Global Nuclear Explosion Discrimination Using a Convolutional Neural Network
Abstract 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 Compre...
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| Format: | Article |
| Language: | English |
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Wiley
2023-09-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2022GL101528 |
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| author | Louisa Barama Jesse Williams Andrew V. Newman Zhigang Peng |
| author_facet | Louisa Barama Jesse Williams Andrew V. Newman Zhigang Peng |
| author_sort | Louisa Barama |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-2d3fdb5699164d0e9cff2c42d180af96 |
| institution | DOAJ |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-2d3fdb5699164d0e9cff2c42d180af962025-08-20T03:02:10ZengWileyGeophysical Research Letters0094-82761944-80072023-09-015017n/an/a10.1029/2022GL101528Global Nuclear Explosion Discrimination Using a Convolutional Neural NetworkLouisa Barama0Jesse Williams1Andrew V. Newman2Zhigang Peng3School of Earth and Atmospheric Sciences Georgia Institute of Technology GA Atlanta USAGlobal Technology Connection, Inc. GA Atlanta USASchool of Earth and Atmospheric Sciences Georgia Institute of Technology GA Atlanta USASchool of Earth and Atmospheric Sciences Georgia Institute of Technology GA Atlanta USAAbstract 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.https://doi.org/10.1029/2022GL101528seismologymachine learningglobal discriminationnuclear blast |
| spellingShingle | Louisa Barama Jesse Williams Andrew V. Newman Zhigang Peng Global Nuclear Explosion Discrimination Using a Convolutional Neural Network Geophysical Research Letters seismology machine learning global discrimination nuclear blast |
| title | Global Nuclear Explosion Discrimination Using a Convolutional Neural Network |
| title_full | Global Nuclear Explosion Discrimination Using a Convolutional Neural Network |
| title_fullStr | Global Nuclear Explosion Discrimination Using a Convolutional Neural Network |
| title_full_unstemmed | Global Nuclear Explosion Discrimination Using a Convolutional Neural Network |
| title_short | Global Nuclear Explosion Discrimination Using a Convolutional Neural Network |
| title_sort | global nuclear explosion discrimination using a convolutional neural network |
| topic | seismology machine learning global discrimination nuclear blast |
| url | https://doi.org/10.1029/2022GL101528 |
| work_keys_str_mv | AT louisabarama globalnuclearexplosiondiscriminationusingaconvolutionalneuralnetwork AT jessewilliams globalnuclearexplosiondiscriminationusingaconvolutionalneuralnetwork AT andrewvnewman globalnuclearexplosiondiscriminationusingaconvolutionalneuralnetwork AT zhigangpeng globalnuclearexplosiondiscriminationusingaconvolutionalneuralnetwork |