth and lowest offshore. Biomass and V max values usually decreased with depth although subsurface maxima were occasionally observed at inshore stations. Correlation of biomass and heterotrophic potential data with selected hydrographic variables was determind with a nonparametric statistic. Results indicate viable counts are positively and significantly correlated with total chlorophyll, temperature, direct count and V max during June 1980; significant negative correlations are obtained with salinity and depth. Calculations of bacterial standing crop are discussed.

Kator, H. I.↗
NASA NTRS2014

Drill Embedded Nanosensors For Planetary Subsurface Exploration

We have developed a carbon nanotube (CNT) sensor for water vapor detection under Martian Conditions and the miniaturized electronics can be embedded in the drill bit for collecting sensor data and transmit it to a computer wirelessly.This capability will enable the real time measurement of ice during drilling. With this real time and in-situ measurement, subsurface ice detection can be easy, fast, precise and low cost.

Planetary exploration↗
DOE OSTI2025

Fiber-Optic Sensing for Earthquake Hazards Research, Monitoring, and Early Warning

The use of fiber‐optic sensing systems in seismology has exploded in the past decade. Despite an ever‐growing library of ground‐breaking studies, questions remain about the potential of fiber‐optic sensing technologies as tools for advancing if not revolutionizing earthquake‐hazards‐related research, monitoring, and early warning systems. A working group convened to explore these topics; we comprehensively examined the application of fiber optics in various aspects of earthquake hazards, encompassing earthquake source processes, crustal imaging, data archiving, and technological challenges. There is great potential for fiber‐optic systems to advance earthquake monitoring and understanding, but to fully unlock their capabilities requires continued progress in key areas of research and development, including instrument testing and validation, increased dynamic range for applications focused on larger earthquakes, and continued improvement in subsurface and source imaging methods. A key current stumbling block results from the lack of clear data archiving requirements, and we propose an initial strategy that balances data volume requirements with preserving key data for a broad range of future studies. In addition, we demonstrate the potential for fiber‐optic sensing to impact monitoring efforts by documenting the data completeness in a number of long‐term experiments. Finally, we outline the features of a instrument testing facility that would enable progress toward reliable and standardized distributed acoustic sensing data. Overcoming these current obstacles would facilitate progress in fiber‐optic sensing and unlock its potential application to a broad range of earthquake hazard problems.

58 GEOSCIENCES↗
DOE OSTI2025

Toward more-robust, AI-enabled subsurface seismic imaging for geotechnical applications

Non-invasive seismic imaging has the potential to cost-effectively evaluate large volumes of subsurface material to inform geotechnical site investigation. However, seismic imaging using full waveform inversion (FWI) requires significant computational time and is dependent on an initial starting model. As a result, FWI has not yet been widely adopted into geotechnical practice. Previous efforts, on relatively simple two-layered models, indicate that data-driven artificial intelligence (AI) models may be as effective as FWI at predicting 2D images of shear wave velocity (V s ). Furthermore, the AI model predictions can be made almost instantaneously after data acquisition and do not require an initial starting model. We examine the generality of these findings by developing a new AI model for subsurface seismic imaging, whereby we make several notable contributions. First, we architect a multimodal AI model that combines time- and frequency-domain representations of the seismic wavefield to predict a 50 m by 20 m subsurface image of V s . Second, we developed a new diverse dataset of 100,000 images with their corresponding seismic wavefields to train the AI model. Third, we propose four physics-informed data augmentations for data-driven seismic imaging. Fourth, we develop two prediction consistency tests to evaluate the model’s performance when the true subsurface is unknown. Our final model, which has been made publicly available, is capable of predicting a subsurface V s image from a single seismic wavefield with an average, mean absolute percent error (MAPE) of 24 %. The predictive model is applied to a field dataset and shown to be consistent with local geology and shear-wave refraction measurements from the same location.

Artificial intelligence↗
NASA NTRS1972

The microwave spectrum and nature of the subsurface of Mars.

Expected microwave spectra of Mars are computed using an improved thermal model and accurate aspect geometry. It is found that when seasonal polar cap effects are included in the calculations, the observable spectrum of Mars is flat from 0.1-21 cm to within the accuracy of present data. The spectra obtained from this model are consistent with all the data and are obtainable from a relatively simple model (homogeneous, dry, smooth dielectric sphere). This result differs from that predicted by the analytical theory in common use which is in apparent conflict with the observed spectra. A range of electrical loss tangents, covering the extreme limits for likely dry particulate geological materials, is employed. The case of a lunar-like subsurface is completely consistent with all present data.

Cuzzi, J. N.↗
NASA NTRSDate not supplied

Celestial Mapping System and Digital Lunar Library Initiative

We are preparing to create an interactive, global 3D lunar environment with integrated dataset and AI/ML tools to provide unique value to mission planners, scientists and the entire lunar community. This lunar environment will be based on NASA Ames Celestial Mapping System (CMS) [1] and Digital Lunar Library (DLL) Initiative. CMS provides a 3D virtual Lunar Globe with extensive user friendly tool sets, that include high resolution terrain visualization, elevation profiles, measurement kits, slope analysis, path optimization, line of sight analysis, equipment planning and placement tools and many other functionalities [1]. It has a thick client with less overhead to access hardware resources. This allows features such as terrain profiling and distance calculations to be performed on the client and on the fly. The application is developed to provide situational and domain awareness on the Lunar surface, planning capabilities for equipment placements and traverse path optimization. As data becomes available, CMS has the capabilities to integrate data sets that change dynamically in real-time, which will be useful for monitoring satellites and remotely-sensed data on the Lunar surface. CMS supports importing synthetic features in a variety of 3D, 2D, vector and raster formats. In the future, these capabilities will be enhanced by incorporating AI/ML tools and a plug-in architecture to enable customization by the user groups. With the help of DLL we will be able to : 1) Amplify the value of lunar information with AI-powered data enhancements 2) Acquire and integrate lunar data with AI-assisted georectification and homogenization 3) Analyze lunar data with advanced 3D visualization, intelligent search-by-example 4) Apply lunar data insights to specific use cases with an open plug-in architecture. The CMS-DLL initiative will have several potential use cases for NASA and the lunar community in general, including subsurface lava tube visualization and analysis, soil analysis, in-situ lunar resource visualization and representation on 3D globe, and data analytics for utilization. REFERENCES: [1] https://celestial.arc.nasa.gov/

3D Globe↗
NASA NTRS2009

Potential of Probing the Lunar Regolith using Rover-Mounted Ground Penetrating Radar: Moses Lake Dune Field Analog Study

Probing radars have been widely recognized by the science community to be an efficient tool to explore lunar subsurface providing a unique capability to address several scientific and operational issues. A wideband (200 to 1200 MHz) Ground Penetrating Radar (GPR) mounted on a surface rover can provide high vertical resolution and probing depth from few tens of centimeters to few tens of meters depending on the sounding frequency and the ground conductivity. This in term can provide a better understand regolith thickness, elemental iron concentration (including ilmenite), volatile presence, structural anomalies and fracturing. All those objectives are of important significance for understanding the local geology and potential sustainable resources for future landing sites in particular exploring the thickness, structural heterogeneity and potential volatiles presence in the lunar regolith. While the operation and data collection of GPR is a straightforward case for most terrestrial surveys, it is a challenging task for remote planetary study especially on robotic platforms due to the complexity of remote operation in rough terrains and the data collection constrains imposed by the mechanical motion of the rover and limitation in data transfer. Nevertheless, Rover mounted GPR can be of great support to perform systematic subsurface surveys for a given landing site as it can provide scientific and operational support in exploring subsurface resources and sample collections which can increase the efficiency of the EVA activities for potential human crews as part of the NASA Constellation Program. In this study we attempt to explore the operational challenges and their impact on the EVA scientific return for operating a rover mounted GPR in support of potential human activity on the moon. In this first field study, we mainly focused on the ability of GPR to support subsurface sample collection and explore shallow subsurface volatiles.

Horz, F.↗
NASA NTRS1995

Sea-air of CO2 in the North Pacific using shipboard and satellite data

A method has been developed to produce high-resolution maps of pCO2 in surface water for the North Pacific using satellite sea surface temperature (SST) data and statistical relationships between measured pCO2 and temperature. In the subtropical North Pacific the pCO in seawater is controlled primarily by temperature. Accordingly, pCO2 values that are calculated from the satellite SST data have good agreement with the measured values (rms deviation of +/- microatm). In the northwestern subpolar region the pCO2 is controlled not only by temperature, but also by significant seasonal changes in the total CO2 concentration, which are caused by seasonal changes in primary production, mixing with subsurface waters and sea-air exchange. Consequently, the parameterization of oceanic p CO2 based on SST data alone is not totally successful in the northwestern region (rms deviation of +/- 40 microatm). The use of additional satellite products, such as wind and ocean color data, as planned for a future study, is considered necessary to account for the pCO2 variability caused by seasonal changes in the total CO2 concentration. The net CO2 flux for the area of the North Pacific included in this study (north of 10 deg N) has been calculated using the monthly pCO2 distributions computed, and monthly wind speeds from the European Centre for Medium-Range Weather Forecasts. The region is found to be a net source to the atmosphere of 1.9 x 10(exp 12) to 5.8 x 10(exp 12) moles of CO2 per year (or 0.02-0.07 Gt C/yr), most of the outflux occurring in the subtropics.

Stephens, Mark P.↗
DOE OSTI2025

Controls From Above and Below: Snow, Soil, and Steepness Drive Diverging Trends of Subsurface Water and Streamflow Dynamics

ABSTRACT The importance of subsurface water dynamics, such as water storage and flow partitioning, is well recognised. Yet, our understanding of their drivers and links to streamflow generation has remained elusive, especially in small headwater streams that are often data‐limited but crucial for downstream water quantity and quality. Large‐scale analyses have focused on streamflow characteristics across rivers with varying drainage areas, often overlooking the subsurface water dynamics that shape streamflow behaviour. Here we ask the question: What are the climate and landscape characteristics that regulate subsurface dynamic storage, flow path partitioning, and dynamics of streamflow generation in headwater streams? To answer this question, we used streamflow data and a widely‐used hydrological model (HBV) for 15 headwater catchments across the contiguous United States. Results show that climate characteristics such as aridity and precipitation phase (snow or rain) and land attributes such as topography and soil texture are key drivers of streamflow generation dynamics. In particular, steeper slopes generally promoted more streamflow, regardless of aridity. Streams in flat, rainy sites (< 30% precipitation as snow) with finer soils exhibited flashier regimes than those in snowy sites (> 30% precipitation as snow) or sites with coarse soils and deeper flow paths. In snowy sites, less weathered, thinner soils promoted shallower flow paths such that discharge was more sensitive to changes in storage, but snow dampened streamflow flashiness overall. Results here indicate that land characteristics such as steepness and soil texture modify subsurface water storage and shallow and deep flow partitioning, ultimately regulating streamflow response to climate forcing. As climate change increases uncertainty in water availability, understanding the interacting climate and landscape features that regulate streamflow will be essential to predict hydrological shifts in headwater catchments and improve water resources management.

Kerins, Devon [Department of Civil and Environment↗
NASA NTRS1997

Seismic Study of the Subsurface Structure and Dynamics of the Solar Interior from High Spatial Resolution Observations

We have carried out the data reduction and analysis of Mt. Wilson 60' solar tower high spatial resolution observations. The reduction of the 100-day-long summer of 1990 observation campaign in terms of rotational splittings was completed leading to an excess of 600,000 splittings. The analysis of these splittings lead to a new inference of the solar internal rotation rate as a function of depth and latitude.

Korzennik, Sylvain G.↗
NASA NTRS1981

Development of Great Lakes algorithms for the Nimbus-G coastal zone color scanner

A series of experiments in the Great Lakes designed to evaluate the application of the Nimbus G satellite Coastal Zone Color Scanner (CZCS) were conducted. Absorption and scattering measurement data were reduced to obtain a preliminary optical model for the Great Lakes. Available optical models were used in turn to calculate subsurface reflectances for expected concentrations of chlorophyll-a pigment and suspended minerals. Multiple nonlinear regression techniques were used to derive CZCS water quality prediction equations from Great Lakes simulation data. An existing atmospheric model was combined with a water model to provide the necessary simulation data for evaluation of the preliminary CZCS algorithms. A CZCS scanner model was developed which accounts for image distorting scanner and satellite motions. This model was used in turn to generate mapping polynomials that define the transformation from the original image to one configured in a polyconic projection. Four computer programs (FORTRAN IV) for image transformation are presented.

Tanis, F. J.↗
NASA NTRS2015

Deriving Scaling Factors Using a Global Hydrological Model to Restore GRACE Total Water Storage Changes for China's Yangtze River Basin

This study used a global hydrological model (GHM), PCR-GLOBWB, which simulates surface water storage changes, natural and human induced groundwater storage changes, and the interactions between surface water and subsurface water, to generate scaling factors by mimicking low-pass filtering of GRACE signals. Signal losses in GRACE data were subsequently restored by the scaling factors from PCR-GLOBWB. Results indicate greater spatial heterogeneity in scaling factor from PCR-GLOBWB and CLM4.0 than that from GLDAS-1 Noah due to comprehensive simulation of surface and subsurface water storage changes for PCR-GLOBWB and CLM4.0. Filtered GRACE total water storage (TWS) changes applied with PCR-GLOBWB scaling factors show closer agreement with water budget estimates of TWS changes than those with scaling factors from other land surface models (LSMs) in China's Yangtze River basin. Results of this study develop a further understanding of the behavior of scaling factors from different LSMs or GHMs over hydrologically complex basins, and could be valuable in providing more accurate TWS changes for hydrological applications (e.g., monitoring drought and groundwater storage depletion) over regions where human-induced interactions between surface water and subsurface water are intensive.

water↗
NASA NTRS2003

Optimization Of Ocean Color Algorithms: Application To Satellite And In Situ Data Merging

The objective of our program is to develop and validate a procedure for ocean color data merging which is one of the major goals of the SIMBIOS project (McClain et al., 1995). The need for a merging capability is dictated by the fact that since the launch of MODIS on the Terra platform and over the next decade, several global ocean color missions from various space agencies are or will be operational simultaneously. The apparent redundancy in simultaneous ocean color missions can actually be exploited to various benefits. The most obvious benefit is improved coverage (Gregg et al., 1998; Gregg & Woodward, 1998). The patchy and uneven daily coverage from any single sensor can be improved by using a combination of sensors. Beside improved coverage of the global ocean the merging of ocean color data should also result in new, improved, more diverse and better data products with lower uncertainties. Ultimately, ocean color data merging should result in the development of a unified, scientific quality, ocean color time series, from SeaWiFS to NPOESS and beyond. Various approaches can be used for ocean color data merging and several have been tested within the frame of the SIMBIOS program (see e.g. Kwiatkowska & Fargion, 2003, Franz et al., 2003). As part of the SIMBIOS Program, we have developed a merging method for ocean color data. Conversely to other methods our approach does not combine end-products like the subsurface chlorophyll concentration (chl) from different sensors to generate a unified product. Instead, our procedure uses the normalized waterleaving radiances (LwN( )) from single or multiple sensors and uses them in the inversion of a semianalytical ocean color model that allows the retrieval of several ocean color variables simultaneously. Beside ensuring simultaneity and consistency of the retrievals (all products are derived from a single algorithm), this model-based approach has various benefits over techniques that blend end-products (e.g. chlorophyll): 1) it works with single or multiple data sources regardless of their specific bands, 2) it exploits band redundancies and band differences, 3) it accounts for uncertainties in the LwN( ) data and, 4) it provides uncertainty estimates for the retrieved variables.

Maritorena, Stephane↗
NASA NTRS2003

Optimization of Ocean Color Algorithms: Application to Satellite Data Merging

The objective of our program is to develop and validate a procedure for ocean color data merging which is one of the major goals of the SIMBIOS project. The need for a merging capability is dictated by the fact that since the launch of MODIS on the Terra platform and over the next decade, several global ocean color missions from various space agencies are or will be operational simultaneously. The apparent redundancy in simultaneous ocean color missions can actually be exploited to various benefits. The most obvious benefit is improved coverage. The patchy and uneven daily coverage from any single sensor can be improved by using a combination of sensors. Beside improved coverage of the global Ocean the merging of Ocean color data should also result in new, improved, more diverse and better data products with lower uncertainties. Ultimately, ocean color data merging should result in the development of a unified, scientific quality, ocean color time series, from SeaWiFS to NPOESS and beyond. Various approaches can be used for ocean color data merging and several have been tested within the frame of the SIMBIOS program. As part of the SIMBIOS Program, we have developed a merging method for ocean color data. Conversely to other methods our approach does not combine end-products like the subsurface chlorophyll concentration (chl) from different sensors to generate a unified product. Instead, our procedure uses the normalized water-leaving radiances (L(sub WN)(lambda)) from single or multiple sensors and uses them in the inversion of a semi-analytical ocean color model that allows the retrieval of several ocean color variables simultaneously. Beside ensuring simultaneity and consistency of the retrievals (all products are derived from a single algorithm), this model-based approach has various benefits over techniques that blend end-products (e.g. chlorophyll): 1) it works with single or multiple data sources regardless of their specific bands, 2) it exploits band redundancies and band differences, 3) it accounts for uncertainties in the (L(sub WN)(lambda)) data and, 4) it provides uncertainty estimates for the retrieved variables.

Maritorena, Stephane↗
NASA NTRS2004

Optimization of Ocean Color Algorithms: Application to Satellite Data Merging

The objective of the program is to develop and validate a procedure for ocean color data merging, which is one of the major goals of the SIMBIOS project. As part of the SIMBIOS Program, we have developed a merging method for ocean color data. Conversely to other methods our approach does not combine end-products like the subsurface chlorophyll concentration (chl) from different sensors to generate a unified product. Instead, our procedure uses the normalized water-leaving radiances L((sub wN)(lambda)) from single or multiple sensors and uses them in the inversion of a semi-analytical ocean color model that allows the retrieval of several ocean color variables simultaneously. Beside ensuring simultaneity and consistency of the retrievals (all products are derived from a single algorithm), this model-based approach has various benefits over techniques that blend end-products (e.g. chlorophyll): 1) It works with single or multiple data sources regardless of their specific bands; 2) It exploits band redundancies and band differences; 3) It accounts for uncertainties in the L((sub wN)(lambda)) data; 4) It provides uncertainty estimates for the retrieved variables.

Ritorena, Stephane↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗