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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Determining neutron star properties by fitting oblate-star waveform models to X-ray burst oscillations

We describe sophisticated new Bayesian analysis methods that make it possible to estimate quickly the masses and radii of rapidly rotating, oblate neutron stars by fitting oblate-star waveform models to energy-resolved observations of the waveforms of X-ray burst oscillations produced by such stars. We find that a 25% variation of the temperature of the hot spot in the north-south direction does not significantly bias estimates of the mass M and equatorial radius R{sub eq} derived by fitting a model that assumes a uniform-temperature spot. Our results show that fits of oblate-star waveform models to waveform data can simultaneously determine M and R{sub eq} with 1σ uncertainties ≲7% if (1) the star's rotation rate is ≳ 600 Hz; (2) the spot center and observer's sightline are both within 30{sup ∘} of the star's rotational equator; and (3) ∼10{sup 7} counts are collected during burst oscillations that have a fractional rms amplitude ≳10%. A fractional amplitude of ∼10% is realistic, and the accepted NICER and proposed LOFT and AXTAR space missions could collect 10{sup 7} counts from a single star by combining data from many X-ray bursts from the star. Uncertainties ≲7% are small enough to improve substantially our understanding of cold, ultradense matter.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Model-based approach to investigate equipment-induced error in pressure-waveform derived hemodynamic measurements

Advanced hemodynamic monitoring systems have provided less invasive methods for estimating pressure-derived measurements such as pressure-derived cardiac output (CO) measurements. These devices apply algorithms to arterial pressure waveforms recorded via pressure recording components that transmit the pressure signal to a pressure monitor. While standards have been developed for pressure monitoring equipment, it is unclear how the equipment-induced error can affect secondary measurements from pressure waveforms. We propose an approach for modelling different components of a pressure monitoring system and use this model-based approach to investigate the effect of different pressure recording configurations on pressure-derived hemodynamic measurements. The proposed model-based approach is a three-step process. (1) Modelling the response of pressure recording components using bench tests; (2) verifying the identified models through nonparametric equivalence tests; and (3) assessing the effects of pressure recording components on pressure-derived measurements. To delineate the application of this approach, we performed a series of model-based analyses to quantify the combined effect of a wide range of tubing configurations with various damping ratios and natural frequencies and monitors with different bandwidths on pressure waveforms and CO measurements by six pulse contour algorithms. Model-based results show the error in pressure-derived CO measurements because of tubing configurations with different natural frequencies and damping ratios. Tubing configurations with low natural frequencies (<23 Hz) altered characteristics of pressure waveforms in a way that affected the CO measurement, some by as much as 20%. Our method can serve as a tool to quantify the performance of pressure recording systems with different dynamic properties. In conclusion, this approach can be applied to investigate the effects of physiologic signal recording configurations on various pressure-derived hemodynamic measurements.

42 ENGINEERING↗
DOE OSTI2021

Shear reflectivity compensation in full-waveform inversion using least-squares reverse-time migration

SUMMARY The computational cost of elastic-waveform inversion is too high for inverting PP reflections, while using acoustic full-waveform inversion (FWI) is inaccurate because it does not depend on the shear modulus/velocity/impedance that affects elastic PP wavefield amplitudes. To solve this problem, we develop a waveform inversion method that uses acoustic least-squares reverse-time migration (LSRTM) to compensate the shear reflectivity for acoustic FWI. Our method is based on the quasi-elastic-wave equation developed by Chapman et al. (2014). The quasi-elastic-wave equation uses a linearized acoustic-wave equation with shear modulus μ as a virtual source to correct the acoustic PP wavefield amplitudes toward elastic ones. Our waveform inversion method inverts for elastic parameters by minimizing the L2 norm of the difference between recorded and predicted PP reflections modelled using the quasi-elastic-wave equation. Numerical tests on synthetic and field data show that our method can properly handle the amplitudes of elastic PP reflections and provides an accurate estimate of the P- and S-wave velocities/impedances and, in some cases, the density. The method does not need the computationally expensive numerical solution to the elastic-wave equation. It also gives a better estimate of elastic parameters than a pure LSRTM method for elastic PP reflections.

Feng, Zongcai↗
DOE OSTI2016

OFDM inspired waveforms for 5G

As the standardization activities are being formed to lay the foundation of 5G wireless networks, there is a common consensus on the need to replace the celebrated OFDM by a more effective air interface that better serves the challenging needs of 5G. The main reason that has made OFDM popular in the past is related to the fact that information symbols are carried over a number of pure tones/sinusoidal signals. Moreover, with the use of cyclic prefix (CP), it is assured that the information carrying tones are only affected by the channel (complex) gains at the respective frequencies. Accordingly, the channel effect can be trivially compensated for (equalized) in the frequency domain through a single complex tap per subcarrier. However, as network air interfaces become more complex and the demand for multiuser services grows, OFDM is found to be incapable of handling the inevitable loss of synchronization among users. In the recent past, two novel waveforms (namely, GFDM and C-FBMC) have been discussed in the literature to overcome this and other drawbacks of OFDM. Interestingly, and at the same time not surprising, these methods share a common fundamental property with OFDM: each data packet is made up of a number of tones that are modulated by information symbols. In this tutorial article, we build a common framework based on the said OFDM principle and derive GFDM and C-FBMC waveforms from this point of view. This derivation provides a new prospective that facilitates straightforward understanding of channel equalization and the application of these new waveforms to MIMO channels. As a result, it also facilitates derivation of new structures for more efficient synthesis/analysis of these waveforms than those that have been reported in the literature.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

3D Deep Learning Joint Inversion of Active Seismic Full Waveform and Passive Seismic Traveltime Data for Reservoir Imaging and Uncertainty Quantification

Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.

Um, Evan Schankee [Lawrence Berkeley National Labo↗
DOE OSTI2018

Uncertainty analysis for infrasound waveform inversion: Application to explosion yield estimation

While the acoustic waveform inversion method is increasingly used in geophysical acoustics to constrain source parameters, the inversion results are often provided without any uncertainty analysis. Here, this study presents a probabilistic representation for acoustic waveform inversion and method to evaluate the inversion uncertainty using ground-truth data. A posteriori probability distribution of source estimate is described by a priori waveform misfit covariance and the variance of acoustic source model. The probabilistic framework is applied to local explosion infrasound to estimate the yields of explosions and uncertainty. Estimated yields showed overall good agreement with the true yields (less than 25% errors). The uncertainty of the estimated yield is represented by the sum of the waveform inversion uncertainty and source model uncertainty. It is shown that the yield uncertainty attributed to local infrasound inversion (within 10 km) is as small as the uncertainty caused by 10% prediction errors in the acoustic source model. Finally, these results indicate that the acoustic source model uncertainty should also be considered for accurate yield estimation and that local infrasound can be a valuable tool to understand the magnitude of the source uncertainty.

58 GEOSCIENCES↗
DOE OSTI2021

Using Dense Array Waveform Correlations to Build a Velocity Model with Stochastic Variability

In an ongoing effort to improve 3D seismic-wave propagation modeling for frequencies up to 10 Hz, we used cross correlations between vertical-component waveforms from an underground chemical explosion to estimate the statistical properties of small-scale velocity heterogeneities. The waveforms were recorded by a dense 2D seismic array deployed during the Source Physics Experiments for event number 5 (SPE-5) in a series of six underground chemical explosions, conducted at the Nevada National Security Site. The array consisted of 996 geophones with a 50–100 m grid spacing, deployed at the SPE site at the north end of the Yucca Flat basin. The SPE were conducted to investigate the generation and propagation of seismic and acoustic waves from underground explosions. Comparisons of decay rates of waveform cross correlations as function of interstation distance, computed for observed and synthetic seismograms from the SPE-5 chemical explosion, were used to constrain statistical properties of correlated stochastic velocity perturbations representing small-scale heterogeneities added to a geology-based velocity model of the Yucca Flat basin. Using comparisons between recorded and simulated waveform cross correlations, we were able to recover sets of statistical properties of small-scale velocity perturbations in the velocity model that produce the best-fit between the recorded and simulated ground motion. The stochastic velocity fluctuations in the velocity model that produced the smallest misfits have a horizontal correlation distance of between 400 and 800 m, a vertical correlation distance between 100 and 200 m, and a standard deviation of 10% from the nominal model velocity in the alluvium basin layers. They also have a horizontal correlation distance of 1000 m, a vertical correlation distance of 250 m, and a standard deviation of 6% in the underlying and consolidated sedimentary layers, up to a depth of 4 km. Comparisons between observed and simulated wavefields were used to assess the proposed small-scale heterogeneity enhancements to the Yucca Flat basin model. We found that adding a depth-resolved stochastic variability to the geology-based velocity model improves the overall performance of ground-motion simulations of an SPE-5 explosion in the modeled frequency range up to 10 Hz. The results may be applicable to other similar basins.

58 GEOSCIENCES↗
DOE OSTI2021

Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high–quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear–Test–Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. Here, we compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.

58 GEOSCIENCES↗
DOE OSTI2026

Phase Picking Beyond Local Distances: Where Waveform Filtering Still Matters for Deep Learning Models

Waveform filtering is a standard step in traditional seismic phase picking but often receives little attention in deep learning workflows, where models are typically trained on raw or minimally processed waveforms. Although this strategy performs well for local events, we show that performance can degrade substantially at regional distances. To address this limitation, we introduce two ways to incorporate multiband-filtered waveforms into deep learning phase pickers. The stacking approach concatenates filtered inputs along the channel dimension, while the branching approach processes each frequency band through a dedicated network branch before feature fusion. Both approaches can substantially improve performance across epicentral distances of 0° to 20°, but their effectiveness depends strongly on the selected frequency bands. Tests with multiple filter banks show that filter-bank design should be treated as part of model optimization rather than as a fixed preprocessing choice. Grad-CAM analysis of the branching model indicates that band importance varies among waveform samples and across training realizations, with only a weak overall preference for the 0.25 to 0.5 Hz band. These results show that no single filter band is consistently optimal and demonstrate that explicit feature engineering remains valuable for robust deep learning-based seismic phase picking.

58 GEOSCIENCES↗
DOE OSTI2018

Computation of Kernels for Full Waveform Seismic Inversion Using Parelasti

Full waveform inversion allows the seismologist to utilize an entire waveform and all the information it contains to help image the 3-D structure of the interior of the earth. This report summarizes the basic theory that has been developed in full waveform seismic inversion, primarily related to computation of sensitivity kernels. It then describes the implementation of this theory using Sandia Geophysics Department's Parelasti code, a 3-D full waveform elastic simulation algorithm. Finally, the code is validated using synthetics from simple homogeneous elastic earth models.

58 GEOSCIENCES↗
DOE OSTI2020

Applying Waveform Correlation to Mining Blasts Using a Global Sparse Network

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment that uses waveform templates recorded by multiple International Monitoring System stations of the Comprehensive Nuclear-Test-Ban Treaty for up to 10 years prior to detect and identify mining blasts that occur during single weeks of study. We discuss approaches for template selection, threshold setting, and event detection that are specialized for mining blasts and a sparse, global network. We apply the approaches to two different weeks of study for each of two geographic regions, Wyoming and Scandinavia, to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be effective for operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to develop an intuition about potential reduction in analyst workload.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Applying Waveform Correlation to Mining Blasts Using a Global Sparse Network

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment that uses waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization for up to 10 years prior to the time period of interest to detect and identify mining blasts that occur during single weeks of study. We discuss approaches for template selection, threshold setting, and event detection that are specialized for mining blasts and a sparse, global network. We apply the approaches to two different weeks of study for each of two geographic regions, Wyoming and Scandinavia, to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be effective for operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to develop an intuition about potential reduction in analyst workload.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2019

Applying Waveform Correlation to Aftershock Sequences Using a Global Sparse Network

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize earthquake aftershock sequences to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in detecting similar seismic waveforms from repeating earthquakes, including aftershock sequences. We report the results of an experiment that uses waveform templates recorded by multiple stations of the Comprehensive Nuclear-Test-Ban Treaty International Monitoring System during the first twelve hours after a mainshock to detect and identify aftershocks that occur during the subsequent week. We discuss approaches for station and template selection, threshold setting, and event detection that are specialized for aftershock processing for a sparse, global network. We apply the approaches to three aftershock sequences to evaluate the potential for establishing a set of standards for aftershock waveform correlation processing that can be effective for operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Center to develop an intuition about potential reduction in analyst workload.

58 GEOSCIENCES↗
DOE OSTI2025

Technical Report on Subsurface Monitoring of the Brady Hot Spring Geothermal Site, Nevada, based upon Full Waveform Inversion

Abilities to accurately characterize the subsurface in a geothermal setting is key to assess and support production. An important element of geothermal reservoir monitoring is also the ability to investigate fluid transport within fracture network. This report focuses on improving subsurface imaging and monitoring in geothermal settings using full waveform inversion based on the adjoint method and time-lapse imaging. To assess our method, we rely on a dense seismic dataset collected in 2016 at the Brady Hot Springs geothermal site in Nevada for the DOE-funded project Poroelastic Tomography by Adjoint Inverse Modeling of Data from Seismology, Geodesy, and Hydrology. This dataset captures subsurface changes across four stages of geothermal power plant operations, which involve varying rates of fluid injection and extraction. Two velocity models were previously derived from this dataset using different methods: one based on travel times and another on sweep interferometry. Our first step is to refine these models using adjoint tomography, which has been applied successfully at global and regional-scales but is less common at the reservoir-scale. Two approaches are then explored for time-lapse analysis: directly comparing refined tomographic models from different stages or backpropagating waveform differences relative to a baseline tomographic model. The main take away is that both approaches highlight similar reservoir behaviors, but the latter approach is more computationally effective in capturing small-scale changes in subsurface properties. For this work, we leverage the use of Salvus (www.mondaic.com), an end-to-end seismic imaging solution, relying on the spectral element method to compute forward and adjoint simulations, and developed by Mondaic Ltd. It includes integrated workflow management that handles waveform and metadata, launches simulations, computes waveform misfits and adjoint sources, and iterates for model updates by nonlinear optimization.

15 GEOTHERMAL ENERGY↗
NASA NTRS1971

Response of electroexplosive devices to impulsive waveforms.

The firing characteristics of insensitive electroexplosive devices to certain impulsive waveforms have been investigated. For these waveforms, energy is delivered in a time short compared to the thermal time constant and therefore cooling plays a negligible role. One waveform is a terminated capacitor discharge wherein the regular discharge of a capacitor is terminated at a preset point. Another is a half-sine wave pulse. The theory, design, and application of both impulsive waveform generators are presented together with certain limited experimental observations.

Rosenthal, L. A.↗
NASA NTRS1977

Altimeter waveform software design

Techniques are described for preprocessing raw return waveform data from the GEOS-3 radar altimeter. Topics discussed include: (1) general altimeter data preprocessing to be done at the GEOS-3 Data Processing Center to correct altimeter waveform data for temperature calibrations, to convert between engineering and final data units and to convert telemetered parameter quantities to more appropriate final data distribution values: (2) time "tagging" of altimeter return waveform data quantities to compensate for various delays, misalignments and calculational intervals; (3) data processing procedures for use in estimating spacecraft attitude from altimeter waveform sampling gates; and (4) feasibility of use of a ground-based reflector or transponder to obtain in-flight calibration information on GEOS-3 altimeter performance.

Hayne, G. S.↗
NASA NTRS1980

Wallops waveform analysis of SEASAT-1 radar altimeter data

Fitting a six parameter model waveform to over ocean experimental data from the waveform samplers in the SEASAT-1 radar altimeter is described. The fitted parameters include a waveform risetime, skewness, and track point; from these can be obtained estimates of the ocean surface significant waveheight, the surface skewness, and a correction to the altimeter's on board altitude measurement, respectively. Among the difficulties encountered are waveform sampler gains differing from calibration mode data, and incorporating the actual SEASAT-1 sampled point target response in the fitted wave form. There are problems in using the spacecraft derived attitude angle estimates, and a different attitude estimator is developed. Points raised in this report have consequences for the SEASAT-1 radar altimeter's ocean surface measurements are for the design and calibration of radar altimeters in future oceanographic satellites.

Hayne, G. S.↗
NASA NTRS1981

Effect of positive pulse charge waveforms on the energy efficiency of lead-acid traction cells

The effects of four different charge methods on the energy conversion efficiency of 300 ampere hour lead acid traction cells were investigated. Three of the methods were positive pulse charge waveforms; the fourth, a constant current method, was used as a baseline of comparison. The positive pulse charge waveforms were: 120 Hz full wave rectified sinusoidal; 120 Hz silicon controlled rectified; and 1 kHz square wave. The constant current charger was set at the time average pulse current of each pulse waveform, which was 150 amps. The energy efficiency does not include charger losses. The lead acid traction cells were charged to 70 percent of rated ampere hour capacity in each case. The results of charging the cells using the three different pulse charge waveforms indicate there was no significant difference in energy conversion efficiency when compared to constant current charging at the time average pulse current value.

Smithrick, J. J.↗