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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Multi-Channel Correlation Detectors: Accounting for and Reducing Non-target Detections [Slides]

Overwhelming non-target detections require more representative hypothesis test to target real events. Implicit signal model with convex-cone geometry includes both target waveforms and non-targets. New detector screens target waveforms from convex cone members that correlation detectors return. Proto-type detector returns analyst-equivalent detections, requires minimal modification from correlation detector, and has quantifiable detection performance, and satisfies all solution requirements to reduce false detections for GNDD.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Fully Autonomous Multiplet Event Detection: Application to Local-Distance Monitoring of Blood Falls Seismicity

We apply a fully autonomous icequake detection methodology to a single day of high-sample rate (200 Hz) seismic network data recorded from the terminus of Taylor Glacier, ANT that temporally coincided with a brine release episode near Blood Falls (May 13, 2014). We demonstrate a statistically validated procedure to assemble waveforms triggered by icequakes into populations of clusters linked by intra-event waveform similarity. Our processing methodology implements a noise-adaptive power detector coupled with a complete-linkage clustering algorithm and noise-adaptive correlation detector. This detector-chain reveals a population of 20 multiplet sequences that includes ~150 icequakes and produces zero false alarms on the concurrent, diurnally variable noise. Our results are very promising for identifying changes in background seismicity associated with the presence or absence of brine release episodes. We thereby suggest that our methodology could be applied to longer time periods to establish a brine-release monitoring program for Blood Falls that is based on icequake detections.

42 ENGINEERING↗
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↗
DOE OSTI2017

Paracousti-UQ: A Stochastic 3-D Acoustic Wave Propagation Algorithm.

Acoustic full waveform algorithms, such as Paracousti, provide deterministic solutions in complex, 3-D variable environments. In reality, environmental and source characteristics are often only known in a statistical sense. Thus, to fully characterize the expected sound levels within an environment, this uncertainty in environmental and source factors should be incorporated into the acoustic simulations. Performing Monte Carlo (MC) simulations is one method of assessing this uncertainty, but it can quickly become computationally intractable for realistic problems. An alternative method, using the technique of stochastic partial differential equations (SPDE), allows computation of the statistical properties of output signals at a fraction of the computational cost of MC. Paracousti-UQ solves the SPDE system of 3-D acoustic wave propagation equations and provides estimates of the uncertainty of the output simulated wave field (e.g., amplitudes, waveforms) based on estimated probability distributions of the input medium and source parameters. This report describes the derivation of the stochastic partial differential equations, their implementation, and comparison of Paracousti-UQ results with MC simulations using simple models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Evaluating Corrosion in SAVY Containers using Non-Destructive Techniques

Powerpoint presentation on Ultrasonic and Eddy Current NDT; UT Theory; Eddy current (ECA): How it works; Controlled Corrosion at NM Tech; Results – HCl Corrosion; Waveform Data for 10M HCl; Accuracy Statistics; Results – FeCl 3 Pitting; Waveforms for Anhydrous FeCl 3 ; Analyzing Corroded Stainless Steel 316L Plates; 316L Plate to Imitate Pitting; ECA Pit Depth Calibration Curve; C Scan Imaging; UT Pit Detection; SST Containers: Ultrasonic (UT) vs. CMM; UT Data Analysis; UT Conclusions and Observations; ECA Conclusions; Automated System Vision.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Traditional Look at SPE Phase 1 Surface Seismic Data: Far-Field Instruments, Measurements, and Analysis

We visually examined surface seismic data from the Source Physics Experiment (SPE) Phase-1 chemical explosions in preparation for waveform analysis and interpretation. Recording stations included far-field (near source) geophone lines, and both permanent network, reoccupied USArray, and temporary stations at local and regional distances. We found that near-source waveforms are dominated by broad band compressional (P), and band limited surface wave (Rg) (< 10 Hz) phases. Distinct, consistent shear (S) phases are difficult to observe visually even with a comprehensive bank of frequency bands. Secondary phases are indistinct at near-local distances, unlike for earthquake sources, especially at the high frequencies of interest for discrimination (e.g. 4-8 Hz), likely due to intense near surface scattering. S phases begin to emerge from the scattered background at and beyond 100 km. We identified regional Pn, Pg and Lg phases to 446 km for SPE-5. The data set is, unfortunately, plagued by poorly recorded instrument responses, or, possibly, time varying site amplification due to human interaction with sensors. Instrument response issues should be fixable, and this should be attempted to make this a truly, high-quality data set for use by the monitoring community.

58 GEOSCIENCES↗
DOE OSTI2019

Evaluating and Improving the USGS 3D Seismic Velocity Model in the San Francisco East Bay by Integrating Earthquake Ground-Motion Simulations and Noise-Derived Empirical Green's Functions

Simulations of scenario earthquake ground motions play an increasingly important role in improving seismic hazard assessment. In the San Francisco Bay Area (SFBA) the USGS has developed a 3D geologic/seismic model. Simulations of large magnitude (M) 7 Hayward Fault scenario earthquakes show dramatic differences in peak ground motions across the fault, arising from wavespeed differences, which highlights the need to evaluate the model. Our comparison of observed and simulated waveforms from M 4 SFBA earthquakes reveals bias in the travel times of direct S-waves for paths crossing the East Bay Hills, east of Hayward Fault, suggests that the shear wavespeeds in the upper crust of the USGS 3D model are too low, resulting in large delay times of S-wave arrivals at the East Bay Hills. We also retrieved noise-derived Green's functions (NGFs) and examined the path-specific bias in the USGS 3D model. We systematically compared synthetic waveforms with the 3D velocity model and observed NGFs. Our result shows that the time lags between the synthetic and observed data ranging from 2-5 seconds for the ray paths propagating the East Bay Hills. This result also suggests that the SFBA 3D model generates arrivals that are too slow. Additionally, we evaluate the Rayleigh-wave ellipticity to evaluate the subsurface velocity beneath seismic stations. A lateral variation in Rayleigh-wave ellipticity across the Hayward fault is observed, which will contribute to refine the SFBA 3D velocity model.

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 OSTI2019

Benchmarking Current and Emerging Approaches to Infrasound Signal Classification

Low frequency sound ≤ 20 Hz, known as infrasound, is generated by a variety of natural and anthropogenic sources. Following an event, infrasonic waves travel through a dynamic atmosphere that can change on the order of minutes. This makes infrasound event classification a difficult problem as waveforms from the same source type can look drastically different. Event classification usually requires ground truth information from seismic or other methods. This is time consuming, inefficient, and does not allow for a classification if the event locates somewhere other than a known source, the location accuracy is poor, or ground truth from seismic data is lacking. Here we compare the performance of the state of the art for infrasound event classification, support vector machine (SVM), to the performance of a convolutional neural network (CNN), a method that has been proven in tangential fields such as seismology. For a 1-class catalog consisting of only volcanic activity and earthquake events, the 4-fold average SVM classification accuracy is 75%, while it is 74% when using a CNN. Classification accuracies from the 4-class catalog consisting of the most common infrasound events detected at the global scale are 55% and 56% for the SVM and CNN architectures, respectively. These results demonstrate that using a CNN does not increase performance for infrasound event classification. This suggests that SVM should be the preferred classification method as it is a simpler and more trustworthy architecture and can be tied to the physical properties of the waveforms. The SVM and CNN algorithms described in this paper are not yet generalizable to other infrasound event catalogs. We anticipate this study to be a starting point for the development of large and comprehensive, systematically labeled, infrasound event catalogs as such catalogs will be necessary to provide an increase in the value of deep learning on event classification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Acoustic Source Parameters and Yield Estimation for SPE Phase I and Forensics Surface Events

Acoustic signals recorded for explosive events are sensitive to pressure changes in the source region and can provide valuable information about source characteristics. However, acoustic wave propagation in the atmosphere is significantly affected by weather conditions (e.g., temperature, pressure, and wind), and the atmospheric propagation effects should be considered properly to obtain accurate source information. Acoustic waveform inversion method has been increasing useful to determine quantitative source parameters by incorporating accurate Green’s function into the inversion and by effectively removing propagation path effects. This paper reviews recent progress in the acoustic waveform inversion and its application to explosion yield/emplacement (e.g., height-of-burst or depth-of-burial) estimation. The inversion method is applied to acoustic signals recorded during Source Physics Experiment Phase I and Forensics Surface Event conducted at the Nevada National Security Site (NNSS). Inverted acoustic source strengths are related to the explosion yield and emplacement, and their estimation uncertainties are explored by the comparison with the ground-truth data.

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

Improved Fireset Acceptance Using Established Evaluation Measurements

As new insight is gained into the relationship between firesets and detonators, tighter control of the circuit parameters is desired to maintain consistent and optimum system performance. An analysis of the established acceptance method reveals that it has insufficient precision to guarantee a uniform population of units. While the production has been historically very uniform, the uniformity has been primarily due to the quality and consistency of the manufacturing process rather than to the stringency of the acceptance criteria. The established method of evaluating and accepting firesets for the stockpile has been to discharge the units into a representative load and to record the output current waveform. Three characteristics of the output waveform (Peak Current, Rise Time, and Pulse Width) are then analyzed and an acceptance window is applied directly to these measurements. The purpose of this paper is to introduce a new approach that incorporates these same established measurements. However, instead of applying acceptance criteria to the three measurements directly, rather these measurements are used to evaluate the circuit values of capacitance, inductance and resistance to provide a more stringent and uniform acceptance criteria.

42 ENGINEERING↗
DOE OSTI2020

Coherent Processing of Up/Down Linear Frequency Modulated Chirps

A useful and popular waveform for high-performance radar systems is the Linear Frequency Modulated (LFM) chirp. The chirp may have a positive frequency slope with time (up-chirp) or a negative frequency slope with time (down-chirp). There is no inherent advantage to one with respect to the other, except that the receiver needs to be matched to the proper waveform. However, if up-chirps and down-chirps are employed on different pulses in the same Coherent Processing Interval (CPI), then care must be taken to maintain coherence in the range-compressed echo signals. We present the mathematics for doing so, for both correlation processing and stretch processing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Recovery and calibration of legacy analog data from the Leo Brady Seismic Network for the Source Physics Experiment

The Leo Brady Seismic Network (LBSN) was established in 1960 by Sandia National Laboratories for monitoring underground nuclear tests (UGTs) at the Nevada Test Site— renamed in 2010 to the Nevada National Security Site (NNSS). The LBSN has been in various configurations throughout its existence, but it has been generally comprised of four to six stations at regional distances from the NNSS with evenly spaced azimuthal coverage. Between 1962 and the early 1980s, the LBSN—and a sister network operated by Lawrence Livermore National Laboratory—were the most comprehensive U.S. source of regional seismic data of UGTs. During the pre-digital era, LBSN data were transmitted as frequency-modulated (FM) audio over telephone lines to the NTS and recorded in analog on hi-fi 8-track AMPEX tapes. These tapes have been stored in temperature-stable buildings or bunkers on the NNSS and Kirtland Air Force Base in Albuquerque, NM for decades and contain the sole record of this irreplaceable data from the analog era; full waveforms of UGTs during this time were never routinely converted to digital form. We have been developing a process over the past few years to recover and calibrate data from these tapes, converting them from FM audio to digital waveforms in ground motion units. The calibration of legacy data from the LBSN is still ongoing. To date, we have digitized tapes from 592 separate UGTs. As a proof-of-concept, we calibrated data from the BOXCAR event.

58 GEOSCIENCES↗
DOE OSTI2019

Integrated Geomechanics and Geophysics in Induced Seismicity: Mechanisms and Monitoring

Quantifying in-situ subsurface stresses and predicting fracture development are critical to reducing risks of induced seismicity and improving modern energy activities in the subsurface. In this work, we developed a novel integration of controlled mechanical failure experiments coupled with microCT imaging, acoustic sensing, modeling of fracture initiation and propagation, and machine learning for event detections and waveform characterization. Through additive manufacturing (3D printing), we were able to produce bassanite-gypsum rock samples with repeatable physical, geochemical and structural properties. With these "geoarchitected" rock, we provided the role of mineral texture orientation on fracture surface roughness. The impact of poroelastic coupling on induced seismicity has been systematically investigated to improve mechanistic understanding of post shut-in surge of induced seismicity. This research will set the groundwork for characterizing seismic waveforms by using multiphysics and machine learning approaches and improve the detection of low-magnitude seismic events leading to the discovery of hidden fault/fracture systems.

42 ENGINEERING↗
DOE OSTI2021

2021 Summer SPE Project

SPE is a project in order to develop new, more physics-based, seismic models of explosions (see Nelson et al. 2013). One key component of this effort is numerical modeling enabled by modern state-of-art code/software. Accurate modeling of the shape and amplitude of seismic waves from their generation to their propagation to remote monitoring seismic stations is important to our ability to determine the origin and strength of the source from remote recording. In this project, the modeling is performed by coupling two codes HOSS and SPECFEM3D. HOSS models the dynamic nonlinear processes happening near the explosion. SPECFEM3D computes the propagation of seismic waves as they travel through 3D complex Earth models. SPECFEM3D was modified in order to be driven by a set of time-series calculated by HOSS in lieu of a seismic source. The goal of this summer project is the investigation of several questions pertinent to the establishment of a full end-to-end modeling capability from the high-rate strain regime area to remote distances where seismic station record seismic waves generated by explosions. The investigated questions are: (1) How to perform proper filtering? Direct-solution modeling only sustains a limited range of frequency depending on the grid size. As we go from one modeling domain to the other via coupling, the mesh size is getting coarser to allow modeling at large scale but also to account for the fact that high-frequency waves do not physically travel to large distances. So filtering of the time-series generated by the near-field hydrodynamic modeling is a current practice often employed but its effects on the modeled waveforms has to be investigated. (2) Quantitative assessment of the efficiency of attenuation to remove high-frequency content of the wavefield. This assessment will allow to create meshes with a grid size appropriate to the actual physics of wave propagation for a given explosion. (3) Checking that the coupling process and the two codes respect the cylindrical symmetry that is expected in the case of a pure explosion in a half-space. (4) The effect of the state-of-stress in the near-source area on the modeled seismic waveforms. These questions will be investigated through the modeling of SPE-4P, the fourth explosion of this series because it was designed to have little interaction with the geologic surrounding and the free surface of the Earth so that it is the most explosion-like experiment, with the most symmetries to be verified.

58 GEOSCIENCES↗
DOE OSTI2021

Seismic Array Analysis Using Fiber-Optic Distributed Acoustic Sensing on Small Local and Regional Earthquakes

We examined the feasibility of using fiber-optic Distributed Acoustic Sensing (DAS) as an alternative to a traditional seismic array. Seismic arrays are used routinely globally to measure waveform propagation parameters and signal features including back-azimuth (BAZ) and apparent horizontal velocity through the process of beamforming or frequency-wavenumber (f-k) array analysis with the assumption that the signals travel across the array as a plane-wave. These measurements are useful for identifying signal detections as teleseismic, regional, or local distance seismic phases based on their velocities traveling across the array (e.g., Rost and Thomas, 2001). Signal enhancement is the main benefit of array processesing through the stacking of multiple channels as a phased array. This enhancement increases as the square root of the number of channels relative to a single seismic station (e.g., Rost and Thomas, 2001), which is a factor of 3 from a typical nine-channel array. DAS provides the potential of stacking waveforms from 100’s to 1000’s of channels. However, DAS is a novel technology designed for geophysical exploration and therefore has some limitations which we will explore. We started with the dataset from the PoroTomo project because of its unique experimental layout of colocated geophone array and DAS deployment so the two recording technologies can be compared side-by-side.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluating Scalograms for Seismic Event Denoising

Denoising contaminated seismic signals for later processing is a fundamental problem in seismic signals analysis. The most straightforward denoising approach, using spectral filtering, is not effective when noise and seismic signal occupy the same frequency range. Neural network approaches have shown success denoising local signal when trained on short-time Fourier transform spectrograms (Zhu et al 2018; Tibi et al 2021). Scalograms, a wavelet-based transform, achieved ~15% better reconstruction as measured by dynamic time warping on a seismic waveform test set than spectrograms, suggesting their use as an alternative for denoising. We train a deep neural network on a scalogram dataset derived from waveforms recorded by the University of Utah Seismograph Stations network. We find that initial results are no better than a spectrogram approach, with additional overhead imposed by the significantly larger size of scalograms. A robust exploration of neural network hyperparameters and network architecture was not performed, which could be done in follow on work.

58 GEOSCIENCES↗