Automated Pattern Recognition Methods to Identify Nuclear Explosions
Award last edited on: 1/8/2020

Sponsored Program
Awarding Agency
Total Award Amount
Award Phase
Solicitation Topic Code
Principal Investigator
Hyrum Laney

Company Information

Acorn Science & Innovation Inc (AKA: AcornSI)

1487 Chain Bridge Road | Suite 200
McLean, VA 22101
   (703) 995-9872
Location: Single
Congr. District: 08
County: Arlington

Phase I

Contract Number: HDTRA119P0025
Start Date: 3/19/2019    Completed: 10/18/2019
Phase I year
Phase I Amount
Reliable automated pattern recognition in the current system is limited by excessive noise and clutter combined with insufficient and/or ambiguous features. Our team consisting of AcornSI and Leidos thus propose a multi-step approach based on the combined effects of improved detection, feature extraction, phase identification, and global association.A key innovation here is creating a new classification feature and confidence based on machine vision to directly classify waveform spectrograms.Improved legacy and new features are then sent to a (possibly nested) support vector machine (SVM) to automatically determine event classifications with associated confidence scores.machine learning,classification,deep convolutional neural network (DCNN),nuclear explosion,Support Vector Machine (SVM),feature extraction,seismic hydroacoustic infrasound (SHI)

Phase II

Contract Number: HDATA220C0014
Start Date: 9/15/2020    Completed: 9/14/2022
Phase II year
Phase II Amount
We propose a multi-step approach to event classification using machine learning based on convolutional neural networks for noise reduction, improved phase picking and combined new and legacy features.