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At least 991 records · Page 55

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NASA NTRS1975

Sensitivity analysis of short-arc station coordinate determinations from range data

The accurate determination of the geocentric coordinates of a tracking station is essential for most geodetic and geophysical satellite applications. Since most of these satellites are close to the earth, the geopotential model is a dominant source of error which significantly influences station coordinate determinations. Other sources, such as GM error and drag, also influence the accuracy of the station coordinate determination. One technique for reducing the effect of these errors is to use short-arcs consisting of a few passes of the satellite over the tracking station. This paper analyzes the sensitivity of short-arc station coordinate estimates to various errors in the physical model, to the number of observations, and to the station-satellite geometry using simulated as well as real data.

Schutz, B. E.↗

NASA NTRS1976

Sensitivity analysis of short-arc station coordinate determinations from range data

Schutz, B. E.↗

DOE OSTI2024

EGS Collab Experiment 2: Microseismic Monitoring

This dataset contains continuous seismic waveform data recorded during stimulation and thermal circulation tests for the Enhanced Geothermal Systems (EGS) Collab Experiment #2, conducted from February to September 2022 at the Sanford Underground Research Facility in Lead, South Dakota. This experiment aimed to study and validate models of geothermal systems by injecting high-pressure fluids into rock formations 1200-1500 meters below the surface, inducing microseismic events. The seismic monitoring system included 16 three-component accelerometers and a 24-channel hydrophone array, installed in boreholes surrounding the test area. Data were recorded at high sampling rates using a continuous waveform recording system to monitor seismic activity in real time. The dataset contains the raw data stored in binary format, with files named based on timestamps, and includes calibration certificates for some sensors to facilitate corrections to real units. Users are strongly advised to consult the accompanying detailed report, which outlines the experimental setup, sensor specifications, installation procedures, and data processing methods. The report also describes important nuances, such as the hardware filters on hydrophones, sensor calibration details, and the naming conventions for the recorded data. Proper use of this dataset may require familiarity with seismic data analysis tools, such as the Obspy Python package, and an understanding of the SEED naming conventions used for channel identification.

15 GEOTHERMAL ENERGY↗

NASA NTRSDate not supplied

New Ways of Facilitating Improved Data Discovery and Access for NASA's Suborbital Earth Science Observations

NASA conducts field research in various Earth Science disciplines utilizing airborne and other non-satellite platforms to acquire in situ and remotely sensed observations indicative of physical processes across a range of scales. Field efforts are key in the development and validation of instruments and satellite algorithm refinements. The heterogeneous data, with a range of file formats, scales, and acquisition methods, support research in several science areas. NASA’s archive process assigns data products to discipline-oriented Distributed Active Archive Centers (DAACs) for stewardship. Over time, individual DAACs have developed tools for data browsing and serving disparate user bases. As science becomes more interdisciplinary, researchers need to incorporate observations from multiple campaigns, and multiple DAACs, into their work. Motivated in part by this shifting paradigm of needs, the Catalog of Archived Suborbital Earth Science Investigations (CASEI) was created. CASEI provides a single starting point to browse, search, and discover airborne and field data. Contextual metadata are organized and inter-linked allowing intuitive, integrated exploration across all NASA DAACs. Campaign science objectives, platform and instrument configurations, geographical details, geophysical concepts, and more are tracked in CASEI’s database, facilitating multi-parameter search, browse, and discovery of relevant data products. Researchers are able to directly access associated data products, via DOI links, regardless of the DAAC where they reside. Significant events, key time periods of high science interest within the longer-duration campaign effort, are also indicated and allow for a more efficient identification of critical data subsets. This presentation describes CASEI’s development, intensive metadata curation process, and demonstrates the web interface experience. Initial content metrics and plans for continued maintenance will also be discussed.

metadata↗

DOE OSTI2022

Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms

Machine learning methods have been extensively used to study the dynamics of complex fluid flows. One such algorithm, known as adaptive neural fuzzy inference system (ANFIS), can generate data-driven predictions for flow fields, but has not been applied to natural geophysical flows in large-scale rivers. Herein, we demonstrate the potential of ANFIS to produce three-dimensional (3D) realizations of the instantaneous flood flow field in several large-scale, virtual meandering rivers. The 3D dynamics of flood flow in large-scale rivers were obtained using large-eddy simulation (LES). The LES results, i.e., the 3D velocity components, were employed to train the learnable coefficients of an ANFIS. Further, the trained ANFIS, along with a few time-steps of LES results (precursor data) were then used to produce 3D realizations of flood flow fields in large-scale rivers with geometries other than the one the ANFIS was trained with. We also used the trained ANFIS to generate 3D realizations of river flow at a discharge other than that the ANFIS was trained with. The flow field results obtained from ANFIS were validated using separate LES runs to assess the accuracy of the 3D instantaneous realizations of the machine learning algorithm. An error analysis was conducted to quantify the discrepancies among the ANFIS and LES results for various flood flow predictions in large-scale rivers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Dynamic mode decomposition with core sketch

With the increase in collected data volumes, either from experimental measurements or high fidelity simulations, there is an ever-growing need to develop computationally efficient tools to process, analyze, and interpret these datasets. Modal analysis techniques have gained great interest due to their ability to identify patterns in the data and extract valuable information about the system being considered. Dynamic mode decomposition (DMD) relies on elements of the Koopman approximation theory to compute a set of modes, each associated with a fixed oscillation frequency and a decay/growth rate. Extracting these details from large datasets can be computationally expensive due to the need to implement singular value decomposition of the input data matrix. Sketching algorithms have become popular in numerical linear algebra where statistical theoretic approaches are utilized to reduce the cost of major operations. A sketch of a matrix is another matrix, which is significantly smaller, but still sufficiently approximates the original system. We put forth an efficient DMD framework, SketchyDMD, based on a core sketching algorithm that captures information about the range and corange (their mutual relationship) of input data. The proposed sketching-based framework can accelerate various portions of the DMD routines, compared to classical methods that operate directly on the raw input data. We conduct numerical experiments using the spherical shallow water equations as a prototypical model in the context of geophysical flows. In conclusion, we show that the proposed SketchyDMD is superior to existing randomized DMD methods that are based on capturing only the range of the input data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1974

Solar flare emissions and geophysical disturbances

Various geophysical phenomena are produced by both wave and particle emission from solar flares. In understanding these phenomena, it is necessary to investigate the nature of these emissions and solar flare phenomena. Using the observed data for these emissions, a review is given on the nature of solar flares and their development. Geophysical phenomena are discussed by referring to the results for solar flare phenomena.

Sakurai, K.↗

DOE OSTI2024

Utah FORGE: Well 16A(78)-32 Hydraulic Fracturing Stage 8 Crosswell Strain FDI and Microseismic Presentations - April 2024

This is a pair of PowerPoint presentations from Neubrex Energy Services (US), LLC. The presentations review work done in April 2024 on crosswell strain fracture driven interactions (FDI) and microseismic event monitoring during hydraulic fracturing in stage 8 of Utah FORGE well 16A(78)-32. Well 16B(78)-32 was the monitoring well and was where the data for these presentations were collected.

15 GEOTHERMAL ENERGY↗

NASA NTRS1977

Impact cratering phenomenon for the Ries multiring structure based on constraints of geological, geophysical, and petrological studies and the nature of the impacting body

In the present paper, an attempt is made to delineate, on the basis of field and laboratory data, the phenomenon of formation of the Ries multiring basin - the best preserved very large terrestrial impact structure. The model proposed conforms to constraints imposed by geological, geophysical, and petrological studies and by the nature of the postulated impacting body. It is also based on the impact features of a stony meteorite measuring 3 km in diameter at an impact velocity of 15 km/sec. The schematic reconstruction shows that critical to the production of a shallow crater is shallow impact penetration (shallow depth of burst). This and the nonballistic ejection of excavated material appear to be genetically related, i.e., if extensive nonballistic transport is recognized, then the associated crater must be a shallow structure and vice versa. This also means the shallow configuration of a crater may not have anything to do with postcratering readjustment.

Chao, E. C. T.↗

NASA NTRS2004

Picturing Data With Uncertainty

NASA is in the business of creating maps for scientific purposes to represent important biophysical or geophysical quantities over space and time. For example, maps of surface temperature over the globe tell scientists where and when the Earth is heating up; regional maps of the greenness of vegetation tell scientists where and when plants are photosynthesizing. There is always uncertainty associated with each value in any such map due to various factors. When uncertainty is fully modeled, instead of a single value at each map location, there is a distribution expressing a set of possible outcomes at each location. We consider such distribution data as multi-valued data since it consists of a collection of values about a single variable. Thus, a multi-valued data represents both the map and its uncertainty. We have been working on ways to visualize spatial multi-valued data sets effectively for fields with regularly spaced units or grid cells such as those in NASA's Earth science applications. A new way to display distributions at multiple grid locations is to project the distributions from an individual row, column or other user-selectable straight transect from the 2D domain. First at each grid cell in a given slice (row, column or transect), we compute a smooth density estimate from the underlying data. Such a density estimate for the probability density function (PDF) is generally more useful than a histogram, which is a classic density estimate. Then, the collection of PDFs along a given slice are presented vertically above the slice and form a wall. To minimize occlusion of intersecting slices, the corresponding walls are positioned at the far edges of the boundary. The PDF wall depicts the shapes of the distributions very dearly since peaks represent the modes (or bumps) in the PDFs. We've defined roughness as the number of peaks in the distribution. Roughness is another useful summary information for multimodal distributions. The uncertainty of the multi-valued data can also be interpreted by the number of peaks and the widths of the peaks as shown by the PDF walls.

Kao, David↗

DOE OSTI2021

GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Imaging the Shallow Structure of the Yucca Flat at the Source Physics Experiment Phase II Site with Horizontal-to-Vertical Spectral Ratio Inversion and a Large- N Seismic Array

The Source Physics Experiment (SPE) is a series of chemical explosions at the Nevada National Security Site (NNSS) with the goal of understanding seismic-wave generation and propagation of underground explosions. To understand explosion source physics, accurate geophysical models of the SPE site are needed. Here, we utilize a large-N seismic array deployed at the SPE phase II site to generate a shallow subsurface model of shear-wave velocity. The deployment consists of 500 geophones and covers an area of, approximately, 2.5 × 2 km. The array is located in the Yucca Flat in the northeast corner of the NNSS, Nye County, Nevada. Using ambient-noise recordings throughout the large-N seismic array, we calculate horizontal-to-vertical spectral ratios (HVSRs) across the array. We obtain 2D seismic images of shear-wave velocities across the SPE phase II site for the shallow structure of the basin. In this work the results clearly image two significant seismic impedance interfaces at ~150–500 and ~350–600 m depth. The shallower interface relates to the contrast between Quaternary alluvium and Tertiary volcanic rocks. The deeper interface relates to the contrast between Tertiary volcanic rocks and the Paleozoic bedrock. The 2D subsurface models support and extend previous understanding of the structure of the SPE phase II site. This study shows that the HVSR method in conjunction with a large-N seismic array is a quick and effective method for investigating shallow structures.

58 GEOSCIENCES↗

DOE OSTI2024

WHOLESCALE: Microseismic Event Catalog for San Emidio, Nevada 2022

This submission includes a high-precision seismic event catalog estimated from seismic data collected at San Emidio, Nevada from April to May 2022. The catalog lists the precise time, location, and magnitude of microseismic events recorded during this period. Both the seismic data and microseismic event catalog were produced as part of the Water & Hole Observations Leverage Effective Stress Calculations and Lessen Expenses (WHOLESCALE) project. Attached here are the microseismic event catalog, a link to the raw seismic data, and a detailed description of methods used to create the catalog.

15 GEOTHERMAL ENERGY↗

NASA NTRS1985

Venus gravity - A harmonic analysis and geophysical implications

An improved theoretical model of Venusian global gravity has been obtained by fitting a tenth degree spherical harmonic series to 78 orbital arcs of Doppler tracking data from the Pioneer Venus Orbiter. Maps of the free-air anomaly and its formal error are presented. Isostatic anomaly and 'Geoid' maps are also presented, and their geophysical implications are discussed in details. Comparison with equivalent resolution topographic models reveals a strong correlation between long wavelength gravity and the topography of Venus. Analysis of the second degree harmonics showed two aspects of the orientation of the inertial axes of Venus: (1) a significant (about three degrees) departure of the axis of greatest inertia from the rotational axis; and (2) a near alignment of the axis of least inertia with the location of the subterrestrial point at the time of the next inferior conjunction with earth (December 16, 2101). A series of contour maps of the Venusian free-air anomalies is provided.

Mottinger, N. A.↗

NASA NTRS2021

Detection of Anomalies in the UV/Vis Reflectances from the Ozone Monitoring Instrument

Various instrumental or geophysical artifacts, such as saturation, stray light, or obstruction of light (either coming from the instrument or related to solar eclipses), negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sun glint over open water or thick clouds. When saturation occurs, additional photoelectric charge generated at the saturated pixel may overflow to pixels adjacent to a saturated area and be reflected as a distorted image in the final sensor output.When these effects cannot be corrected to an acceptable level for science quality retrievals, flagging of the affected pixels is indicated. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectraover a 10 nm-spectral range; our Decorrelation Index (DI for brevity) is simply defined as DI=1-r. DI increases with anomalous additive effects or excessive noise in either radiances, the most likely cause indata from theOzone Monitoring Instrument (OMI),or irradiances. DI is relatively straight-forward to use and interpret and can be 20computed for different wavelength intervals. We developed a set of DIs for two spectral channelsof the OMI, a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350-498 nm) and 6 DIs in its ultraviolet 2 (UV2) channel (310-370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary Saturation Possibility Warning (SPW) flags currently provided for each individual spectral/spatial pixel in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.

Space sensor↗

DOE OSTI2023

Carbon Storage Technical Viability Approach (CS TVA) Database

The Carbon Storage Technical Viability Approach (CS TVA) database was developed to support the implementation of the CS TVA Matrix to a national data availability assessment for technically viable carbon storage. This database leverages the efforts of multiple adjacent and overlapping databases by non-redundantly combining the databases into a single database along with additionally providing tags facilitating the CS TVA. The non-redundant aspect of the database permits an accurate assessment of the concentration of available data, aiding in spatial and categorical data gaps analysis relative to the individual CS TVA Matrix Components. Version 2.0 of the database is an expansion of Version 1.0. Version 2.0 was created to include additional data gathered to fill gaps in the existing data set. Downloading the CS TVA v2.0 database will result in two separate databases, the version 1.0 original .gdb, and a second addendum .gdb with the new data gathered, together these two databases make up v2.0. Please see the ReadMe file below for full details, metadata information, use disclaimer, and attributions.

Coal↗

NASA NTRS1988

Wintertime losses of ozone in high northern latitudes

Total column ozone data over the past 22 to 30 years from ground-based Dobson and filter ozonometer stations between 30 N and 80 N are analyzed for residual trends remaining after allowance for the known geophysical variations corresponding to: (1) the change of seasons; (2) the quasi-biennial oscillation; and (3) the 11-year solar sunspot cycle. Examination of the data from several ground stations between 45 and 55 N indicated a seasonal difference in the long-term ozone series, with statistically significant losses in several winter months. Accordingly, the data from individual stations were analyzed with multiple regression analysis, seeking trends on a monthly basis after allowance for the known geophysical cycles. Previous statistical analyses were conducted as tests of 1-D model calculations which do not show any differences with the seasons, and any trend toward change in ozone concentrations is expressed in a yearly trend without seasonal variation. Such a model is inappropriate for calculations with a data set which exhibits a pronounced tendency toward seasonal differences in the trends. Comparisons with model calculations then require 2-D models into which seasonal and latitudinal differences can easily be programmed.

Harris, Neil↗

NASA NTRS2005

Comparing Goldstone Solar System Radar Earth-based Observations of Mars with Orbital Datasets

The Goldstone Solar System Radar (GSSR) has collected a self-consistent set of delay-Doppler near-nadir radar echo data from Mars since 1988. Prior to the Mars Global Surveyor (MGS) Mars Orbiter Laser Altimeter (MOLA) global topography for Mars, these radar data provided local elevation information, along with radar scattering information with global coverage. Two kinds of GSSR Mars delay-Doppler data exist: low 5 km x 150 km resolution and, more recently, high (5 to 10 km) spatial resolution. Radar data, and non-imaging delay-Doppler data in particular, requires significant data processing to extract elevation, reflectivity and roughness of the reflecting surface. Interpretation of these parameters, while limited by the complexities of electromagnetic scattering, provide information directly relevant to geophysical and geomorphic analyses of Mars. In this presentation we want to demonstrate how to compare GSSR delay-Doppler data to other Mars datasets, including some idiosyncracies of the radar data. Additional information is included in the original extended abstract.

Haldemann, A. F. C.↗