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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Flexible approach to vibrational sum-frequency generation using shaped near-infrared light

We describe a new approach that expands the utility of vibrational sum-frequency generation (vSFG) spectroscopy using shaped near-infrared (NIR) laser pulses. Here, we demonstrate that arbitrary pulse shapes can be specified to match experimental requirements without the need for changes to the optical alignment. In this way, narrowband NIR pulses as long as 5.75 ps are readily generated, with a spectral resolution of about 2.5 cm -1 , an improvement of approximately a factor of 3 compared to a typical vSFG system. Moreover, the utility of having complete control over the NIR pulse characteristics is demonstrated through nonresonant background suppression from a metallic substrate by generating an etalon waveform in the pulse shaper. The flexibility afforded by switching between arbitrary NIR waveforms at the sample position with the same instrument geometry expands the type of samples that can be studied without extensive modifications to existing apparatuses or large investments in specialty optics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hearing carrier-envelope offset frequency and phase in air with a microphone

Attosecond science and frequency metrology rely on the precise measurement and control of the laser pulse waveform, a feat traditionally achieved using optoelectronic techniques. In this study, we conducted a laser-induced acoustic experiment in air ionized by carrier-envelope phase (CEP)-stabilized sub-4 fs pulses. Our results reveal that the acoustic signal exhibits CEP dependence in few-cycle pulses, primarily through amplitude modulation from laser-driven ionization. This optoacoustic phenomenon is, to the best of our knowledge, a novel phenomenon that enables not only the measurement of the carrier-envelope offset frequency but also the direct characterization of the waveform of optical pulses through a microphone. Our study highlights the potential of laser-induced acoustic waves for advancing ultrafast science.

Han, Meng [Kansas State University, Manhattan, KS ↗
DOE OSTI2022

Directional Cooling-Induced Fracturing: Directional Cooling Data Under True-Triaxial Stress State

Data includes Directional Cooling-Induced Fracturing (DCIF) testing data using westerly granite blocks. This submission includes data from two samples of westerly granite, lab sample 7 and 8. Files contain stress, temperature and acoustic emission data acquired during polyaxial, laboratory testing of westerly granite blocks for each sample. FILES: .tradb -- files containing acoustic emission waveforms; sqlite3 database .pridb -- files containing basic acoustic emission information (no waveforms); sqlite3 database .geom -- geometry of the AE sensor network (ASCII)

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Moment Magnitudes of Local/Regional Events from 1D Coda Calibrations in the Broader Middle East Region [Moment Magnitudes of Small to Moderate Size Regional Events from Coda Calibration in the Broader Middle East]

Reliable moment magnitude estimates for seismic events in the Middle East region can be difficult to obtain due to the uneven distribution of stations, the complex tectonic structure, and regions of high attenuation. In this paper, we take advantage of the many new broadband seismic stations that have become available through improved national networks and numerous temporary deployments. We make coda envelope-amplitude measurements for 2247 events recorded by 68 stations over 13 narrow frequency bands ranging between 0.03 and 8 Hz. The absolute scaling of these spectra was calculated based on independent waveform modeling solutions of the moment magnitudes for a subset of these events to avoid circularity. Using our 1D path calibrations, we determined coda-based magnitudes for a majority of the events. We obtain fairly good agreement with waveform-modeled seismic moments for the larger events (Mw>4.5) at low frequencies (<0.7 Hz). As expected, the coda-derived source spectra become increasingly scattered at higher frequencies (>0.7 Hz) because of unaccounted 2D path effects, as well as mixing of both Sn coda and Lg coda, which have different attenuation behavior. This scatter leads to increased variance in the magnitudes estimated for smaller events in which low-frequency amplitudes are below the noise levels and the higher frequencies are the only signals available. We quantify the expected variance in coda envelope amplitudes as a function of frequency using interstation scatter as our metric. Finally, the net results of this study provide thousands of new 1D coda magnitude estimates for events in the broad region, as well as the necessary initial starting model for use in a new related 2D coda study (Pasyanos et al., 2016).

58 GEOSCIENCES↗
DOE OSTI2019

Microseismic Focal Mechanisms and Implications for Changes in Stress during the 2014 Newberry EGS Stimulation

We adapt the relative polarity method from Shelly et al. (2016) to compute focal mechanisms for microearthquakes associated with the 2014 hydroshearing stimulation at the Newberry volcano geothermal site. We focus the analysis on events relocated by Aguiar and Myers (2018), who report that six event clusters predominantly comprise the 2014 sequence. Data quality allows focal mechanism analysis for four of the six event clusters. We use Hardebeck and Shearer (2002, 2003; hereafter HASH) to compute focal mechanisms based on first–motion polarities and S/P amplitude ratios. We manually determine P– and S–wave polarities for a well–recorded reference event in each cluster, then use waveform cross correlation to determine whether recordings of other events in the cluster are the same or reversed polarity at each network station. Here, most waveform polarities are consistent with the affiliated reference event, indicating similar focal mechanisms within each cluster. The deeper clusters are east–west–striking normal faults, whereas the shallower clusters, close to the top of the open–hole section of the borehole, are strike slip with east–west motion. Regional studies and prestimulation borehole breakouts find the maximum stress direction is vertical and maximum horizontal stress is approximately north–south. Fault geometry and focal mechanisms of microseismicity during the stimulation suggest that increased pressure from fluid injection predominantly caused changes in horizontal stress, consistent with predictions from numerical studies of stress change caused by fluid injection. At shallow depths, where previous studies suggest the difference between vertical and horizontal stress is lowest, injection appears to have rotated the direction of maximum stress from vertical to horizontal, resulting in strike–slip motion. At greater depth, vertical stress continued to be the dominant direction during the stimulation, but fault orientation indicates either reactivation of pre–existing fractures or rotation of the direction of maximum horizontal stress from approximately north–south to east–west.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Empirical Acoustic Source Model for Chemical Explosions in Air

Chemical explosions generate pressure disturbances in air that radiate as nonlinear shock waves near the source and transition into acoustic waves with distance. Because low-frequency acoustic waves generally travel large distances without significant loss of energy, they are often used for explosion monitoring and yield estimation. However, quantitative relationships between acoustic energy and explosion yields are required for accurate yield estimation. Here, we develop an empirical acoustic source model for chemical explosions from experimental data. The empirical model returns the acoustic pressure waveform for the detonation of 1 kg of trinitrotoluene, which is conventionally used to represent the explosive release of 4.184 MJ of explosion energy. The full-waveform model can be used to predict acoustic signals for an arbitrary yield of a high-explosive detonation based on the standard scaling law and to estimate acoustic energies in a specific frequency range. We evaluate the accuracy of the acoustic source model independently by estimating the yield of other explosive events that are not included in the model development. Finally, statistical characteristics of the model and their implications for the uncertainty quantification of estimated yields are discussed.

58 GEOSCIENCES↗
DOE OSTI2021

Rayleigh Wave Propagation in the Bighorn Mountains Region, Wyoming

Short-period Rayleigh waves, Rg , provide strong constraints on the depth of shallow seismic events and are of interest for monitoring small explosions. Characterizing the seismic sources that generate Rg requires an understanding of how shallow crustal structure affects Rayleigh wave propagation. Here, in support of these efforts, this study utilizes observed waveforms from small shallow explosions recorded on temporary seismic network deployments in the Bighorn region, Wyoming. We study regional near-surface geology by measuring changes in surface-wave amplitude and polarization during propagation through basins, foothills, and mountains. We develop additional insight by carrying out surface-wave eigenfunction analyses and numerical-wave simulations, which together reproduce many characteristics seen in the observed waveforms. Our results show how sedimentary basins in the Bighorn region allow for amplified prograde-polarized higher-mode and retrograde-polarized fundamental-mode Rayleigh waves, whereas adjacent mountains only support retrograde motion. These different modes provide distinct constraints on the Earth structure and source characteristics, potentially enabling targeted inversions in future studies. Finally, our findings provide insight into Rg propagation through complex near-surface geology, improving our understanding of shallow propagation and source effects that are relevant to explosion monitoring efforts.

58 GEOSCIENCES↗
DOE OSTI2023

Numerical Modeling of Air-Blast Suppression as a Function of Explosive-Charge Burial Depth

As a chemical explosion is buried, the mechanism for acoustic wave generation transitions from fully gas-generated at the surface to completely spall-induced at full containment depth. The fully gas-generated and completely spall-induced signals in the acoustic waveform are well described; however, the transition between these two end-members eludes numerical modeling because of the complex phenomena that are involved. The phenomena of crater formation and explosive cloud evolution are simulated using an Eulerian hydrocode that incorporates geomaterials with strength and porosity. Having accurately modeled these phenomena, we can confidently predict the propagation and relative strength of the gas-generated and spall-induced pulses in the recorded acoustic waveform. The numerical predictions agree with observations from the historical Stagecoach experiment as well as modern recordings from the Source Physics Experiment. In particular, the peak pressure p generated by an explosion is initially due to the gas-generated mechanism and decays with scaled depth of burial d s (depth d scaled by the cube-root of explosive yield w 1/3 ) as exp(-d s ) but then transitions near a scaled depth of 6 m/ton 1/3 to the spall-generated mechanism in which the decay is d$_{s}^{-7/4}$. This decay form is related to the strong ground-motion attenuation relationship that affects spall strength. So these results can improve seismoacoustic inverse models for the explosive source that need to account for the gas-generated and spall-induced signals and their effect on peak pressures and other acoustic signal features.

58 GEOSCIENCES↗
DOE OSTI2024

Wasatch Fault Structure from Machine Learning Arrival Times and High-Precision Earthquake Locations

Abstract On 18 March 2020, a magnitude 5.7 earthquake hit the Salt Lake valley in the state of Utah, United States. Using a dense geophone deployment and machine learning (ML), an additional several thousand events were detected and located. Currently, both the mainshock and the majority of the aftershocks are suspected to have occurred on or near a deeper portion of the Salt Lake segment of the Wasatch fault—part of a large range-bounding fault system thought to be capable of generating an Mw 7.2 earthquake. However, a small subset of aftershocks may have occurred on a portion of the more steeply, eastward dipping, and poorly understood West Valley fault. Unfortunately, the catalog locations and lack of focal mechanisms for this subset of aftershocks provide only a crude constraint on the true fault structure. To better illuminate fault structure, we relocate the ML-generated catalog with a range of magnitudes from −2 to 4.6, using: (1) NonLinLoc, a nonlinear location algorithm, (2) source-specific station terms, and (3) waveform coherence. We further compute first-motion focal mechanisms for 68 events. Results of the relocation suggest a simpler, minimally listric Wasatch fault geometry, contrary to what has been previously proposed. We also find that analysis of the focal mechanisms and waveform similarity indicates minimal event similarity throughout the Magna sequence, suggesting a highly complex and heterogeneous rupture zone, as opposed to rupture on a single plane. These findings suggest an increased seismic hazard due to the overall shallowness of the earthquake sequence and highly varied rupture mechanisms.

Geochemistry & Geophysics↗
DOE OSTI2024

Using a 1D Radially Symmetric Coda Envelope Model for Robust Moment Magnitude ( M w ) Estimation in Iraq’s Tectonically Diverse Zones

Robust estimation of moment magnitude (M w ) can be challenging for Iraq due to the strong lateral heterogeneity across diverse tectonic zones. We aim to improve moment magnitude estimation by investigating the reliability of using a 1D coda envelope model in diverse tectonic zones of different lateral effects and offer a way forward for reliable estimates of M w for small events that are difficult to waveform model. Iraq comprises two main tectonic zones: (1) the Outer platform, consisting of the northwestern Zagros fold-thrust belt and the Mesopotamian foredeep, and (2) the Inner Arabian platform which is overlain by the Iraqi desert. A simple 1D coda envelope model was used because coda waves have a low sensitivity to the source and path heterogeneity. Three separate coda calibrations were conducted to investigate the robustness of a single 1D calibration to fit the country: Whole-region calibration, Zagros calibration, and Mesopotamia calibration. In the whole-region calibration, we used stations from both the Zagros and Mesopotamia zones. In the two other calibration models, we used only stations that were in those particular zones. Ground-truth reference spectra derived from the coda spectral ratio method were used to constrain high-frequency site terms. There was no drastic difference when comparing the moment magnitudes calculated from the waveform modeling and the three calibration models. The results show that the 1D coda envelope model is a reliable method even for a region with diverse tectonic zones. Therefore, we recommend using the whole-region calibration model for moment magnitude estimation that provides more complete path coverage and avoids biases introduced by path correction failures. The proposed calibration is a fundamental step in updating the comprehensive earthquake catalog and probabilistic seismic hazard assessments for Iraq.

58 GEOSCIENCES↗
DOE OSTI2018

The Coupled Location/Depth/Yield Problem for North Korea’s Declared Nuclear Tests

On 3 September 2017, the Democratic People's Republic of Korea (DPRK) conducted its sixth and largest declared nuclear test at the Pung'gye Ri test site. Recently, we have been using regional waveform envelopes to estimate the explosive yield and overburden of chemical and nuclear explosions by coupling explosion source models to propagation parameters. Like most yield determination methods, there can be tradeoffs between yield and depth, leading to uncertainties in both parameters. The relative locations are well-constrained by small timing differences in seismic-phase arrivals at stations that recorded multiple events, but there are potential uncertainties on the absolute locations. Depths are poorly constrained by the relative arrival times. In this study, we have performed a coupled location and yield analysis of the DPRK nuclear tests. We use correlation methods to get highly accurate travel times and relative locations using a Bayesian location method. Then, while keeping the relative locations of the six tests constant, we consider the consequences of shifts to the absolute locations on the resulting overburden for each event. Given that overburden, we determine the yield that minimizes the waveform misfit. We also test and compare a number of explosion source models. By considering the coupled location/depth/yield problem, we reduce uncertainties in the absolute locations, and yield and depth estimates of the events. Based on statistical analysis, we estimate that the 2017 test has a yield of 125 kilotons (equivalent TNT) with a 1 sigma uncertainty range of 103-150 ktons at about 600 m of overburden.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2018

Unsupervised Dictionary Learning for Signal-to-Noise Ratio Enhancement of Array Data

Waveform enhancement methods generally explore lateral coherency in arrivals, often assuming a linear moveout across an array, as exhibited by plane waves. Here, we illustrate how unsupervised dictionary learning combined with orthogonal matching pursuit for feature extraction can be used for signal-to-noise ratio (SNR) enhancement. In this strategy, waveform characteristics are directly learned from provided data samples; the created dictionary is then used for signal extraction. This combination prevents the need to set a predefined dictionary, and it becomes computationally efficient because learning is only done on smaller data portions. Because the dictionary is learned from data, there is no assumption regarding wavefront shape or form. Tests on synthetic and field data demonstrate the better denoising performance in terms of SNR enhancement compared to other methods.

58 GEOSCIENCES↗
DOE OSTI2019

Pairwise Association of Seismic Arrivals with Convolutional Neural Networks

Correctly determining the association of seismic phases across a network is crucial for developing accurate earthquake catalogs. Nearly all established methods use travel time information as the main criterion for determining associations, and in problems in which earthquake rates are high and many false arrivals are present, many standard techniques may fail to resolve the problem accurately. As an alternative approach, in this work we apply convolutional neural networks (CNNs) to the problem of associations; we train CNNs to read earthquake waveform arrival pairs between two stations and predict the binary classification of whether the two waveforms are from a common source or different sources. Applying the method to a large training dataset of previously cataloged earthquakes in Chile, we obtain >80% true positive prediction rates for high-frequency data (>2 Hz) and stations separated in excess of 100 km. As a secondary benefit, the output of the neural network can also be used to infer predicted phase types of arrivals. Here, the method is ideally applied in conjunction with standard travel-time-based association routines and can be adapted for arbitrary network geometries and applications, so long as sufficient training data are available.

58 GEOSCIENCES↗
DOE OSTI2023

A Mitigation Strategy for the Prediction Inconsistency of Neural Phase Pickers

Neural phase pickers—neural networks designed and trained to pick seismic phase arrivals—have proven to be a powerful tool for developing earthquake catalogs. However, these pickers suffer from prediction inconsistency in which the results they produce change, sometimes substantially, even under a small perturbation to the input waveform. This problem has not been addressed by the developers and users of these pickers. In this study, we show how prediction inconsistency can negatively affect the completeness of earthquake catalogs developed using neural phase pickers. Further, we show that simply using a small step size for the sliding window when processing continuous waveform data and aggregating the results significantly mitigates this problem. We also highlight the importance of training datasets for increasing the consistency and other performance metrics.

58 GEOSCIENCES↗
DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗
DOE OSTI2026

The 10 September 2025 M w 4.1 Earthquake in Northeastern Utah, United States: An Archetypal Continental Mantle Event

The 10 September 2025 M w 4.1 earthquake in northeastern Utah, United States, had a focal depth 68 km beneath sea level, which is ∼20–25 km greater than estimates of local crustal thickness, making it a rare example of a continental mantle earthquake (CME). The focal depth is well resolved from arrival-time inversion (nearest station ∼13 km away) and moment tensor inversion of regional waveforms. Similar to other CMEs in the Intermountain West, there were no obvious aftershocks or foreshocks, and the waveforms were enriched in high-frequency energy. Spectral modeling gives a stress drop of ∼80 MPa and a radiation efficiency of ∼0.08, albeit with large uncertainties. The high stress drop and low radiation efficiency are consistent with a dissipative source process such as thermal runaway. Also similar to previous Intermountain West CMEs, the event occurred along the boundary of the Archean Wyoming craton, where pressure–temperature conditions favor ductile deformation. We hypothesize that edge-driven or regional-scale mantle convection produces increased strain rates near the craton boundary that make either conventional brittle failure or thermal runaway feasible at relatively high pressure–temperature conditions. High conductivity inferred around the edge of the craton may suggest that fluids also contribute to CME occurrence.

Koper, Keith D. [Univ. of Utah, Salt Lake City, UT↗
DOE OSTI2018

Studies of CSR and Microbunching at Jefferson Laboratory ERLs

One attractive feature of energy recovery linacs (ERLs) is they are source limited. However as beam brightness increases so too do the effects of coherent synchrotron radiation (CSR) and the microbunching instability. The Low Energy Recirculator Facility at Jefferson Laboratory provides a test bed to characterize aspects of CSR's effect on the beam by measuring the energy extraction via CSR as a function of bunch compression. Data was recorded with acceleration occurring on the rising part of the RF waveform while the full compression point was moved along the backleg of the machine and the response of the beam measured. Acceleration was moved to the falling part of the RF waveform and the experiment repeated. Initial start-to-end simulations using a 1D CSR model show good agreement with measurements. The experiment motivated the design of a modified Continuous Electron Beam Accelerator Facility-style arc with control of CSR and the microbunching gain. Insights gained from that study informed designs for recirculation arcs in an ERL-driven electron cooler for Jefferson Laboratory's Electron Ion Collider. Progress on the design and outstanding challenges of the cooler are discussed.

Tennant, C.↗
DOE OSTI2020

Initial Implementation Of Machine Learning System for SRF Cavity Fault Classification at CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is high power CW electron accelerator. It uses a mixture of two types of SRF modules, older, lower energy C20/C50 modules and newer higher energy C100 modules, arrayed in two anti-parallel linear accelerators. Accurately classifying the type of cavity faults is essential to maintaining and improving accelerator performance. Each C100 cryomodule contains eight 7-cell cavities. When a cavity fault occurs within a cryomodule, all eight cavities generate 17 waveforms each containing 8192 points. This data is exported from EPICS and saved for review. Analysis of these waveforms is time intensive and requires subject matter expertise (SME). SMEs examine the data from each event and label it according to one of several known cavity fault types. Multiple machine learning models have been developed on this labeled dataset with sufficient performance to warrant the creation of a limited machine learning software system for use by accelerator operations staff. This paper discusses the transition from model development to implementation of a prototype system.

Carpenter, A.↗