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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Lunar Seismic Detector to Advance the Search for Strange Quark Matter

Detection of small seismic signals on the Moon are needed to study lunar internal structure and to detect possible signals from Strange Quark m&er transit events. The immediate objective is to create a prototype seismic detector using a tunnel diode oscillator with a variable capacitor attached to a proof mass. The device is designed to operate effectively on the Moon, which requires a low power consumption to operate through lunar night, while preserving sensitivity. The goal is capacitance resolution of better than 1 part in 10' and power consumption of less than 1 watt.

Galitzki, Nicholas B.↗
DOE OSTI2023

SPC-70783 Rev 0 Seismic Detector and Switch Survey and Verification Specification

A. Idaho National Laboratory (INL) is developing a microreactor to produce electrical power utilizing a small nuclear core and Stirling engines under the Microreactor Application Research Validation and Evaluation (MARVEL) program. The MARVEL microreactor Reactor Protection System (RPS) will have a seismic trip function. The selected part for this design is ETNA2 manufactured by Kinemetrics, Inc. B. Acceptance of the items delivered under this scope of work can be performed through a direct evaluation of the supplier’s controls and a factory acceptance test.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2020

A Multi-Instance learning Framework for Seismic Detectors

In this report, we construct and test a framework for fusing the predictions of a ensemble of seismic wave detectors. The framework is drawn from multi-instance learning and is meant to improve the predictive skill of the ensemble beyond that of the individual detectors. We show how the framework allows the use of multiple features derived from the seismogram to detect seismic wave arrivals, as well as how it allows only the most informative features to be retained in the ensemble. The computational cost of the "ensembling" method is linear in the size of the ensemble, allowing a scalable method for monitoring multiple features/transformations of a seismogram. The framework is tested on teleseismic and regional p-wave arrivals at the IMS (International Monitoring System) station in Warramunga, NT, Australia and the PNSU station in University of Utah's monitoring network.

58 GEOSCIENCES↗
NASA NTRS2006

Using the Moon As A Low-Noise Seismic Detector For Strange Quark Nuggets

Strange quark matter made of up, down and strange quarks has been postulated by Witten [1]. Strange quark matter would be nearly charge neutral and would have density of nuclear matter (10(exp 14) gm/cu cm). Witten also suggested that nuggets of strange quark matter, or strange quark nuggets (SQNs), could have formed shortly after the Big Bang, and that they would be viable candidates for cold dark matter. As suggested by de Rujula and Glashow [2], an SQN may pass through a celestial body releasing detectable seismic energy along a straight line. The Moon, being much quieter seismically than the Earth, would be a favorable place to search for such events. We review previous searches for SQNs to illustrate the parameter space explored by using the Moon as a low-noise detector of SQNs. We also discuss possible detection schemes using a single seismometer, and using an International Lunar Seismic Network.

moon↗
NASA NTRS1982

Scanning Seismic Intrusion Detector

Scanning seismic intrusion detector employs array of automatically or manually scanned sensors to determine approximate location of intruder. Automatic-scanning feature enables one operator to tend system of many sensors. Typical sensors used with new system are moving-coil seismic pickups. Detector finds uses in industrial security systems.

Lee, R. D.↗
DOE OSTI2018

Dynamic Tuning of Seismic Signal Detector Trigger Levels for Local Networks

The quality of automatic signal detections from sensor networks depends on individual detector trigger levels (TLs) from each sensor. The largely manual process of identifying effective TLs is painstaking and does not guarantee optimal configuration settings, yet achieving superior automatic detection of signals and ultimately, events, is closely related to these parameters. In this paper, we present a Dynamic Detector Tuning (DDT) system that automatically adjusts effective TL settings for signal detectors to the current state of the environment by leveraging cooperation within a local neighborhood of network sensors. After a stabilization period, the DDT algorithm can adapt in near-real time to changing conditions and automatically tune a signal detector to identify (detect) signals from only events of interest. Our current work focuses on reducing false signal detections early in the seismic signal processing pipeline, which leads to fewer false events and has a significant impact on reducing analyst time and effort. This system provides an important new method to automatically tune detector TLs for a network of sensors and is applicable to both existing sensor performance boosting and new sensor deployment. Finally, with ground truth on detections from a local neighborhood of seismic sensors within a network monitoring the Mount Erebus volcano in Antarctica, we show that DDT reduces the number of false detections by 18% and the number of missed detections by 11% when compared with optimal fixed TLs for all sensors.

58 GEOSCIENCES↗
DOE OSTI2023

Moment tensor reconstruction

A seismic monitoring system includes a plurality of seismic monitors and a processing device operatively coupled to the plurality of seismic monitors. The processing device receives recordings of waveforms of motion detected at the plurality of seismic detectors in a geographic area. The processing device applies the respective recordings to corresponding positions of the seismic detectors in a three-dimensional geological model that describes its elastic attributes and tests a plurality of moment tensors at a plurality of locations. Based on the testing, the processing device determines a globally convergent source location and moment tensor in the three-dimensional model based on the testing.

Petrov, Petr↗
DOE OSTI2021

The Modeled Performance of the ORNL Matched Filter Detector for Seismic Signatures of Multi-Axle Tractor Trailers

The MINOS program seeks methods to passively monitor the transfer of special materials, which are reproducible and quantitatively defensible. To date, we have demonstrated that short-period seismometers deployed near vehicle tracks can detect and monitor signatures of multi-axle tractor trailers impacting small road obstacles as means to detect and identify some transfer activities. In this report, we model the seismic velocity signal output from a tractor-trailer impacting a small obstacle (a template waveform) to parameterize a matched filter that identifies signals with similar shapes buried in noise, and that indicate repeating events (target waveforms). We then use this model to perform thousands of synthetic experiments, in which we apply our detector to identify noisy waveforms output from a similarly modeled vehicle data set; we process all data in the same manner as we process real data collected from the Oak Ridge National Laboratory (ORNL) seismic network. Our experiments, while limited, reveal that the matched filter shows a high discrimination capability, and at very low SNR values (at visibility thresholds). This means that template waveforms can match target waveforms that are sourced by models that share the same vehicle speed (target vehicles), and have a low probability of mis-identification. In particular, a matched filter designed to detect a five-axle tractor-trailer will reliably identify that target vehicle down to an SNR of 11.25 dB, and within ±0.1 km/hr, while successfully screening target vehicles with three axles.

42 ENGINEERING↗
DOE OSTI2016

Threshold magnitudes for a multichannel correlation detector in background seismicity

Colocated explosive sources often produce correlated seismic waveforms. Multichannel correlation detectors identify these signals by scanning template waveforms recorded from known reference events against "target" data to find similar waveforms. This screening problem is challenged at thresholds required to monitor smaller explosions, often because non-target signals falsely trigger such detectors. Therefore, it is generally unclear what thresholds will reliably identify a target explosion while screening non-target background seismicity. Here, we estimate threshold magnitudes for hypothetical explosions located at the North Korean nuclear test site over six months of 2010, by processing International Monitoring System (IMS) array data with a multichannel waveform correlation detector. Our method (1) accounts for low amplitude background seismicity that falsely triggers correlation detectors but is unidentifiable with conventional power beams, (2) adapts to diurnally variable noise levels and (3) uses source-receiver reciprocity concepts to estimate thresholds for explosions spatially separated from the template source. Furthermore, we find that underground explosions with body wave magnitudes m b = 1.66 are detectable at the IMS array USRK with probability 0.99, when using template waveforms consisting only of P -waves, without false alarms. We conservatively find that these thresholds also increase by up to a magnitude unit for sources located 4 km or more from the Feb.12, 2013 announced nuclear test.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Deep Learning Models for Planetary Seismicity Detection

Research in planetary seismology is fundamentally constrained by a lack of data. Seismo-logical science products of future missions can typically only be informed by theoretical signal/noise characteristics of the environment or likely Earth-analogues. Although objectives can be re-assessed after some initial data-collection upon lander arrival, transfer of high-resolution data back to Earth is costly on lander power usage. Over the last several years, development of GPU computing techniques and open-source high-level APIs have led to rapid advances in deep learning within the fields of computer vision, natural language processing, and collaborative filtering. These techniques are actively being adapted in seismology for a variety of tasks, including: earthquake detection, seismic phase discrimination, and ground-motion prediction. Until the recent detection of mars quakes during the Mars InSight mission, the only other measurements of seismicity recorded outside of Earth was on the Moon during the Apollo missions between 1969 to 1977. These unique data sets have been periodically revisited using new seismological methods, including ambient noise interferometry and Hidden Markov Models. Our objective is to develop a deep learning seismic detector and use it to catalog moonquakes from the Apollo 17 Lunar Seismic Profiling Experiment (LSPE) and compare the results with those obtained by other methods. Additionally, we will assess the accuracy tradeoff between using a training set of lunar data and one composed of Earth seismicity. In this document, we present preliminary results using a prototype classifier trained on a small set of earthquakes that was able to obtain detections for LSPE moonquakes with a greater accuracy than a recent study using Hidden Markov Models.

Civilini, F.↗
DOE OSTI2022

A Wrapper to Use a Machine-Learning-Based Algorithm for Earthquake Monitoring

Seismology is one of the main sciences used to monitor volcanic activity worldwide. Fast, efficient, and accurate seismicity detectors are crucial to assess the activity level of a volcano in near–real time and to issue timely warnings. Traditional real–time seismic processing software uses phase onset pickers followed by a phase association algorithm to declare an event and estimate its location. The pickers typically do not identify whether the detected phase is a P or S arrival, which can have a negative impact on hypocentral location quality and complicates phase association. We implemented the deep–neural–network–based method PhaseNet to identify in real time P and S seismic waves on data from one– and three–component seismometers. We tuned the Earthworm binder_ew associator module to use the phase identification from PhaseNet to detect and locate the events, which we archive in a SeisComP3 database. We assessed the performance of the algorithm by comparing the results with existing catalogs built to monitor seismic and volcanic activity in Mayotte and the Lesser Antilles region. Our algorithm, which we refer to as PhaseWorm, showed promising results in both contexts and clearly outperformed the previous automatic method implemented in Mayotte. As a result, this innovative real–time processing system is now operational for seismicity monitoring in Mayotte and Martinique.

58 GEOSCIENCES↗
DOE OSTI2015

Predicting the liquefaction phenomena from shear velocity profiling: Empirical approach to 6.3 Mw, May 2006 Yogyakarta earthquake

The liquefactions phenomena have been reported after a shocking 6.5Mw earthquake hit Yogyakarta province in the morning at 27 May 2006. Several researchers have reported the damage, casualties, and soil failure due to the quake, including the mapping and analyzing the liquefaction phenomena. Most of them based on SPT test. The study try to draw the liquefaction susceptibility by means the shear velocity profiling using modified Multichannel Analysis of Surface Waves (MASW). This paper is a preliminary report by using only several measured MASW points. The study built 8-channel seismic data logger with 4.5 Hz geophones for this purpose. Several different offsets used to record the high and low frequencies of surface waves. The phase-velocity diagrams were stacked in the frequency domain rather than in time domain, for a clearer and easier dispersion curve picking. All codes are implementing in Matlab. From these procedures, shear velocity profiling was collected beneath each geophone’s spread. By mapping the minimum depth of shallow water table, calculating PGA with soil classification, using empirical formula for saturated soil weight from shear velocity profile, and calculating CRR and CSR at every depth, the liquefaction characteristic can be identify in every layer. From several acquired data, a liquefiable potential at some depth below water table was obtained.

58 GEOSCIENCES↗
DOE OSTI2016

Mobile seismic exploration

Laser-Doppler-Vibrometry (LDV) is an established technique to measure vibrations in technical systems with picometer vibration-amplitude resolution. Especially good sensitivity and resolution can be achieved at an infrared wavelength of 1550 nm. High-resolution vibration measurements are possible over more than 100 m distance. This advancement of the LDV technique enables new applications. The detection of seismic waves is an application which has not been investigated so far because seismic waves outside laboratory scales are usually analyzed at low frequencies between approximately 1 Hz and 250 Hz and require velocity resolutions in the range below 1 nm/s/√Hz. Thermal displacements and air turbulence have critical influences to LDV measurements at this low-frequency range leading to noise levels of several 100 nm/√Hz. Commonly seismic waves are measured with highly sensitive inertial sensors (geophones or Micro Electro-Mechanical Sensors (MEMS)). Approaching a laser geophone based on LDV technique is the topic of this paper. We have assembled an actively vibration-isolated optical table in a minivan which provides a hole in its underbody. The laser-beam of an infrared LDV assembled on the optical table impinges the ground below the car through the hole. A reference geophone has detected remaining vibrations on the table. We present the results from the first successful experimental demonstration of contactless detection of seismic waves from a movable vehicle with a LDV as laser geophone.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Assessing the Effectiveness of Generalized Likelihood Ratio Test Detector Schemes in Seismic Event Detection and the Avoidance of Nontarget Signals

Cross-correlation techniques have played a long-standing and pivotal role in seismic event monitoring. However, the performance of correlation-based detectors is challenged by nuisance seismicity, or nontarget signals. Such detections are a problem when the mission is to automatically map events to the correct source region. Using aftershocks of the 2014 $M_w$ 6.0 South Napa, California, earthquake, we demonstrate the effectiveness of utilizing a dynamic correlation processor framework in a generalized likelihood ratio test (GLRT) detector configuration to minimize nontarget detections. A GLRT maximizes a detection statistic with respect to one or more unknown parameters. In this case, the detection statistic is a template signal match against the waveform in a window sliding over a data stream, and the unknown parameter is an index variable indicating group membership of the template event or events. Detected events are assigned to the event group that yields the largest detection statistic. In this work, our results show that a GLRT detector will outperform a suite of independently operating correlation and subspace detectors in terms of having a lower nontarget detection rate at a given missed detection rate. We also show that a GLRT detector composed of a few high-rank subspace detectors has a slightly higher nontarget detection rate, but a significantly lower missed detection rate, than a GLRT detector composed of many low-rank subspace detectors. The high-rank GLRT configuration produced impressive results even with marginal data (single channel, single station, and very low time bandwidth product), which bodes well for the utility of building efficient aftershock classification systems and global monitoring systems at larger scales. However, future work is required to assess performance at the regional scale and to assess the performance of the system at detecting target events not used in the detector template creation.

58 GEOSCIENCES↗
DOE OSTI2018

Sensitivity of Seismically Cued Antineutrino Detectors to Nuclear Explosions

Here, we evaluate the sensitivity of large gadolinium-doped water detectors to antineutrinos released by nuclear-fission explosions, using updated signal and background models and taking advantage of the capacity for seismic observations to provide an analysis trigger. Under certain realistic conditions, the antineutrino signature of a 250-kton pure-fission explosion could be identified several hundred kilometers away, in a detector about the size of the largest module currently proposed for a basic physics experiment. In principle, such an observation could provide rapid confirmation that the seismic signal coincided with a fission event, possibly useful for international monitoring of nuclear-weapon tests. We discuss the limited potential for seismically cued antineutrino observations to constrain fission yield, differentiate pure fission from fusion-enhanced weapon tests, indicate that the seismic evidence of an explosion had been intentionally masked, or verify the absence of explosive testing in a targeted area. We conclude that advances in seismic monitoring and neutrino physics have made the detection of explosion-derived antineutrinos more conceivable than previously asserted, but the size and cost of sufficiently sensitive detectors continue to limit applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1978

Intruder Detector

The shadowy prowler is attempting a break-in, unaware that his presence has already been detected and reported by the device in the lower left corner of the photo. It is part of a three-element ntruder Detecti on System developed by NASA's Ames Research Center from technology acquired in the Apollo lunar exploration program. Apollo astronauts left behind on the moon small portable seismic (shock) detectors to record subsurface vibrations and transmit to Earth data on the moon's density and thickness. A similar seismic detector is the key component of the lntruder Detection System. Encased in a stainless steel tube, the detector is implanted in the ground outside the facility being protected-home, bank, industrial or other facilities. The vibration-sensing detector picks up the footstep of anyone within a preset range. The detector is connected by cable to the transmitter, which relays the warning to a portable radio receiver. The radio alerts plant guards or home occupants by emitting an audible tone burst for each footstep.

Source record↗