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At least 919 records · Page 51

Single-shot electro-optic sampling with arbitrary terahertz polarization

With the recent development of diversity electro-optic sampling (DEOS), significant progress has been made in the range of applicability of single-shot EOS measurements, allowing broadband THz waveforms to be captured in a single shot over large temporal windows. In addition to the decrease in acquisition time compared to standard multishot data acquisition, this technique allows measurements on systems far from equilibrium with large shot-to-shot noise or with irreversible or poorly repeatable dynamics. Although DEOS has been demonstrated and verified for linearly polarized THz waveforms, we investigate the effects resulting from the presence of a secondary polarization component. This imposes new challenges for accurate waveform reconstruction, and opens the opportunity to measure out complex polarization states such as arbitrary elliptically polarized THz field. We demonstrate a single-shot diversity-electro-optic-sampling-based approach to capture both x- and y-THz fields simultaneously with a single (110)-cut EO crystal for THz polarimetry and ellipsometry over a wide range of frequencies.

Lenz, Maximilian [University of California, Los An↗

International Monitoring System Correlation Detection at the North Korean Nuclear Test Site at Punggye-ri with Insights from the Source Physics Experiment

Seismic waveform correlation offers the prospect of greatly reducing event detection thresholds when compared with more conventional processing methods. Correlation is applicable for seismic events that in some sense repeat, that is they have very similar waveforms. A number of recent studies have shown that correlated seismic signals may form a significant fraction of seismicity at regional distances. For the particular case of multiple nuclear explosions at the same test site, regional distance correlation also allows very precise relative location measurements and could offer the potential to lower thresholds when multiple events exist. Using the Comprehensive Nuclear-Test-Ban Treaty (CTBT) International Monitoring System (IMS) seismic array at Matsushiro, Japan (MJAR), Gibbons and Ringdal (2012) were able to create a multichannel correlation detector with a very low false alarm rate and a threshold below magnitude 3.0. They did this using the 2006 or 2009 Democratic People’s Republic of Korea (DPRK) nuclear explosion as a template to search through a data stream from the same station to find a match via waveform correlation. In this paper, we extend the work of Gibbons and Ringdal (2012) and measure the correlation detection threshold at several other IMS arrays. We use this to address three main points. First, we show the IMS array station at Mina, Nevada (NVAR), which is closest to the Nevada National Security Site (NNSS), is able to detect a chemical explosion that is well under 1 ton with the right template. Second, we examine the two IMS arrays closest to the North Korean (DPRK) test site (at Ussuriysk, Russian Federation [USRK] and Wonju, Republic of Korea [KSRS]) to show that similarly low thresholds are possible when the right templates exist. We also extend the work of Schaff et al. (2012) and measure the correlation detection threshold at the nearest Global Seismic Network (GSN) three-component station (MDJ) at Mudanjiang, Heilongjiang Province, China, from the New China Digital Seismograph Network (IC). To conclude, we use these results to explore the recent claim by Zhang and Wen (2015) that the DPRK conducted “…a low-yield nuclear test…” on 12 May 2010.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Recovery and Calibration of Legacy Underground Nuclear Test Seismic Data from the Leo Brady Seismic Network

The Leo Brady Seismic Network (LBSN, originally the Sandia Seismic Network) was established in 1960 by Sandia National Laboratories to monitor underground nuclear tests (UGTs) at the Nevada National Security Site (NNSS, formerly named the Nevada Test Site). The LBSN has been in various configurations throughout its existence, but it has generally been comprised of four to six stations at regional distances (~150–400 km) from the NNSS with approximately evenly spaced azimuthal coverage. Between 1962 and the end of nuclear testing in 1992, the LBSN—and a sister network operated by Lawrence Livermore National Laboratories—was the most comprehensive United States source of regional seismic data of UGTs. Approximately 75% of all UGTs performed by the United States occurred in the predigital era. At that time, LBSN data were transmitted as frequency-modulated (FM) audio over telephone lines to a central location and recorded as analog waveforms on high-fidelity magnetic audio tapes. These tapes have been in dry temperature-stable storage for decades and contain the sole record of this irreplaceable data; full waveforms of LBSN-recorded UGTs from this era were not routinely digitized or otherwise published. We have developed a process to recover and calibrate data from these tapes. First, we play back and digitize the tapes as audio. Next, we demodulate the FM “audio” into individual waveforms. We then estimate the various instrument constants through careful measurement of “weight-lift” tests performed prior to each UGT on each instrument. Finally, these coefficients allow us to scale and shape the derived instrument response of the seismographs and compute poles and zeros. Finally, the result of this process is a digital record of the recorded seismic ground motion in a modern data format, stored in a searchable database. To date, we have digitized tapes from 592 UGTs.

58 GEOSCIENCES↗

High-Precision Characterization of Seismicity from the 2022 Hunga Tonga-Hunga Ha'apai Volcanic Eruption

The earthquake swarm accompanying the January 2022 Hunga Tonga-Hunga Ha'apai (HTHH) volcanic eruption includes a large number of posteruptive moderate-magnitude seismic events and presents a unique opportunity to use remote monitoring methods to characterize and compare seismic activity with other historical caldera-forming eruptions. We compute improved epicentroid locations, magnitudes, and regional moment tensors of seismic events from this earthquake swarm using regional to teleseismic surface-wave cross correlation and waveform modeling. Precise relative locations of 91 seismic events derived from 59,047 intermediate-period Rayleigh- and Love-wave cross-correlation measurements collapse into a small area surrounding the volcano and exhibit a southeastern time-dependent migration. Regional moment tensors and observed waveforms indicate that these events have a similar mechanism and exhibit a strong positive compensated linear vector dipole component. Precise relative magnitudes agree with regional moment tensor moment magnitude ($M_w$) estimates while also showing that event sizes and frequency increase during the days after the eruption followed by a period of several weeks of less frequent seismicity of a similar size. The combined information from visual observation and early geologic models indicate that the observed seismicity may be the result of a complex series of events that occurred after the explosive eruption on 15 January, possibly involving rapid resupply of the magma chamber shortly after the eruption and additional faulting and instability in the following weeks. In addition, we identify and characterize an $M_w$ 4.5 event five days before the paroxysmal explosion on 15 January, indicating that additional seismic events preceding the main eruption could have been identified with improved local monitoring. As a result, our analysis of the HTHH eruption sequence demonstrates the value of potentially utilizing teleseismic surface-wave cross correlation and waveform modeling methods to assist in the detailed analysis of remote volcanic eruption sequences.

58 GEOSCIENCES↗

Chemical Blast Standard (1 kg)

Chemical explosions create blast waves with large overpressure disturbances. It is important to develop a standard blast model based on data to accurately predict acoustic blast-wave amplitudes near detonations and invert for explosion energy from distant observations of blast-wave signals. However, open data from large, controlled chemical explosions with reliable ground truth can be challenging to find. The lack of access to such data could limit the number of contributions to related research and potentially stifle the rate of discoveries or validation of existing models. Here, to address these data scarcity problem, we have curated and compiled a standardized set of 817 blast-wave waveforms from 19 distinct high-explosive events. The blast-wave waveforms are standardized to a 1 kg trinitrotoluene explosion using scaling laws and corrections for location effects. A brief overview of the dataset is presented along with explosion feature models as well as recommendations for extracting explosion features. The resulting dataset is distributed to an open repository in both Seismic Analysis Code and pandas DataFrame formats containing the waveforms, the scaled distances, and the sample rates.

58 GEOSCIENCES↗

Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms

The discrimination problem in seismology aims to accurately classify different underground source types based on local, regional, and/or teleseismic observations of ground motion. Typical discriminant approaches are rooted in fundamental, physics-based differences in radiation pattern or wave excitation, which can be frequency-dependent and may not make use of the full waveform. In this article, we explore whether phase and amplitude distances derived from dynamic time warping (DTW) and elastic shape analysis (ESA) can inform event discrimination. We demonstrate the ability to distinguish underground point sources using synthetic waveforms calculated for a 1D Earth model and various source mechanisms. We then apply the method to recorded data from events in the Korean Peninsula, which includes declared nuclear explosions, a collapse event, and naturally occurring earthquakes. Phase and amplitude distances derived from DTW and ESA are then used to classify the event types via dendrogram and k-nearest-neighbor clustering analyses. Using information from the full waveform, we show how different underground sources can be distinguished at regional distances. We highlight the potential of these nonlinear alignment algorithms for discrimination and comment on ways we can extend the framework presented here.

58 GEOSCIENCES↗

PERLE: A High Power Energy Recovery Facility

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator to make use of SRF accelerating structures. The structures are configured in two anti-parallel linacs connected by arcs. Each linac consists of twenty C20/C50 cryomodules each containing eight 5-cell cavities and five C100 upgrade cryomodules each containing eight 7-cell cavities. Accurately classifying the source of cavity faults is critical for improving accelerator performance. A cavity fault triggers a waveform acquisition process where 17 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the affected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. This data combined with archived signals sampled at 10 Hz are used to classify faults. Significant time is required for a subject matter expert to analyze and identify the intra-cavity signatures of imminent faults. This paper describes a path forward that utilizes machine learning for automatic fault classification. Post-training identification of the physical origins of faults are discussed, as are potential machine-trained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance

Kaabi, W.↗

SRF Cavity Fault Classification Using Machine Learning At CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator to make use of SRF accelerating structures. The structures are configured in two anti-parallel linacs connected by arcs. Each linac consists of twenty C20/C50 cryomodules each containing eight 5-cell cavities and five C100 upgrade cryomodules each containing eight 7-cell cavities. Accurately classifying the source of cavity faults is critical for improving accelerator performance. A cavity fault triggers a waveform acquisition process where 17 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the affected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. This data combined with archived signals sampled at 10 Hz are used to classify faults. Significant time is required for a subject matter expert to analyze and identify the intra-cavity signatures of imminent faults. This paper describes a path forward that utilizes machine learning for automatic fault classification. Post-training identification of the physical origins of faults are discussed, as are potential machine-trained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance

Solopova, A.↗

Use Case Realization Report: UCR-03.02.08 Compares Events

This architecturally significant use case describes how the Analyst compares events to determine how similar events were constructed. The Analyst compares waveforms from comparison events by visually inspecting an overlay of the waveforms to determine if the events are from a similar source. The Analyst searches for comparison events or creates agglomerative hierarchical clusters of waveforms from events and determines that the events are from a similar source if the correlation coefficient is above a selected threshold. This use case is architecturally significant due to the introduction of the capability to compare events within an operational context.

97 MATHEMATICS AND COMPUTING↗

Computational Approach for Improving Three-Dimensional Sub-Surface Earth Structure for Regional Earthquake Hazard Simulations in the San Francisco Bay Area

In our Exascale Computing Project (ECP) we seek to simulate earthquake ground motions at much higher frequency than is currently possible. Previous simulations in the SFBA were limited to 0.5-1 Hz or lower (Aagaard et al. 2008, 2010), while we have recently simulated the response to 5 Hz. In order to improve confidence in simulated ground motions, we must accurately represent the three-dimensional (3D) sub-surface material properties that govern seismic wave propagation over a broad region. We are currently focusing on the San Francisco Bay Area (SFBA) with a Cartesian domain of size 120 x 80 x 35 km, but this area will be expanded to cover a larger domain. Currently, the United States Geologic Survey (USGS) has a 3D model of the SFBA for seismic simulations. However, this model suffers from two serious shortcomings relative to our application: 1) it does not fit most of the available low frequency (< 1 Hz) seismic waveforms from moderate (magnitude M 3.5-5.0) earthquakes; and 2) it is represented with much lower resolution than necessary for the high frequency simulations (> 5 Hz) we seek to perform. The current model will serve as a starting model for full waveform tomography based on 3D sensitivity kernels. This report serves as the deliverable for our ECP FY2017 Quarter 4 milestone to FY 2018 “Computational approach to developing model updates”. We summarize the current state of 3D seismic simulations in the SFBA and demonstrate the performance of the USGS 3D model for a few selected paths. We show the available open-source waveform data sets for model updates, based on moderate earthquakes recorded in the region. We present a plan for improving the 3D model utilizing the available data and further development of our SW4 application. We project how the model could be improved and present options for further improvements focused on the shallow geotechnical layers using dense passive recordings of ambient and human-induced noise.

58 GEOSCIENCES↗

Seismic Analysis of Spatio-Temporal Fracture Generation During EGS Resource Development

This DOE GTO funded project was a collaborative effort between Array Information Technology and the University of California at Berkeley. The project goal is to improve technology to assess in-situ stress magnitude and orientation, kinematic fracture parameters, rupture size, rupture orientation as well as temporal changes and volumetric distribution of the injected fluid during Enhanced Geothermal System (EGS) resource development. The project leveraged high-frequency seismic data recorded by the LBNL 34-station permanent geophone network and seismic broadband data recorded by a temporary 33-station seismometer network that operated in The Geysers during the injection phase of the DOE GTO funded EGS demonstration project at the well Prati-32. The operation of the broadband network with high station density and good azimuthal coverage to monitor the injection phase produced an unprecedented dataset that is typically unavailable for EGS operations. The two datasets offer the advantages of studying the parameters under investigation over a broader frequency band and the higher sensitivity of the broadband seismometers resulted in a richer dataset with a lower magnitude of completeness. Moment tensor (MT) inversion have been employed to develop a catalog of earthquake source solutions including moment tensor and finite source rupture area. This resulted in a 168-event waveform-based seismic moment tensor catalog for events 0.7≤Mw≤3.9 based on a semi-automated moment tensor analysis technique. The MT solutions were used for in-situ stress estimation during the EGS injection phases. We found an approximate 15-degree counterclockwise rotation of the least compressive stress sigma-3, and a rotation of the maximum compressive stress sigma-1 toward the vertical as the injected volume of water increased. We developed a rupture-area magnitude scaling relationship of The Geysers earthquakes obtained from finite-source inversion for fault slip that reveals a very high correlation to published results. Based on the rupture area-magnitude relationship we mapped Mw from the LBNL earthquake catalog to estimate the activated fracture area, its location and orientation within the injection volume of Prati-32. The double-difference Wadati (DDW) technique was applied to high-accuracy P- and S-wave differential travel times derived from waveform cross correlation to image fluid saturation in the subsurface based on Vp/Vs-ratio. The recently developed technique has been applied to estimate temporal changes of fluid distribution in fault zones and in volcanic regions but not yet in engineered geo-reservoirs. The improvement of the technology includes high-resolution waveform cross correlation applied to the high-quality datasets with lower magnitude of completeness resulting in a higher number of recorded events. The improvement allowed to estimate both, the spatial and temporal distribution of injected fluids and the state of the injected fluid during reservoir stimulation. Results of the DDW analysis revealed the development of an injection-derived steam plume during the early injection phase, particularly in the northern part of the Prati-32 study area.

15 GEOTHERMAL ENERGY↗

Do large and small earthquakes start alike? Rupture determinism and earthquake early warning

Do earthquakes large and small begin alike, or are there systematic differences in the nucleation or early rupture process that determine the final earthquake size? This question of rupture determinism is one of the outstanding challenges in earthquake physics, and has significant implications for earthquake early warning systems, which rely on the ability to rapidly estimate earthquake magnitudes based on small snapshots of waveform data. Here I examine the question of rupture determinism using a massive dataset of strong-motion acceleration waveforms from M4-9 earthquakes occurring near Japan over the past 20 years. We analyze the time-evolution of peak displacement from more than 130,000 strong-motion waveforms, finding no significant evidence for deterministic rupture in our dataset. We then use these observations to develop a rigorous Bayesian framework for calculating time-dependent uncertainties in earthquake early warning systems like ShakeAlert, which is currently undergoing testing for the western United States. This work forms the basis for future data-driven studies designed to improve methodology used real-time geophysical hazard assessments.

58 GEOSCIENCES↗

Characterizing Photometric Flicker

The focus of this study is simply to report on the commercial availability and performance of emerging flicker meters. Commercial-meter measurements and calculations were compared against those generated by a photoelectric characterization system developed by the Pacific Northwest National Laboratory (PNNL). The results and analysis show that the three commercially available flicker meters evaluated for this study measured light-intensity waveforms and calculated essential flicker-performance characteristics and metrics similarly, both to each other and to the reference meter chosen as an accuracy benchmark. Some differences in performance were found, however, when measurements were taken of light-intensity waveforms with significant high-frequency content – greater than the dominant 120 Hz found in many products at full output. Such conditions may be found in light sources that employ pulse-width modulation to achieve their target light output (e.g., intensity or color). If the meter was not appropriately configured (e.g., sampling frequency was too low), or if proper configuration was not possible given meter constraints (e.g., maximum number of available data points), then the waveform characteristics were not accurately captured, often resulting in the calculation of flicker metrics that deviated significantly from the reference.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Applications of seismic threshold monitoring at an Antarctic glacier [Slides]

Geophysical waveforms collected from Arctic regions provides data required for monitoring global security targets. Some seismic instruments deployed on Scandinavian and Greenlandic sites provide waveform data, but these installations include specific challenges: Sea ice and glacial ice generates waveform interference; Meltwater generates narrowband interference, resembling tonal noise.

58 GEOSCIENCES↗

Open Source Fault-tolerant Grid Frequency Measurement for Solar Inverters

The Discrete Fourier transform (DFT) based measurement algorithms are one of the most common measurement algorithms for grid parameter estimation such as rms, phase angle, frequency. Over the past few years, many DFT based algorithms have been developed to enhance its measurement accuracy under steady-state and/or dynamic grid conditions. For example, an adaptive band-pass filter utilizing exponential modulation filter has been proposed to reduce measurement errors at the presence of large frequency deviations. Measurement accuracy of different algorithms including FIR filter, extended Kalman filtering (EKF), and enhanced DFT method have been compared in detail under different grid conditions. Two artificial signals that have 90-degree phase difference were constructed by the Clarke transformation to address the frequency spectrum leakage of DFT. A multi-module approach was developed to enhance both steady-state and dynamic measurement accuracies, in which each module was developed to eliminate some specific errors. Besides DFT-based measurement algorithms, some signal model-based algorithms have been developed to further improve the accuracy under dynamic conditions. However, a key drawback of the state-of-the-art algorithms is that they cannot perform measurements accurately during system transient faults. In the Blue Cut Fire event, there was a phase angle jump of about 26 degrees in the voltage waveform during the transient fault. The phase angle jump fault will cause waveform discontinuity, and these algorithms will fail to provide reliable measurements during this period because they typically assume the waveform to be measured is continuous, no matter what method (DFT, PLL, EKF, FIR, or Taylor WLS) is used for estimation. In fact, the measurement errors during the system transient faults like phase-jump is not required in the IEEE Standard. As a result, although a measurement instrument can pass the strict IEEE Standard, it could still be the source of the problem in the future if we have similar system transient faults, which could happen again. Therefore, developing the fault-tolerant measurement technology is the key to solve the problem.

14 SOLAR ENERGY↗

SRF Cavity Fault Classification Using Machine Learning At CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator to make use of SRF accelerating structures. The structures are configured in two anti-parallel linacs connected by arcs. Each linac consists of twenty C20/C50 cryomodules each containing eight 5-cell cavities and five C100 upgrade cryomodules each containing eight 7-cell cavities. Accurately classifying the source of cavity faults is critical for improving accelerator performance. A cavity fault triggers a waveform acquisition process where 17 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the affected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. This data combined with archived signals sampled at 10 Hz are used to classify faults. Significant time is required for a subject matter expert to analyze and identify the intra-cavity signatures of imminent faults. This paper describes a path forward that utilizes machine learning for automatic fault classification. Post-training identification of the physical origins of faults are discussed, as are potential machine-trained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance

Solopova, A.↗

A case study of denoising microseismic data in the Farnsworth, TX CO 2 EOR site

As geologic carbon sequestration projects begin to be funded with a higher degree of frequency, microseismic monitoring will become more necessary to establish caprock integrity and induced seismicity risk. The Farnsworth, TX site, which hosts enhanced oil recovery operations, provides an ideal laboratory to test essential components of microseismic monitoring such as surface station placement, denoising, and different autopicker methods. We find that despite careful optimization, the surface station placement at the Farnsworth site was insufficient given the high level of background noise from the industrial operations at the oil field. Thus, we use only borehole geophone data in our processing. Two denoising techniques were examined: the DeepDenoiser and the continuous wavelet transform. The continuous wavelet transform was shown to be a valuable tool in converting between raw waveforms to processed denoised waveforms for microseismic monitoring. In the case of the noisy waveforms, only ten detections are found, compared with 90 in the denoised data for a two-hour window. The DeepDenoiser suffered from the fact that the training data was regional earthquake data. In addition, the PhaseNet machine-learning autopicker was applied to both the noisy and the denoised data, and this algorithm detected thousands of more arrivals in the data denoised with the continuous wavelet transform technique.

58 GEOSCIENCES↗

Collaborative Research: Properties and Dynamics of the Shallow Crust (Final Report)

Ground motions recorded at one location are often extrapolated to nearby regions within a given radius. Here, we explore the appropriateness of spatial extrapolation using data from seven small aperture seismic network deployments in southern California. Six of these deployments are linear arrays of 4-13 stations, and one is a 2D array of 13 stations at Pinyon Flats Observatory. The spatial footprint array diameters are 3 km or less, and each array was operational for a year or more. From our base catalog (M2.5+ earthquakes; 4038 events; September 2010 - June 2023), automated methods remove temporally overprinted waveforms from nearby events (< 5 km) in quick succession (< 5 min) and data with nonviable waveforms. These 200 samples per second data are filtered at 0.5-25 Hz and must have signal-tonoise ratios (SNRs) of 2.5+. Peak ground acceleration (PGA) and peak ground velocity (PGV) are derived individually from the maximum absolute values of each of the 3-component waveforms (vertical, northsouth, and east-west). Five of the seven arrays traverse the San Jacinto fault, and two do not. Ground motion observations are compared with theoretical estimates from Abrahamson et al., 2014. On average, arrays deployed within and across fault zones consistently record ground motions above theoretical expectations, whereas off-fault arrays record ground motions at or slightly below theoretical expectations. We attribute these differences to site conditions because these trends prevail for the full data suites. For each network and each individual channel, the coefficient of variation indicates that the standard deviations are ~30±6% of the mean. Exploring relative ground motion contributions from all three channels (ternary plots), as expected, most data show that vertical ground motions are attenuated compared to horizontal ones. However, this is not always the case for RA array data, where vertical motions can be ~2-3 times larger than horizontal motions for select events near Cahuilla, CA. These anomalously high vertical motions are focal mechanism-related. These results suggest that ternary plots created using only a small amount of data can be used as a data quality metric and a tool to find anomalous features in three-component data.

58 GEOSCIENCES↗