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Machine Learning Methods to Catalog Sources from Diverse, Widely Distributed Sensors

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9453-19-P-0685
Agency Tracking Number: F19A-012-0202
Amount: $149,811.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF19A-T012
Solicitation Number: 19.A
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-07-17
Award End Date (Contract End Date): 2020-07-17
Small Business Information
148 Middle St Suite 1D
Portland, ME 04101
United States
DUNS: 962583956
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Caryl Johnson
 Chief Innovation Officer
 (207) 699-4017
Business Contact
 Kay Aikin
Phone: (207) 699-4051
Research Institution
 Southern Research
 Dr. Seth Cohen Dr. Seth Cohen
757 Tom Martin Drive
Birmingham, AL 35211
United States

 (205) 581-2649
 Domestic nonprofit research organization

The efficient detection and understanding of clandestine nuclear device testing is critical to the security and credibility of the organizations currently tasked with this important work. At present, the Air Force Tactical Applications Center (AFTAC) and likewise, the Comprehensive Test Ban Treaty Organization (CTBTO) are running methods and algorithms that are out of date. These approaches use a complex Export-Transform-Load (ETL) that is focused on a central database, constituting a single point of failure as well as being a restriction to increasing the quantity and quality of the analytical effort. th Detecting and understanding of clandestine nuclear device testing is a problem that has been around for a very long time. Most of the work in this area has been based on physical attributes as revealed in the structure of seismic waves propagating through the earth. What advancement can be made in a system that has been established for more than half a century? The opportunity is to apply machine learning (ML) to improve the analysis of seismic waves. This proposal will build off a proven seismic processing system that provides adaptable streaming analytics and eventually into a Distributed HPC Seismic System fully integrated with today’s and future machine learning.

* Information listed above is at the time of submission. *

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