Trends in Nuclear Explosion Monitoring Research &amp; Development - A Physics Perspective

Maceira, Mónica; Blom, Philip; MacCarthy, J.; Marcillo, Omar; Euler, G. G.; Begnaud, M. L.; Ford, S. R.; Pasyanos, Michael E.; Orris, Gregory J.; Foxe, Michael; Arrowsmith, Stephen J.; Merchant, Bion J.; Slinkard, Megan Elizabeth.

doi:10.2172/1355758

Cited by 17 publications

(9 citation statements)

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“…We found we retained the most data of energy filter threshold greater than 5 as well as model performance with data filtered between 1 Hz and 5 Hz using a Butterworth filter. Maceira et al (2017) showed that seismo-acoustic wave amplitude -yield relationship can vary and depends on many factors such as emplacement geologic conditions and depth. To address such amplitude variations, we applied a signal normalization function to the data before feeding them into the CNN classifier.…”

Section: Datamentioning

confidence: 99%

“…Robust global nuclear blast detection is essential for international regulatory bodies such as the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO) and the Air Force Technical Applications Center (AFTAC) to monitor the near-worldwide moratorium on nuclear explosive testing and eventually to verify the compliance of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Although the CTBTO uses a variety of data including continuous seismic, acoustic, hydro-acoustic signals, and radionuclide data, seismic methods have proved to be the most robust and rapid for the purpose of detecting, locating and discriminating underground nuclear blasts from natural earthquakes and other near surface seismic signals (Maceira et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Improved and continued development of nuclear blast detection using seismic methods is imperative given that underground nuclear blast testing is the most likely scenario for all future testing as it allows for control of radioactive explosive products (Maceira et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Global Nuclear Blast Discrimination using a Convolutional Neural Network

Barama

Williams

Newman

et al. 2022

Preprint

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Using P-wave seismograms by Barama et al. (2022), we trained a seismic source classifier using a Convolutional Neural Network. We trained for three classes: earthquake P-wave, nuclear P-wave, and noise. Seismograms with low signal to noise ratios (SNR) adversely affect the model performance, thus a threshold was applied, limiting the training set size. Our method can accurately characterize most events, finding over 95\% signals in the validation set, even with the SNR-limited training data. We applied the model on holdout datasets of the North Korean test blasts to evaluate the performance on unique region and station-source pairs. Additionally, we tested on the Source Physics Experiment events to investigate the potential for chemical blasts to act as a surrogate for nuclear blasts. We anticipate that machine-learning models like our classifier system can have broad application for other seismic signals including volcanic and non-volcanic tremor, anomalous earthquakes, ice-quakes or landslide-quakes.

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global Nuclear Blast Discrimination using a Convolutional Neural Network

Barama

Williams

Newman

et al. 2022

Preprint

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“…The purpose of seismic data monitoring in nuclear explosion monitoring is to accurately and reliably detect seismic or explosion events from ambient noises. This processing mainly involves detection, association, location, discrimination, and magnitude/yield estimation [2]. The IMS of CTBTO includes 50 primary seismic stations and 120 auxiliary seismic stations, most of which record seismic signals by the seismic array.…”

Section: Introductionmentioning

confidence: 99%

GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition

Hei

Cui

et al. 2021

Applied Sciences

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The purpose of seismic data processing in nuclear explosion monitoring is to accurately and reliably detect seismic or explosion events from complex ambient noises. Accurate detection and identification of seismic phases are of great significance to the detection and parameter estimation of seismic events. In seismic phase identification, discriminating between noise signals and real seismic signals is essential. Accurate identification of noise signals helps reduce false detections, improves the accuracy of automatic bulletins, and relieves the workload of analysts. At the same time, in seismic exploration, the prime objective in data processing is also to enhance the signal and suppress the noises. In this study, we combined a generative adversarial network (GAN) with a long short-term memory network (LSTM) to discriminate between noise and phases in seismic waveforms recorded by the International Monitoring System (IMS) array MKAR. First, using the beamforming data of the array as the input, we obtained the signal features of seismic phases through the learning of the GAN discriminator network. Then, we input these features and trained the joint network on mixed seismic phase and noise data, and successfully classified seismic phases and noise signals with a recall of 95.28% and 97.64%, respectively. Based on this model, we established a real-time data processing method, then validated the effectiveness of this method with real 2019 data of MKAR. We also verified whether improved noise signal identification improves the quality of phase association and event detection.

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“…Robust global underground nuclear explosion (UNE) detection is essential for international regulatory bodies such as the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO) and the Air Force Technical Applications Center (AFTAC) to monitor the near-worldwide cessation of nuclear explosive testing and eventually to verify the compliance of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Although the CTBTO uses a variety of data including continuous seismic, acoustic, hydro-acoustic signals, and radionuclide data, seismic methods have proved to be the most robust and rapid for the purpose of detecting, locating and discriminating UNEs from natural earthquakes and other near surface seismic signals (Maceira et al, 2017). Improved and continued development of underground explosion detection using seismic methods is imperative given that UNE testing is the most likely scenario for all future testing as it allows for control of radioactive explosive products (Maceira et al, 2017).Existing methods for seismic detection and discrimination include beamforming, template matching, waveform autocorrelation, ratio of body to surface wave magnitudes, and P to S wave amplitude ratio calculations (Bowers…”

mentioning

confidence: 99%

Global Nuclear Explosion Discrimination Using a Convolutional Neural Network

Barama,

Williams,

Newman

et al. 2023

Geophysical Research Letters

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Using P‐wave seismograms, we trained a seismic source classifier using a Convolutional Neural Network. We trained for three classes: earthquake P‐wave, underground nuclear explosion (UNE) P‐wave, and noise. With the current absence of nuclear testing by countries that have signed the Comprehensive Test Ban Treaty, high quality seismic data from UNEs is limited. Even with limited training data, our model can accurately characterize most events recorded at regional and teleseismic distances, finding over 95% signals in the validation set. We applied the model on holdout datasets of the North Korean test explosions to evaluate the performance on unique region and station‐source pairs, with promising results. Additionally, we tested on the Source Physics Experiment events to investigate the potential for chemical explosions to act as a surrogate for nuclear explosions. We anticipate that machine‐learning models like our classifier system can have broad application for other seismic signals including volcanic and non‐volcanic tremor, anomalous earthquakes, ice‐quakes or landslide‐quakes.

show abstract

Trends in Nuclear Explosion Monitoring Research & Development - A Physics Perspective

Cited by 17 publications

References 0 publications

Global Nuclear Blast Discrimination using a Convolutional Neural Network

Global Nuclear Blast Discrimination using a Convolutional Neural Network

GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition

Global Nuclear Explosion Discrimination Using a Convolutional Neural Network

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