Machine learning for autonomous field data collection

This project will make a significant contribution toward the support of future workers in geology. Understanding how geologists reason, plan to collect new data, consider three-dimensional spatial relations, and evaluate uncertainty are critically important for supporting scientists working on applied problems, such as natural resource exploration. This project will enhance existing efforts in geology to collect data using robot drones. Drones allow access to important areas of the world too dangerous to access in person and not visible from satellite or plane. The project will use machine learning to incorporate expert knowledge into drone flights to support effective autonomous data collection. The data will yield improved geological understanding of an important fault system. Findings from the project will improve understanding of uncertainty in volumes and thus improve our understanding of earthquakes and the analyses of petroleum workers. Understanding how expert geologists reason will support new exploration and mapping strategies for human-robot teams working in natural environments. The collaborative efforts of the interdisciplinary team will advance the fields of cognitive science, geology, and machine learning. The integration of cognitive science, robotics, and geology will develop new approaches to field work with human-autonomous systems teams that are faster and more effective than any either human or autonomous system would be acting alone.