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DOE OSTI2024

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and aiding users in accessing data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data.

accessibility↗
DOE OSTI2025

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository

The Department of Energy's (DOE's) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and helping users access data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data. This paper provides an update on recent improvements made to the GDR's data lakes and automated data pipelines, including: (1) streamlining the data lake intake process, (2) better educating users on the process and requirements through a new data lakes page, (3) adding data lake direct access links to GDR data lake submission pages, (4) implementing a DAS data pipeline to convert DAS data uploaded in SEG-Y format to a standardized hierarchical data format v5 (HDF5), (5) extending this pipeline to encompass data in the GDR data lake, (6) adding metadata requirements for geospatial data, (7) making user interface/user experience (UX) enhancements to the data pipelines' documentation pages, and (8) improving the GDR's data standards and pipelines pages to better guide users in ensuring that their data is standardized by the GDR's automated data pipelines. 2024 Geothermal Resources Council. All rights reserved.

accessibility↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗
NASA NTRS1987

Solar array flight experiment/dynamic augmentation experiment

This report presents the objectives, design, testing, and data analyses of the Solar Array Flight Experiment/Dynamic Augmentation Experiment (SAFE/DAE) that was tested aboard Shuttle in September 1984. The SAFE was a lightweight, flat-fold array that employed a thin polyimide film (Kapton) as a substrate for the solar cells. Extension/retraction, dynamics, electrical and thermal tests, were performed. Of particular interest is the dynamic behavior of such a large lightweight structure in space. Three techniques for measuring and analyzing this behavior were employed. The methodology for performing these tests, gathering data, and data analyses are presented. The report shows that the SAFE solar array technology is ready for application and that new methods are available to assess the dynamics of large structures in space.

Young, Leighton E.↗
NASA NTRS2019

The Seismic Experiment for Interior Structure (SEIS): Experiment Data Distribution

The six sensors of SEIS (The Seismic Experiment for Interior Structure) [- one of three primary instruments on NASA's Mars Lander Insight] cover a broad range of the seismic bandwidth, from 0.01 hertz to 50 hertz, with possible extension to longer periods. Data are transmitted in the form of three continuous VBB (Very Broad-Band) components at 2 samples per second (sps), an estimation of the short period (SP) energy content from the SP at 1 sps, and a continuous compound VBB/SP vertical axis at 10 sps. The continuous streams are augmented by requested event data with sample rates from 20 to 100 sps. SEIS data products are downlinked from the spacecraft in raw CCSDS (Consultative Committee for Space Data Systems) packets and converted to both the Standard for the Exchange of Earthquake Data (SEED) format files and ASCII tables (GeoCSV) for analysis and archiving. Metadata are available in dataless SEED and StionXML. Time series data (waveforms) are available in miniseed and GeoCSV. Data are distributed according to FDSN (Federation of Digital Seismograph Networks - http://www.fdsn.org) formats and interfaces. Wind, pressure and temperature data from the Auxiliary Payload Sensor Suite (APSS) will also be available in SEED format, and can be used for decorrelation and diagnostic purposes on SEIS.

Pardo, Constanza↗
NASA NTRS1978

A three-dimensional potential-flow program with a geometry package for input data generation

Information needed to run a computer program for the calculation of the potential flow about arbitrary three dimensional lifting configurations is presented. The program contains a geometry package which greatly reduces the task of preparing the input data. Starting from a very sparse set of coordinate data, the program automatically augments and redistributes the coordinates, calculates curves of intersection between components, and redistributes coordinates in the regions adjacent to the intersection curves in a suitable manner for use in the potential flow calculations. A brief summary of the program capabilities and options is given, as well as detailed instructions for the data input, a suggested structure for the program overlay, and the output for two test cases.

Halsey, N. D.↗
NASA NTRS1994

Spectral representation of high-frequency Space Shuttle data

High frequency Space Shuttle liftoff data are treated by autoregressive (AR) and autoregressive-moving-average (ARMA) digital algorithms. These algorithms provide useful information on the spectral densities of the data. Further, they yield spectral models, which lend themeselves to incorporation into the concept of the random response spectrum. This concept yields a reasonably smooth power spectrum for the design of structural and mechanical systems when the available data bank is limited. Due to the nonstationary of the liftoff event, the pertinent data are split into three slices. Each of the slices is associated with a rather distinguished phase of the liftoff event, in which stationarity can be expected. The presented results are preliminary in nature; they aim to call attention to the availability of the discussed concepts and to the need to augment the Space Shuttle data bank as more flights are completed.

Spanos, P. D.↗
NASA NTRS1990

Representation of high frequency Space Shuttle data by ARMA algorithms and random response spectra

High frequency Space Shuttle lift-off data are treated by autoregressive (AR) and autoregressive-moving-average (ARMA) digital algorithms. These algorithms provide useful information on the spectral densities of the data. Further, they yield spectral models which lend themselves to incorporation to the concept of the random response spectrum. This concept yields a reasonably smooth power spectrum for the design of structural and mechanical systems when the available data bank is limited. Due to the non-stationarity of the lift-off event, the pertinent data are split into three slices. Each of the slices is associated with a rather distinguishable phase of the lift-off event, where stationarity can be expected. The presented results are rather preliminary in nature; it is aimed to call attention to the availability of the discussed digital algorithms and to the need to augment the Space Shuttle data bank as more flights are completed.

Spanos, P. D.↗
NASA NTRS2004

Overview of NASA's Earth Science Data Systems

For over the last 15 years, NASA's Earth Science Enterprise (ESE) has devoted a tremendous effort to design and build the Earth Observing System (EOS) Data and Information System (EOSDIS) to acquire, process, archive and distribute the data of the EOS series of satellites and other ESE missions and field programs. The development of EOSDIS began with an early prototype to support NASA data from heritage missions and progressed through a formal development process to today's system that supports the data from multiple missions including Landsat 7, Terra, Aqua, SORCE and ICESat. The system is deployed at multiple Distributed Active Archive Centers (DAACs) and its current holdings are approximately 4.5 petabytes. The current set of unique users requesting EOS data and information products exceeds 2 million. While EOSDIS has been the centerpiece of NASA's Earth Science Data Systems, other initiatives have augmented the services of EOSDIS and have impacted its evolution and the future directions of data systems within the ESE. ESDIS had an active prototyping effort and has continued to be involved in the activities of the Earth Science Technology Office (ESTO). In response to concerns from the science community that EOSDIS was too large and monolithic, the ESE initiated the Earth Science Information Partners (ESP) Federation Experiment that funded a series of projects to develop specialized products and services to support Earth science research and applications. Last year, the enterprise made 41 awards to successful proposals to the Research, Education and Applications Solutions Network (REASON) Cooperative Agreement Notice to continue and extend the ESP activity. The ESE has also sponsored a formulation activity called the Strategy for the Evolution of ESE Data Systems (SEEDS) to develop approaches and decision support processes for the management of the collection of data system and service providers of the enterprise. Throughout the development of its earth science data systems, NASA has had an active collaboration with a number of interagency and international partners. One of the mechanisms that has been extremely helpful in initiating and promoting this collaboration has been NASA's participation in the Committee on Earth Observation Satellites (CEOS) and its Working Group on Information Systems and Services (WGISS). The CEOS members, working together, have implemented an International Directory Network that enables users to locate collections of earth science data held by the international community and an International Catalog System to search and order specific data products. CEOS WGISS has also promoted the international interest in the Open GIS Consortium s specifications that further advance the access and use of geospatial data and the interoperation of GTS components. These are just a few highlights of the benefits that member agencies gain from CEOS participation.

McDonald, Kenneth↗
NASA NTRS1976

Preliminary design-lift/cruise fan research and technology airplane flight control system

This report presents the preliminary design of a stability augmentation system for a NASA V/STOL research and technology airplane. This stability augmentation system is postulated as the simplest system that meets handling qualities levels for research and technology missions flown by NASA test pilots. The airplane studied in this report is a T-39 fitted with tilting lift/cruise fan nacelles and a nose fan. The propulsion system features a shaft interconnecting the three variable pitch fans and three power plants. The mathematical modeling is based on pre-wind tunnel test estimated data. The selected stability augmentation system uses variable gains scheduled with airspeed. Failure analysis of the system illustrates the benign effect of engine failure. Airplane rate sensor failure must be solved with redundancy.

Gotlieb, P.↗
NASA NTRS1977

The preliminary design of a lift-cruise fan airplane flight control system

This paper presents the preliminary design of a stability augmentation system for a NASA V/STOL research and technology airplane. This stability augmentation system is postulated as the simplest system that meets handling-quality levels for research and technology missions flown by NASA test pilots. The airplane studied in this report is a modified T-39 fitted with tilting lift/cruise fan nacelles and a nose fan. The propulsion system features a shaft that interconnects three variable-pitch fans and three powerplants. The mathematical modeling is based on pre-wind tunnel test estimated data. The selected stability augmentation system uses variable gains scheduled with airspeed. Failure analysis of the system illustrates the benign effect of engine failure. Airplane rate sensor failure must be solved with redundancy.

Gotlieb, P.↗
NASA NTRS1999

Experiments in Aligning Threaded Parts Using a Robot Hand

Techniques for determining and correcting threaded part alignment using force and angular position data are developed to augment currently limited techniques for align- ing threaded parts. These new techniques are based on backspinning a nut with respect to a bolt and measuring the force change that occurs when the bolt "falls" into the nut. Kinematic models that describe the relationship between threaded parts during backspinning are introduced and are used to show how angular alignment may be determined. The models indicate how to distinguish between the aligned and misaligned cases of a bolt and a nut connection by using axial force data only. In addition, by tracking the in-plane relative attitude of the bolt during spinning, data can be obtained on the direction of the angular misalignment which, in turn, is used to correct the misalignment. Results from experiments using a bolt held in a specialized fixture and a three fingers Stanford/JPL hand are presented.

Diftler, M. A.↗
NASA NTRS2011

Supersonic/Hypersonic Correlations for In-Cavity Transition and Heating Augmentation

Laminar-entry cavity heating data with a non-laminar boundary layer exit flow have been retrieved from the database developed at Mach 6 and 10 in air on large flat plate models for the Space Shuttle Return-To-Flight Program. Building on previously published fully laminar and fully turbulent analysis methods, new descriptive correlations of the in-cavity floor-averaged heating and endwall maximum heating have been developed for transitional-to-turbulent exit flow. These new local-cavity correlations provide the expected flow and geometry conditions for transition onset; they provide the incremental heating augmentation induced by transitional flow; and, they provide the transitional-to-turbulent exit cavity length. Furthermore, they provide an upper application limit for the previously developed fully-laminar heating correlations. An example is provided that demonstrates simplicity of application. Heating augmentation factors of 12 and 3 above the fully laminar values are shown to exist on the cavity floor and endwall, respectively, if the flow exits in fully tripped-to-turbulent boundary layer state. Cavity floor heating data in geometries installed on the windward surface of 0.075-scale Shuttle wind tunnel models have also been retrieved from the boundary layer transition database developed for the Return-To-Flight Program. These data were independently acquired at Mach 6 and Mach 10 in air, and at Mach 6 in CF4. The correlation parameters for the floor-averaged heating have been developed and they offer an exceptionally positive comparison to previously developed laminar-cavity heating correlations. Non-laminar increments have been extracted from the Shuttle data and they fall on the newly developed transitional in-cavity correlations, and they are bounded by the 95% correlation prediction limits. Because the ratio of specific heats changes along the re-entry trajectory, turning angle into a cavity and boundary layer flow properties may be affected, raising concerns regarding the application validity of the heating augmentation predictions.

Everhart, Joel L.↗
NASA NTRS1994

Feasibility of an integrated X-ray instrument for Mars exobiology and geology

By employing an integrated X-ray instrument on a future Mars mission, data obtained will greatly augment those returned by Viking; details relevant to the possibility of the origin and evolution of life on Mars will be acquired. An integrated combined X Ray Fluorescence/X Ray Detection (XRF/XRD) instrument has been breadboarded and demonstrated to accommodate important exobiology and geology experiment objectives outlined for Mars Environmental Survey (MESUR) and future Mars missions. Among others, primary objectives for the exploration of Mars include: the intense study of local areas on Mars to 'establish the chemical, mineralogical, and petrological character of different components of the surface material; to determine the distribution, abundance and sources and sinks of volatile materials, including an assessment of the biologic potential, now and during past epochs; and to establish the global chemical and physical characteristics of the Martian surface'. The XRF/XRD breadboard instrument identifies and quantifies soil surface elemental, mineralogical, and petrological characteristics and acquires data necessary to address questions on volatile abundance and distribution. Additionally, the breadboard is able to characterize the biogenic element constituents of soil samples providing information on the biologic potential of the Mars environment.

Fonda, M. L.↗
NASA NTRS1993

An Integrated XRF/XRD Instrument for Mars Exobiology and Geology Experiments

By employing an integrated x-ray instrument on a future Mars mission, data obtained will greatly augment those returned by Viking; details characterizing the past and present environment on Mars and those relevant to the possibility of the origin and evolution of life will be acquired. A combined x-ray fluorescence/x-ray diffraction (XRF/XRD) instrument was breadboarded and demonstrated to accommodate important exobiology and geology experiment objectives outlined for MESUR and future Mars missions. Among others, primary objectives for the exploration of Mars include the intense study of local areas on Mars to establish the chemical, mineralogical, and petrological character of different components of the surface material; to determine the distribution, abundance, and sources and sinks of volatile materials, including an assessment of the biologic potential, now and during past epoches; and to establish the global chemical and physical characteristics of the Martian surface. The XRF/XRD breadboard instrument identifies and quantifies soil surface elemental, mineralogical, and petrological characteristics and acquires data necessary to address questions on volatile abundance and distribution. Additionally, the breadboard is able to characterize the biogenic element constituents of soil samples providing information on the biologic potential of the Mars environment. Preliminary breadboard experiments confirmed the fundamental instrument design approach and measurement performance.

Koppel, L. N.↗
NASA NTRS1990

Comparisons between Geosat and Seasat tracking over non-ocean surfaces

The differences in both the quantity and quality of the nonocean measurements of the Geosat and Seasat altimeters due to the tracker modifications and satellite and antenna design changes are presented. The Geosat tracker exhibits more agility over the rougher surfaces and therefore is able to maintain lock better than Seasat. Large off-nadir attitude excursions created differences in Geosat tracking between cycles. The Geosat altimeter was at times able to track over surfaces Seasat could not, but sometimes the acquisition problem was worse. It is observed that Geosat yields denser coverage, but over land Seasat measurements yield a better overall picture of the general topography. It is concluded that the Geosat elevation data set over land can best be utilized to augment the information obtained from the Seasat data and is most useful when investigating topography at scales less than 100 km.

Brenner, A. C.↗