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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Applying Compression-Based Metrics to Seismic Data in Support of Global Nuclear Explosion Monitoring

The analysis of seismic data for evidence of possible nuclear explosion testing is a critical global security mission that relies heavily on human expertise to identify and mark seismic signals embedded in background noise. To assist analysts in making these determinations, we adapted two compression distance metrics for use with seismic data. First, we demonstrated that the Normalized Compression Distance (NCD) metric can be adapted for use with waveform data and can identify the arrival times of seismic signals. Then we tested an approximation for the NCD called Sliding Information Distance (SLID), which can be computed much faster than NCD. We assessed the accuracy of the SLID output by comparing it to both the Akaike Information Criterion (AIC) and the judgments of expert seismic analysts. Our results indicate that SLID effectively identifies arrival times and provides analysts with useful information that can aid their analysis process.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

PDV Inspection and Analysis Demonstration: 2024 PDV Workshop

This document walks a user through a demonstration of working with PDV digitizer data using python. This demonstration and included suggested exercises will be used at the 2024 PDV workshop hands-on session as an example and skill-development training session. The tutorial allows the user to generate synthetic but realistic PDV waveform data and visualize/inspect the results using spectrograms and waveform viewing tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

An Adaptable Seismic Data Format

We present ASDF, the Adaptable Seismic Data Format, a modern and practical data format for all branches of seismology and beyond. The growing volume of freely available data coupled with ever expanding computational power opens avenues to tackle larger and more complex problems. Current bottlenecks include inefficient resource usage and insufficient data organization. Properly scaling a problem requires the resolution of both these challenges, and existing data formats are no longer up to the task. ASDF stores any number of synthetic, processed or unaltered waveforms in a single file. A key improvement compared to existing formats is the inclusion of comprehensive meta information, such as event or station information, in the same file. Additionally, it is also usable for any non-waveform data, for example, cross-correlations, adjoint sources or receiver functions. Last but not least, full provenance information can be stored alongside each item of data, thereby enhancing reproducibility and accountability. Any data set in our proposed format is self-describing and can be readily exchanged with others, facilitating collaboration. The utilization of the HDF5 container format grants efficient and parallel I/O operations, integrated compression algorithms and check sums to guard against data corruption. To not reinvent the wheel and to build upon past developments, we use existing standards like QuakeML, StationXML, W3C PROV and HDF5 wherever feasible. Usability and tool support are crucial for any new format to gain acceptance. We developed mature C/Fortran and Python based APIs coupling ASDF to the widely used SPECFEM3D_GLOBE and ObsPy toolkits.

58 GEOSCIENCES↗
NASA NTRS2012

Interpretation of Vector Electric Field Measurements of Bursty Langmuir Waves in the Cusp

An analysis of auroral Langmuir waves in the cusp observed by two high-frequency electric field instruments on the TRICE high-flyer sounding rocket shows many examples of Langmuir wave bursts modulated at approximately 10 kHz. Previous studies have explained these and similar observations as the result of beating between waves with very close frequencies near the Langmuir cutoff, resulting from either wave-wave interactions or independent linear excitations. The unique three-dimensional (3-D) data set provided by the NASA Goddard Space Flight Center TAEFWD instrument shows that up to 25% of waveforms selected from the most intense bursts exhibit anisotropic modulations, i.e., the beat nulls and peaks are not aligned in time across the three perpendicular electric field components. Modulations of this type arise when superposed wave normal modes possess differing polarizations, and simulations using wave modes calculated with the J-WHAMP numerical dispersion code show (1) that waves with differing polarizations can exist in conditions like those observed by TRICE-High and (2) that superpositions of such waves can produce anisotropic modulation. Fourier analysis of the 3-D waveform data suggests that both linear and elliptically polarized waves are present near the Langmuir cutoff at these times. These observations illustrate how 3-D measurements can give valuable insight into the nature of wave interactions in the auroral plasma environment.

Dombrowski, M. P.↗
NASA NTRS1981

A study of high density bit transition requirements versus the effects on BCH error correcting coding

Several methods for increasing bit transition densities in a data stream are summarized, discussed in detail, and compared against constraints imposed by the 2 MHz data link of the space shuttle high rate multiplexer unit. These methods include use of alternate pulse code modulation waveforms, data stream modification by insertion, alternate bit inversion, differential encoding, error encoding, and use of bit scramblers. The psuedo-random cover sequence generator was chosen for application to the 2 MHz data link of the space shuttle high rate multiplexer unit. This method is fully analyzed and a design implementation proposed.

Ingels, F.↗
DOE OSTI2016

SPE-5 Ground-Motion Prediction at Far-Field Geophone and Accelerometer Array Sites and SPE-5 Moment and Corner-Frequency Prediction

This report offers predictions for the SPE-5 ground-motion and accelerometer array sites. These predictions pertain to the waveform and spectral amplitude at certain geophone sites using Denny&Johnson source model and a source model derived from SPE data; waveform, peak velocity and peak acceleration at accelerometer sites using the SPE source model and the finite-difference simulation with LLNL 3D velocity model; and the SPE-5 moment and corner frequency.

47 OTHER INSTRUMENTATION↗
NASA NTRS2003

Pulsed Phase Lock Loop Device for Monitoring Intracranial Pressure During Space Flight

We have developed an ultrasonic device to monitor ICP waveforms non-invasively from cranial diameter oscillations using a NASA-developed pulsed phase lock loop (PPLL) technique. The purpose of this study was to attempt to validate the PPLL device for reliable recordings of ICP waveforms and analysis of ICP dynamics in vivo. METHODS: PPLL outputs were recorded in patients during invasive ICP monitoring at UCSD Medical Center (n=10). RESULTS: An averaged linear regression coefficient between ICP and PPLL waveform data during one cardiac cycle in all patients is 0.88 +/- 0.02 (mean +/- SE). Coherence function analysis indicated that ICP and PPLL waveforms have high correlation in the lst, 2nd, and 3rd harmonic waves associated with a cardiac cycle. CONCLUSIONS: PPLL outputs represent ICP waveforms in both frequency and time domains. PPLL technology enables in vivo evaluation of ICP dynamics non-invasively, and can acquire continuous ICP waveforms during spaceflight because of compactness and non-invasive nature.

Ueno, Toshiaki↗
NASA NTRS2006

LAD-C: A large area debris collector on the ISS

The Large Area Debris Collector (LAD-C) is a 10 sq m aerogel and acoustic sensor system under development by the U.S. Naval Research Laboratory (NRL) with main collaboration from the NASA Orbital Debris Program Office at Johnson Space Center, JAXA Institute of Space and Astronautical Science (ISAS), Chiba University (Japan), ESA Space Debris Office, University of California at Berkeley, and University of Kent at Canterbury (UK). The U.S. Department of Defense (DoD) Space Test Program (STP) has assumed the responsibility for having the system manifested and deployed on the International Space Station (ISS), and then having it retrieved and returned to Earth after one to two years. LAD-C will attempt to utilize the ISS as a scientific platform to characterize the near-Earth meteoroid and orbital debris environment in the size regime where little data exist. In addition to meteoroid and orbital debris sample return, the acoustic sensors will record impact time, location, signal strength, and acoustic waveform data of the largest collected samples. A good time-dependent meteoroid and orbital debris flux estimate can be derived. Analysis of the data will also enable potential source identification of some of the collected samples. This dynamical link can be combined with laboratory composition analysis of impact residuals extracted from aerogel to further our understanding of orbital debris population, and the sources of meteoroids, asteroids and comets.

Liou, J.-C.↗
DOE OSTI2024

Machine Learning Inference of Random Medium Properties

Earth materials are heterogeneous across a range of spatial scales, but the resolvability of small structures is limited by sparse data coverage, noise, bandlimitedness, and other difficulties. In practice, heterogeneities below a certain size cannot be recovered from seismic data except through statistical medium descriptions, which even then can be difficult to uniquely determine. To improve the characterization of such heterogeneities, we develop a novel supervised machine learning (ML) model that provides insight about the recoverability of statistical medium properties from elastic waveform data and succeeds despite cycle-skipping and other challenges well known from elastic waveform inversion. We demonstrate the approach using random media generated by superimposing self-affine random variations on homogeneous and layered background structures. After training on sparsely-recorded, high-frequency waveforms from hundreds of different random medium realizations, we show the ability of our ML model to recover correlation lengths and other statistical properties of interest to near-surface and crustal seismology, among other fields. For frequency passbands and spatial offsets encountered in seismology, Gaussian correlation lengths and the amplitude of the random variations relative to the background model are recovered even in challenging scenarios involving unknown medium parameters, complex crustal structures, and low signal-to-noise ratio. In comparison, von Kármán correlation lengths, which are related to larger-wavelength variations of the medium than Gaussian correlation lengths, are not as well recovered. These results provide one of the first and most systematic investigations of the recoverability of statistical properties of heterogeneities below the resolution limit of deterministic seismic tomography, and suggest practical ML strategies for high-frequency waveform seismology.

58 GEOSCIENCES↗
DOE OSTI2019

3D Moment Tensor Inversion of Underground Chemical Explosions from the Source Physics Experiments

Several physical mechanisms have been proposed to explain the generation of S-waves from underground explosions, such as asymmetries in the source, release of tectonic pre-stress, interactions with the free-surface, spall, and heterogeneities in the Earth. An accurate description of the explosion source processes is an important step towards understanding which of these plausible mechanisms are actively contributing to the generation of S-waves and under what conditions. In this study we explore the application of the seismic moment tensor source to model far-field, low frequency (up to 6 Hz) waveform data of over-buried chemical explosions from the Source Physics Experiment, with a focus on S-wave generation and amplitude predictions. We use an inverse waveform modeling approach to estimate the source properties of the chemical explosions, and compare solutions using different velocity models. 1D and 3D subsurface velocity models are used to characterize wave propagation between the source and receiver. We also performed analysis on wavefield simulations from physic-based explosion source modeling. The analyses show scattering and phase conversion from 3D heterogonies dominate the generation of far-field, S-wave energy observed in data, and that the variability in the recovered deviatoric component of the moment tensor source model are largely a result of inadequately accounting for 3D wave propagation effects in the inversion process.

58 GEOSCIENCES↗
DOE OSTI2024

Online Dynamic Cyber-Attack Diagnosis in Power Electronics Systems Based on Few-Shot Learning

With increasing exposure to software-based sensing and control, power electronics systems are facing higher risks of cyber-physical attacks. To ensure system stability and minimize potential economic losses, it is critical to monitor the operating states and detect those attacks at the early stage. However, anomaly detection and diagnosis of attacks are still challenging, especially when labeled anomaly data is difficult or even infeasible to obtain. To overcome this problem, we propose a Few-Shot Learning (FSL) based approach for cyber-attack diagnosis leveraging the waveform data. To the best of our knowledge, this work is the first attempt at leveraging FSL for cyber-attack diagnosis in power electronics systems. Extensive experimental results demonstrate that our proposed approach can achieve comparable diagnosis accuracy with the state-of-the-art data-driven methods using less than 0.04% of the training samples.

Li, Qi↗
DOE OSTI2020

Detection and Diagnosis of Data Integrity Attacks in Solar Farms Based on Multilayer Long Short-Term Memory Network

Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. Here this letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc–dc and dc–ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. As a comparison, we have evaluated classic data-driven methods, including K-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.

42 ENGINEERING↗
DOE OSTI2022

GraphAlign: Graph-Enabled Machine Learning for Seismic Event Filtering

This report summarizes results from a 2 year effort to improve the current automated seismic event processing system by leveraging machine learning models that can operated over the inherent graph data structure of a seismic sensor network. Specifically, the GraphAlign project seeks to utilize prior information on which stations are more likely to detect signals originating from particular geographic regions to inform event filtering. To date, the GraphAlign team has developed a Graphical Neural Network (GNN) model to filter out false events generated by the Global Associator (GA) algorithm. The algorithm operates directly on waveform data that has been associated to an event by building a variable sized graph of station waveforms nodes with edge relations to an event location node. This builds off of previous work where random forest models were used to do the same task using hand crafted features. The GNN model performance was analyzed using an 8 week IMS/IDC dataset, and it was demonstrated that the GNN outperforms the random forest baseline. We provide additional error analysis of which events the GNN model performs well and poorly against concluded by future directions for improvements.

58 GEOSCIENCES↗
DOE OSTI2024

Oscilloscope Data Push Program

This paper details the development of a Python program designed to automate the data acquisition and conversion for an oscilloscope for the purposes of a one-off/temporary data acquisition system for users that readily need data, and do not have the option of obtaining a Data Acquisition (DAQ) solution. Creating DAQ systems for analyzing a system requires expensive electronics and a dedicated team of engineers for support. Traditionally, manual data collection and processing are time consuming and prone to error. By automating these processes, the cost, efficiency and accuracy of data handling are improved upon. This project involves the creation of a program that interacts with the oscilloscope. During this interaction, there are various functions being performed such as the acquisition of waveform data via floating points, generating plots with the acquired wave points, and storing of floating points in a CSV file format for future reference and plotting purposes. While the initial aim of the project included continuous logging to a cloud database, this was deferred due to time constraints. The results portrayed an almost-instant rate of data collection with a buffer time, showcasing the potential for further integration and real-time data processing.

Osei-Tutu, Jason↗
DOE OSTI2015

A microseismic workflow for managing induced seismicity risk as CO 2 storage projects

It is well established that fluid injection has the potential to induce earthquakes—from microseismicity to large, damaging events—by altering state-of-stress conditions in the subsurface. While induced seismicity has not been a major operational issue for carbon storage projects to date, a seismicity hazard exists and must be carefully addressed. Two essential components of effective seismic risk management are (1) sensitive microseismic monitoring and (2) robust data interpretation tools. This report describes a novel workflow, based on advanced processing algorithms applied to microseismic data, to help improve management of seismic risk. This workflow has three main goals: (1) to improve the resolution and reliability of passive seismic monitoring, (2) to extract additional, valuable information from continuous waveform data that is often ignored in standard processing, and (3) to minimize the turn-around time between data collection, interpretation, and decision-making. These three objectives can allow for a better-informed and rapid response to changing subsurface conditions.

58 GEOSCIENCES↗
NASA NTRS2004

Automated Analysis, Classification, and Display of Waveforms

A computer program partly automates the analysis, classification, and display of waveforms represented by digital samples. In the original application for which the program was developed, the raw waveform data to be analyzed by the program are acquired from space-shuttle auxiliary power units (APUs) at a sampling rate of 100 Hz. The program could also be modified for application to other waveforms -- for example, electrocardiograms. The program begins by performing principal-component analysis (PCA) of 50 normal-mode APU waveforms. Each waveform is segmented. A covariance matrix is formed by use of the segmented waveforms. Three eigenvectors corresponding to three principal components are calculated. To generate features, each waveform is then projected onto the eigenvectors. These features are displayed on a three-dimensional diagram, facilitating the visualization of the trend of APU operations.

Kwan, Chiman↗
DOE OSTI2026

Constructing a High‐Resolution Aftershock Catalog for the 2017 Mw 8.2 Tehuantepec Earthquake Sequence Using a Machine Learning–Based Workflow

The 8 September 2017 Mw 8.2 Tehuantepec earthquake was the largest instrumentally recorded normal‐faulting earthquake in Mexico. The mainshock occurred offshore within the Tehuantepec seismic gap, generating >30,000 aftershocks in the following year. We applied an open‐source, machine learning (ML)–assisted workflow to construct a high‐resolution aftershock catalog using data from temporary and permanent seismic networks in southern Mexico. The workflow integrates PhaseNet for phase detection; GaMMA for phase association; and VELEST, HypoInverse, and HypoDD for velocity modeling and relocation. We processed seven months of continuous waveform data from 29 broadband stations, including a temporary rapid‐response deployment that improved station coverage of the offshore rupture zone. To evaluate performance, we compared our results against analyst‐reviewed picks and event locations from the Servicio Sismológico Nacional catalog. The resulting catalog contains 11,374 relocated earthquakes and represents the most comprehensive published dataset for this sequence, incorporating the first full use of the temporary network. Relocated hypocenters show improved depth control and align well with the Slab2.0 subduction geometry, revealing clearer separation between offshore slab events and onshore crustal seismicity. This study demonstrates that combining ML‐based detection with established methods provides a scalable and reproducible approach for constructing high‐quality earthquake catalogs in tectonically complex environments and offers practical guidance for adapting similar workflows to other earthquake sequences.

Garcia, Marc [The University of Texas at El Paso, ↗
NASA NTRS1982

The 1981 direct strike lightning data

Data waveforms obtained during the 1981 direct strike lightning tests, utilizing the NASA F-106B aircraft specially instrumented for lightning electromagnetic measurements are presented. The aircraft was operated in a thunderstorm environment to elicit strikes. Electromagnetic field data were recorded for both attached lightning and free field excitation of the aircraft.

Pitts, F. L.↗