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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Effect of Baseline Period on Quantification of Climate Extremes over the United States

Extreme weather events, including heatwaves, heavy precipitation, and drought, have a large impact on society through human health, destruction of infrastructure, ecological change, and economic losses. Reanalyses such as NASA’s Modern Era Retrospective Analysis, version 2 (MERRA-2) are a valuable tool for analyzing past extreme events to determine their underlying causes and how extremes have changed over the past four decades. The detection of extreme events relies on a threshold for precipitation or temperature that is derived using a reference period, which can then be used to determine how extreme an event was or what the return period is. Operational centers typically use a 30-year climatology period that shifts in time every ten years, while the World Meteorological Organization suggests that the maximum amount of data should be included for the detection of extreme events due to their rare occurrence. As global and regional climate continues to change, the interpretation of extreme events is reliant on the baseline period that is used for the underlying thresholds and can be a source of uncertainty for the policy making community. Three baseline periods – 1981-2010, 1991-2020, and 1981-2020 – will be used to compute percentiles of temperature and precipitation across the contiguous United States and will then by employed to determine monthly indices representing heatwaves, cold spells, and extreme precipitation events. A spatial and temporal analysis of the resulting extreme weather indices will demonstrate the appropriateness for each baseline period for the evaluation of extreme events.

Extremes↗
DOE OSTI2021

Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high–quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear–Test–Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. Here, we compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.

58 GEOSCIENCES↗
DOE OSTI2020

Detection Limits and Near‐Field Ground Motions of Fast and Slow Earthquakes

Abstract We investigate theoretical limits to detection of fast and slow seismic events, and spatial variations of ground motion expected from M 6 earthquakes at short epicentral distances. The analyses are based on synthetic velocity seismograms calculated with the discrete wavenumber method assuming seismic velocities and attenuation properties of the crust in Southern California. The examined source properties include different magnitudes ( M −1.0 to M 6.0), static stress drops (0.1–10 MPa), and slow and fast ruptures (0.1–0.9 of shear wave velocity). For the M 6 events we also consider variations in rise times producing crack‐ and pulse‐type events and different rupture directivities. Slow events produce ground motion with considerably lower amplitude than corresponding regular fast earthquakes with the same magnitude, and hence are significantly more difficult to detect. The static stress drop and slip rise time also affect the maximum radiated seismic motion, and hence event detectability. Apart from geometrical factors, the saturation and depletion of seismic ground motion at short epicentral distances stem from radiation pattern, earthquake size (magnitude, stress drop), and rupture directivity. The rupture velocity, rise time, and directivity affect significantly the spatial pattern of the ground motions. The results can help optimizing detection of slow and fast small earthquakes and understand the spatial distribution of ground motion generated by large events.

58 GEOSCIENCES↗
NASA NTRS2011

Radiation-Tolerant Intelligent Memory Stack - RTIMS

This innovation provides reconfigurable circuitry and 2-Gb of error-corrected or 1-Gb of triple-redundant digital memory in a small package. RTIMS uses circuit stacking of heterogeneous components and radiation shielding technologies. A reprogrammable field-programmable gate array (FPGA), six synchronous dynamic random access memories, linear regulator, and the radiation mitigation circuits are stacked into a module of 42.7 42.7 13 mm. Triple module redundancy, current limiting, configuration scrubbing, and single- event function interrupt detection are employed to mitigate radiation effects. The novel self-scrubbing and single event functional interrupt (SEFI) detection allows a relatively soft FPGA to become radiation tolerant without external scrubbing and monitoring hardware

Ng, Tak-kwong↗
NASA NTRSDate not supplied

A Comparison of Stratospheric Smoke Events Seen by SAGE II (1984-2005) and SAGE III (2017-2021)

The Stratospheric Aerosol and Gas Experiment (SAGE II) operated for over 20 years (1984-2005) and collected near global observations of aerosol extinction coefficient from the upper troposphere to about40 km. SAGE III has flown aboard the International Space Station (ISS) since 2017 and has collected comparable aerosol measurements during this period. The SAGE II record is dominated by recovery from El Chichón (1982) and the Pinatubo eruption of 1991 with a few smaller volcanic events that are detectable in the data set. In addition, a number of pyrocumulus events injected smoke into the lower stratosphere with magnitudes that are comparable to the smaller volcanic events. The shorter SAGE III/ISS record is qualitatively different than SAGE II’s with a number of small to moderate volcanic eruptions in both low and high latitudes (e.g., Ambae in 2018 and Raikoke in 2020) as well several smokeevents including the two largest events seen by SAGE-like instruments: the BC pyrocumulus event in 2017 and the Australian brush fires of 2020/2021. In this paper, I will review the detection and quantification of the magnitude of these smoke events with a particular focus on the differences between the two data periods.

SAGE↗
NASA NTRSDate not supplied

Quantifying SAGE II (1984-2005) and SAGE III/ISS (2017-2022) Observations of Smoke in the Stratosphere

The Stratospheric Aerosol and Gas Experiment (SAGE II) operated for over 20 years (1984-2005) and collected near global observations of aerosol extinction coefficient from the upper troposphere to about 40 km. SAGE III has flown aboard the International Space Station (ISS) since 2017 and has collected comparable aerosol measurements during this period. The SAGE II record is dominated by recovery from El Chichón (1982) and the Pinatubo eruption of 1991 with a few smaller volcanic events that are detectable in the data set. In addition, a number of pyrocumulus events injected smoke into the lower stratosphere with magnitudes that are comparable to the smaller volcanic events. The shorter SAGE III/ISS record is qualitatively different than SAGE II’s with a number of small to moderate volcanic eruptions in both low and high latitudes (e.g., Ambae in 2018 and Raikoke in 2020) as well several smoke events including the two largest events seen by SAGE-like instruments: the BC pyrocumulus event in 2017 and the Australian brush fires of 2020/2021. In this paper, I will review the detection and quantification of the magnitude of these smoke events with a particular focus on the differences between the two data periods.

Smoke↗
NASA NTRSDate not supplied

Quantifying SAGE II (1984-2005) and SAGE III/ISS (2017-2022) Observations of Smoke in the Stratosphere

The Stratospheric Aerosol and Gas Experiment (SAGE II) operated for over 20 years (1984-2005) and collected near global observations of aerosol extinction coefficient from the upper troposphere to about 40 km. SAGE III has flown aboard the International Space Station (ISS) since 2017 and has collected comparable aerosol measurements during this period. The SAGE II record is dominated by recovery from El Chichón (1982) and the Pinatubo eruption of 1991 with a few smaller volcanic events that are detectable in the data set. In addition, a number of pyrocumulus events injected smoke into the lower stratosphere with magnitudes that are comparable to the smaller volcanic events. The shorter SAGE III/ISS record is qualitatively different than SAGE II’s with a number of small to moderate volcanic eruptions in both low and high latitudes (e.g., Ambae in 2018 and Raikoke in 2020) as well several smoke events including the two largest events seen by SAGE-like instruments: the BC pyrocumulus event in 2017 and the Australian brush fires of 2020/2021. In this talk, I will review the detection and quantification of the magnitude of these smoke events with a particular focus on the differences between the two data periods.

Smoke Stratosphere SAGE↗
NASA NTRSDate not supplied

Quantifying SAGE II (1984-2005) and SAGE III/ISS (2017-2022) Observations of Smoke in the Stratosphere

The Stratospheric Aerosol and Gas Experiment (SAGE II) operated for over 20 years (1984-2005) and collected near global observations of aerosol extinction coefficient from the upper troposphere to about 40 km. SAGE III has flown aboard the International Space Station (ISS) since 2017 and has collected comparable aerosol measurements during this period. The SAGE II record is dominated by recovery from El Chichón (1982) and the Pinatubo eruption of 1991 with a few smaller volcanic events that are detectable in the data set. In addition, a number of pyrocumulus events injected smoke into the lower stratosphere with magnitudes that are comparable to the smaller volcanic events. The shorter SAGE III/ISS record is qualitatively different than SAGE II’s with a number of small to moderate volcanic eruptions in both low and high latitudes (e.g., Ambae in 2018 and Raikoke in 2020) as well several smoke events including the two largest events seen by SAGE-like instruments: the BC pyrocumulus event in 2017 and the Australian brush fires of 2020/2021. In this talk, I will review the detection and quantification of the magnitude of these smoke events with a particular focus on the differences between the two data periods.

SAGE↗
DOE OSTI2021

Listening for the Landing: Seismic Detections of Perseverance's Arrival at Mars With InSight

The entry, descent, and landing (EDL) sequence of NASA's Mars 2020 Perseverance Rover will act as a seismic source of known temporal and spatial localization. We evaluate whether the signals produced by this event will be detectable by the InSight lander (3,452 km away), comparing expected signal amplitudes to noise levels at the instrument. Modeling is undertaken to predict the propagation of the acoustic signal (purely in the atmosphere), the seismoacoustic signal (atmosphere-to-ground coupled), and the elastodynamic seismic signal (in the ground only). Our results suggest that the acoustic and seismoacoustic signals, produced by the atmospheric shock wave from the EDL, are unlikely to be detectable due to the pattern of winds in the martian atmosphere and the weak air-to-ground coupling, respectively. However, the elastodynamic seismic signal produced by the impact of the spacecraft's cruise balance masses on the surface may be detected by InSight. The upper and lower bounds on predicted ground velocity at InSight are 2.0 × 10 -14 and 1.3 × 10 -10 m s -1 . The upper value is above the noise floor at the time of landing 40% of the time on average. The large range of possible values reflects uncertainties in the current understanding of impact-generated seismic waves and their subsequent propagation and attenuation through Mars. Uncertainty in the detectability also stems from the indeterminate instrument noise level at the time of this future event. A positive detection would be of enormous value in constraining the seismic properties of Mars, and in improving our understanding of impact-generated seismic waves.

58 GEOSCIENCES↗
DOE OSTI2025

Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up

The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of ground-based GW detectors. Within this context, the radio band of the EM spectrum presents unique challenges. Sensitive radio facilities capable of detecting the faint radio afterglow seen in GW170817, and with sufficient angular resolution, have small fields of view compared to typical GW localization areas. Additionally, theoretical models predict that the radio emission from binary neutron star mergers can evolve over weeks to years, necessitating long-term monitoring to probe the physics of the various postmerger ejecta components. These constraints, combined with limited radio observing resources, make the development of more coordinated follow-up strategies essential—especially as the next generation of GW detectors promises a dramatic increase in detection rates. Here, we present RADAR, a framework designed to address these challenges by promoting community-driven information sharing, federated data analysis, and system resilience, while integrating AI methods for both GW signal identification and radio data aggregation. We show that it is possible to preserve data rights while sharing models that can help design and/or update follow-up strategies. We demonstrate our approach through a case study of GW170817, and discuss future directions for refinement and broader application.

Gravitational waves↗
NASA NTRS1972

Location of the electron acceleration region in solar flares.

Observations of impulsive solar flare X rays (energy greater than 10 keV) by the OGO-5 satellite and the measurements of energetic solar electrons made with the Explorer-35 and Explorer-41 (IMP-5) satellites during the period March 1968-September 1969 have been analyzed in order to determine the ion density in the X-ray source region as well as the location of the electron acceleration region in the solar atmosphere. The ion density in the X-ray source region varies from event to event and lies between 1 and 100 billion ions per cu cm for those events in which the impulsive X-ray emission could be detected; for those events in which no impulsive emission was detected above threshold, the ion density in the X-ray source was less than one billion ions per cu cm. At least in some small solar flares, the region where the electrons are accelerated during the flash phase is located in the lower corona.

Kane, S. R.↗
DOE OSTI2024

Hls4ml Synthesis Testing

HLS4ml (high level synthesis for machine learning) Is a Python package used to translate commonly used open-source machine learning models into HLS. This is useful in machine learning applications on FPGAs. Machine learning algorithms are only as fast as the hardware that they are used on, and some applications require high speed without sacrificing accuracy. In these situations, an FPGA is a good choice since it is faster than a CPU or a GPU, but programming an FPGA is difficult. This is where HLS4ml can be used to simplify the process, as a well-known learning model can be converted to HLS and more easily deployed onto an FPGA. There are many use cases for a machine learning algorithm running on an FPGA. For example, detectors in a particle accelerator cannot keep every event that they detect, and so a computer must decide which events to keep and which to discard. Using an FPGA with a machine learning algorithm would be a good way to keep as many events as possible.

Swanson, Caiden↗
DOE OSTI2024

hls4ml

hls4ml (high level synthesis for machine learning) Is a Python package used to translate commonly used open-source machine learning models into HLS. This is useful in machine learning applications on FPGAs. Machine learning algorithms are only as fast as the hardware that they are used on, and some applications require high speed without sacrificing accuracy. In these situations, an FPGA is a good choice since it is faster than a CPU or a GPU, but programming an FPGA is difficult. This is where hls4ml can be used to simplify the process, as a well-known learning model can be converted to HLS and more easily deployed onto an FPGA. There are many use cases for a machine learning algorithm running on an FPGA. For example, detectors in a particle accelerator cannot keep every event that they detect, and so a computer must decide which events to keep and which to discard. Using an FPGA with a machine learning algorithm would be a good way to keep as many events as possible.

Swanson, Caiden↗
DOE OSTI2020

Power System Event Classification and Localization Using a Convolutional Neural Network

Detection and timely identification of power system disturbances are essential for situation awareness and reliable electricity grid operation. Because records of actual events in the system are limited, ensemble simulation-based events are needed to provide adequate data for building event-detection models through deep learning; e.g., a convolutional neural network (CNN). An ensemble numerical simulation-based training data set have been generated through dynamic simulations performed on the Polish system with various types of faults in different locations. Such data augmentation is proven to be able to provide adequate data for deep learning. The synchronous generators’ frequency signals are used and encoded into images for developing and evaluating CNN models for classification of fault types and locations. With a time-domain stacked image set as the benchmark, two different time-series encoding approaches, i.e., wavelet decomposition-based frequency-domain stacking and polar coordinate system-based Gramian Angular Field (GAF) stacking, are also adopted to evaluate and compare the CNN model performance and applicability. The various encoding approaches are suitable for different fault types and spatial zonation. With optimized settings of the developed CNN models, the classification and localization accuracies can go beyond 84 and 91%, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Applying Waveform Correlation to Mining Blasts Using a Global Sparse Network

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment that uses waveform templates recorded by multiple International Monitoring System stations of the Comprehensive Nuclear-Test-Ban Treaty for up to 10 years prior to detect and identify mining blasts that occur during single weeks of study. We discuss approaches for template selection, threshold setting, and event detection that are specialized for mining blasts and a sparse, global network. We apply the approaches to two different weeks of study for each of two geographic regions, Wyoming and Scandinavia, to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be effective for operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to develop an intuition about potential reduction in analyst workload.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Analysis of the Source Physics Experiment Dry Alluvium Geology Ball Drop Sources

Two balls were dropped approximately 150 m down an open borehole as part of an investigation into the aftershocks from a 51 ton TNT-equivalent chemical explosion. The ball drop sources, a concrete-filled basketball and a bowling ball, were intended to mimic falling debris within an underground explosion borehole and cavity. Falling debris or sloughing of the borehole and cavity walls is thought to be one of the sources of explosion aftershocks. The signals from the ball drop sources were recorded by the same seismic network that monitored the aftershocks of the chemical explosion, so the ball drop recordings were used as templates to search through the continuous stream of aftershocks and detect correlated signals as a proportion of all aftershocks. The search resulted in only four similar event recordings out of the thousands of detected aftershocks. A synthetic test showed that the small, high-frequency ball drop templates were limited to producing highly correlated detections for events occurring in a small region around the impact location at 150 m depth. The high-frequency templates were necessary since the small ball drop signals were only above the noise at frequencies greater than 10 Hz. Unfortunately, the impact location is where the borehole was filled with sand in preparation for the subsequent chemical explosion in the test series, making it 150 m above the explosion created cavity centered at 300 m depth. This puts the ball drop templates 150 m away from the cavity, where many more events from falling debris would be expected. Further synthetic tests confirmed events emanating from the cavity 150 m below the impact site would have correlated with the lower frequency template and been detected had the template been usable below 10 Hz. Therefore, we conclude that the four detected events were most probably falling debris within the open borehole that impacted the floor at 150 m depth just as the ball drop sources had, and that this type of explosion aftershock mechanism comprises less than 1% of all explosion aftershocks.

58 GEOSCIENCES↗
NASA NTRS2024

Evaluating Precipitation Events Using GPM IMERG 30-Minute Near Real-Time Precipitation Estimates

Accurately detecting and estimating precipitation at near–real time (NRT) is of utmost importance for the early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), provides NRT 0.1° and 30-min precipitation estimates across the globe with only a 4-h latency. This study was an evaluation of the GPM IMERG version 6 level-3 early run 30-min precipitation product for precipitation events from 2014 through 2020. The purpose of this research was to identify when, where, and why GPM IMERG misidentified and failed to detect precipitation events in California, Nevada, Arizona, and Utah in the United States. Precipitation events were identified based on 15-min precipitation from gauges and 30-min precipitation from the IMERG multisatellite constellation. False-positive and false-negative precipitation events were identified and analyzed to determine their characteristics. Precipitation events identified by gauges had longer duration and had higher cumulative precipitation than those identified by GPM IMERG. GPM IMERG had many false event detections during the summer months, suggesting possible virga event detection, which is when precipitation falls from a cloud but evaporates before it reaches the ground. The frequency and timing of the merged passive microwave (PMW) product and forward propagation were responsible for IMERG overestimating cumulative precipitation during some precipitation events and underestimating others. This work can inform experts that are using the GPM IMERG NRT product to be mindful of situations where GPM IMERG–estimated precipitation events may not fully resolve the hydrometeorological conditions driving these hazards.

Jessica R P Sutton↗
DOE OSTI2020

Pre-Sinkhole Seismicity at the Napoleonville Salt Dome: Implications for Local Seismic Monitoring of Underground Caverns

The formation of a sinkhole at the Napoleonville salt dome located near Bayou Corne in Assumption Parish, Louisiana, was preceded by an increase in seismicity, as observed 10 km away by a continuously recording broadband seismic sensor. The seismicity rate in the days prior to the sinkhole formation contained hundreds of long-period events, some of which could be detected using a power detector (short-term to long-term energy ratio) tuned to the duration and frequency content of the seismicity. A characteristic of some of these events is a resonance near 0.4 Hz, which could be due to either an acoustic resonance within the brine-filled cavity or possibly due to unsteady fluid flow through a crack. In this work, a more exhaustive catalog was obtained using some of the events detected by the power detector as templates in a correlation detector. Furthermore, the full pre-sinkhole sequence of detections could offer lessons in remote monitoring of underground caverns.

58 GEOSCIENCES↗