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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Reply to “Comment on ‘The Reduced Detection Rate of Signals That Are Hidden by Earthquakes: Case Studies with Spotlight Detectors That Operate at Seismic Arrays,’ by Joshua D. Carmichael, Brent G. Delbridge, and Richard Alfaro-Diaz” by Paul G. Richards

We reply to a comment that Paul G. Richards (Richards, 2026) directed to an article by Carmichael et al. (2025). Richards expressed concern over two issues. The first issue relates to the size of the explosions discussed in the article. The second relates to the probability that earthquakes and explosions sourced near the same location coincide in time, by chance. We do not address the first issue because the original article does not assign significance to the size of the explosions from the point of view of an experimental party. We do respond to Richards’ comment on the second issue and claim that it can be explained through a clarification about conditional versus joint probabilities. Our present discussion, therefore, describes the difference between the conditional probability that a correlator fails, given that an explosion and earthquake coincidently occur in the same place, and the joint probability that a correlator fails while an earthquake and explosion also coincidentally occur in the same place. This latter, joint probability appears to be rare. We clarify that our original article did not treat the joint probability, only the conditional probability.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

A Data-Driven Framework for Automated Detection of Aircraft-Generated Signals in Seismic Array Data Using Machine Learning

Abstract Ground motions associated with aircraft overflights can cover a significant portion of the seismic data collected by shallowly emplaced seismometers, such as new nodal and Distributed Acoustic Sensing systems. This article describes the first published framework for automated detection of aircraft on single channel and multichannel seismic data. The seismic data are converted to spectrograms in a sliding time window and classified as aircraft or nonaircraft in each window using a deep convolutional neural network trained with analyst-labeled data. A majority voting scheme is used to convert the output from the sequence of sliding time windows onto a decision time sequence for each channel and to combine the binary classifications on the decision time sequences across multiple channels. Precision, recall, and F-score are used to quantify the detection performance of the algorithm on nodal data using fourfold time-series cross validation. By applying our framework to data from the Sage Brush Flats nodal array in Southern California, we provide a benchmark performance and demonstrate the advantage of using an array of sensors.

Geochemistry & Geophysics↗
DOE OSTI2022

Distinguishing Unique Earthquakes with Overlapping Signals in Oklahoma

Abstract During routine operations monitoring Oklahoma earthquakes, we found that certain earthquakes occurred closely both in space and time and had overlapping phases at the recording stations. Through further scrutiny and analysis, we determined that rather than being distinctly different earthquakes, some of the earthquakes exhibited multiphase arrivals and longer than expected coda due to unique ray paths that encounter impedance contrasts such as at the sedimentary rock-basement. Of course, some of these events truly were distinct events, which we term overlapping earthquakes, for which perceived coda duration overlaps and obscures the phase arrivals of the second event due to the source proximity in both time and space. We detail our classification scheme to separate the local earthquakes in Oklahoma as single, overlapping earthquakes, or those associated with multiphase arrivals. We forward model seismic wave propagation in a 2D crustal model and develop a methodology that utilizes waveform correlation to distinguish phases from overlapping earthquakes to those from crustal reverberations. Duration analysis shows a more elongated duration, qualitatively similar to the duration produced by overlapping earthquakes, at the sites where multiphase arrivals are observed.

Geochemistry & Geophysics↗
DOE OSTI2020

Characterizing Acoustic Signals in the Buildup to a Magnet Quench

Superconducting magnets are needed to provide the powerful magnetic fields that direct particles on curved paths at the world’s most powerful accelerators. Imperfections or natural limitations can cause the magnet to lose its superconductivity. The transition to a non-superconducting state is called a quench; it is quite costly, but its exact reasons are still poorly understood. Learning more about the buildup to a quench may help us improve our understanding of the drivers of superconducting magnet performance.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Collaboration with USACyS for Signals Manipulation

Savannah River National Laboratory has been investing into cybersecurity capabilities since 2019. Cybersecurity has been a rapidly growing area for the CSRA in general and has included the consolidation of Cyber and Electronic Warfare units at Fort Gordon. This consolidation has been driven by the convergence of cyberwarfare and electronic warfare as the technologies underpinning Army Cyber Command’s vision of Information Warfare. This convergence, investment by SRNL into cybersecurity for critical infrastructure, and the desire for collaboration between the Army and SRNL has resulted in an opportunity for SRNL to develop R&D capabilities in electronic warfare that will complement SRNL’s cybersecurity efforts. Discussions between SRNL and the U.S. Army Cyber School (USACyS) has identified a general gap in ICS training lab capability in which both entities could contribute to solve. It is proposed to design and architecture that meets this need.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Application Note: Mixed Signal Simulation with Xyce™ 7.1

This application note describes how Release 7.1 of the Xyce circuit simulator can be coupled with external simulators via either a Python-based interface that leverages the Python ctypes foreign function library or via the Verilog Procedural Interface (VPI). It also documents the usage of these interfaces on RHEL7 with Python 2.6 or 2.7. These interfaces are still under development and may change in the future. So, a key purpose of this application note is to solicit feedback on these interfaces from both internal Sandia Xyce users and other performers on the DARPA Posh Open Source Hardware (POSH) program.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

nEXO Passive Signal Chain

The Next Enriched Xenon Observatory (nEXO) primary mission goal is to detect neutrinoless double beta decay, proving neutrinos are Majorana type fermions. Neutrinoless double beta decay (0vBB) is a theoretical type of radioactive decay in which two electrons and no neutrinos are emitted.

42 ENGINEERING↗
DOE OSTI2021

Signal Preconditioning to Minimize Impulse Response Contribution

A study was performed to identify a method to minimize the effect of a linear time-invariant (LTI) system impulse response on an input. Three methods were studied: Wiener filter, the N4SID algorithm and transfer function estimation, the latter two using functions from MATLAB’s System Identification Toolbox. Although all three methods were able estimate an unknown forward impulse response given an input/output time series pair, only the Wiener filter was able to estimate a system inverse which satisfactorily solved the problem using a cosine similarity measure.

42 ENGINEERING↗
DOE OSTI2021

Convolutional Neural Networks for Signal Detection

Currently, traditional methods such as short-term average/long-term average (STA/LTA) are used to detect arrivals in three-component seismic waveform data. Accurately establishing the identity and arrival of these waves is helpful in detecting and locating seismic events. Convolutional Neural Networks (CNNs) have been shown to significantly improve performance at local distances. This work will expand the use of CNNs to more remote distances and lower magnitudes. Sandia National Labs (SNL) will explore the advantages and limits of a particular approach and investigate requirements for expanding this technique to different types, distances, and magnitudes of events in the future. The team will describe detailed performance results of this method tuned on a curated dataset from Utah with its expert-defined arrival picks.

58 GEOSCIENCES↗