Date of Award
6-2020
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Department of Electrical and Computer Engineering
First Advisor
Brett J. Borghetti, PhD
Abstract
Seismic signal processing at the IDC is critical to global security, facilitating the detection and identification of covert nuclear tests in near-real time. This dissertation details three research studies providing substantial enhancements to this pipeline. Study 1 focuses on signal detection, employing a TCN architecture directly against raw real-time data streams and effecting a 4 dB increase in detector sensitivity over the latest operational methods. Study 2 focuses on both event association and source discrimination, utilizing a TCN-based triplet network to extract source-specific features from three-component seismograms, and providing both a complimentary validation measure for event association and a one-shot classifier for template-based source discrimination. Finally, Study 3 focuses on event localization, and employs a TCN architecture against three-component seismograms in order to confidently predict backazimuth angle and provide a three-fold increase in usable picks over traditional polarization analysis.
AFIT Designator
AFIT-ENG-DS-20-J-004
DTIC Accession Number
AD1104459
Recommended Citation
Dickey, Joshua T., "Neural Network Models for Nuclear Treaty Monitoring: Enhancing the Seismic Signal Pipeline with Deep Temporal Convolution" (2020). Theses and Dissertations. 3630.
https://scholar.afit.edu/etd/3630