The broader impact/commercial potential of this Partnerships for Innovation - Research Partnerships (PFI-RP) project aims to assist in humanitarian, scientific, and defense endeavors by automatically detecting buried explosives using a sensor-fused smart unmanned aircraft. Inconsistent buried explosives detection techniques have made remediation slow and stymied agricultural development in countries most impacted by minefields. The proposed technology offers an opportunity to decrease the cost of buried explosives detection by employing multiple sensors on a single platform and reducing the number of operators needed to conduct the automated aerial survey. Educational impacts of this project include the entrepreneurial training of the project’s PhD student, and local scientific outreach to underrepresented communities through hands-on demonstrations of the project’s technology prototype to inspire interest in geophysics, machine learning, and engineering.
This project will further the geophysical understanding of the near-surface (0-2 meters) by providing a method for quickly evaluating which set of instruments will yield the highest buried target identification accuracy. The unmanned aircraft will produce a composite inversion of a field area using machine learning. Optimally combining data from several geophysical instruments through machine learning can increase the confidence of inferences. Insights from this project may help improve inferences based on multiple, complementary geophysical datasets across the geosciences. Using multiple machine learning algorithms in the research and development portion of this project will help inform future applications. This project will help bridge the gap of geophysical data collection and inversions in a real-time and environment-adaptable way. Beyond the scope of buried explosives detection, this project’s technology platform and deep learning package will enable end-users to better differentiate what geologic and man-made materials are present in the subsurface using geophysical data, especially in environments where ground-based fieldwork is costly or hazardous.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
