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A Deep Learning Approach for Enhanced Discrimination of Nuclear Weapons Testing

Award Information
Agency: Department of Defense
Branch: Defense Threat Reduction Agency
Contract: HDTRA119P0026
Agency Tracking Number: T182-005-0049
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DTRA182-005
Solicitation Number: 18.2
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-04-08
Award End Date (Contract End Date): 2019-11-07
Small Business Information
7474 Greenway Center Drive Suite 600
Greenbelt, MD 20715
United States
DUNS: 112935437
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Hafidh Ghalib
 Vice President, Advanced Technology Division
 (321) 752-4985
Business Contact
 Robert Deegan
Phone: (240) 472-4120
Research Institution

Seismic, hydro acoustic, and infrasonic (SHI) analyses possess many desirable properties, such as long-distance wave propagation and attributable spectral characteristics. They have been, by large, relegated to capturing massive explosions, earthquakes, bolides, and space-bound rocket launches. Although machine learning techniques have been employed on various military and commercial projects, rarely has there been an attempt to marry the adaptive capabilities of the machine learning algorithms with all three sources that surround events of interest. Our revolutionary approach, proposes to develop augmented machine learning techniques that inherently leverage the existence of all three sensor data types. The inclusion of each data type is of utmost importance, where event characterization and development of propagation models have clearly demonstrated the impact of source types, atmospheric conditions, and terrain geology on acquired SHI waveforms, respectively. This is contrast to most contemporary methods that look at one or at most a few features or discriminants to make a decision on source type, based on physical modeling or observational. Our approach utilizes both, the sensor data type when available or perform without it for a strictly automated solution. The innovative technical merits we propose have real value that is direct applicable to support DTRA’s mission to

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