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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 NTRS2022

Geophysical Observations Toolkit For Evaluating Coral Health (GOTECH) Fall 2021 Final Report

The NASA Langley Research Center (LaRC) Data Science Team (DST), under the Office of the Chief Information Officer (OCIO), is investigating the capacity of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite to infer the vitality of coral reefs. This report describes the Fall 2021 period of performance for the Geophysical Observations Toolkit for Evaluating Coral Health (GOTECH) project. During this effort, two student teams at Georgia Tech developed machine-learning models to predict the vitality of coral reefs in targeted geographic regions based on backscatter data from the CALIPSO satellite. To train these models, students fused data to form a common operating picture of how coral reefs have grown and decayed worldwide. This report describes the student assignment, background, and results of the semester's research.

Machine Learning↗
NASA NTRS2024

Mapping Ice Buried by the 1875 and 1961 Tephra of Askja Volcano, Northern Iceland using Ground-Penetrating Radar: Implications for Askja Caldera as a Geophysical Testbed for In-Situ Resource Utilization

Eruptions of the Askja Volcano in Northern Iceland in 1875 and 1961 blanketed the caldera with rhyolitic and basaltic tephra deposits, respectively, which preserved layers of seasonal snowpack as massive ice. Askja serves as an operational and geophysical analog to test ground-penetrating radar field and analysis techniques for in-situ resource utilization objectives relevant to the martian and lunar environments. We conducted ground-penetrating radar surveys at center frequencies of 200, 400, and 900 MHz to map the thickness and extent of tephra deposits and underlying massive ice at three caldera sites. We identified up to 1 meter of tephra preserving up to 4.5 meters of massive ice. We measured the real dielectric permittivity of the overlying tephra and the total attenuation at each frequency of the tephra and ice. A key objective of our investigation was to determine if this attenuation (or loss) could be used as an additional, identifying characteristic of massive ice preserved at depth when compared to ice-free stratigraphy. Loss rates of the ice-rich subsurface decreases with increasing ice thickness relative to the overburden which may constitute a possible signature. Attenuation also increased with increasing frequency. The tephra, ice, and other volcanic deposits at each of our three caldera sites and the ice-free pumice-mantled 1961 Vikrahraun lava flow exhibited a characteristically low loss rate at all frequencies. This result highlights the ambiguity associated with identifying the unique signature of ice within low-loss stratigraphies, a possible challenge for its identification in the martian or lunar subsurface using radar.

Emileigh S. Shoemaker↗
NASA NTRS1965

Report of geophysics working group

Lunar exploration program to determine surface and interior physical properties of moon - heat flow measurements, seismology, traverse logging, and instruments for deep probes and surface use

PHYSICAL PROPERTY↗
NASA NTRS2001

A strategy for compression and analysis of massive geophysical data sets

This paper describes a method for summaraizing data in a way that approximately preserves high-resolution data structure while reducing data volume and maintaining global integrity of very large, remote sensing data sets. The method is under development for one of Terra's instruments, the Multi-angle Imaging SpectroRadiometer (MISR).

geophysical data sets compression↗
NASA NTRS1993

Geophysical Model Functions for the Sea State Bias in Satellite Altimetry

The sea state bias in altimeter measurements of sea surface height has been investigated by many authors, resulting in several proposed algorithms of the form SSB = epsilonH where H is the significant wave height and epsilon is a nondimensional function of wind and wave parameters. All known algorithms are presently rated. By analyzing the performance of different models we conclude that further progress can hardly be achieved by raising the degree of the polynomial for epsilon(U,H). Physically based approaches employing a small number of adjustable parameters and theoretically justified non-dimensional combinations of external wind and wave factors appear to be more promising.

geophysical model functions sea state bias in sate↗