Speaker
Thomas Chen
(Academy for Mathematics, Science, and Engineering)
Description
Machine learning algorithms have preliminarily been shown to have the potential to predict volcanic eruptions by training on muon data. The high-energy particles are used to map the interior of the volcano, chiefly due to muon's relatively high mass. In this poster, we discuss opportunities in this area. Challenges include curating the large-scale datasets that are necessary for deep learning applications like this one. We show intercomparisons between muography methods and traditional techniques like seismicity, deformation, and gas emission in terms of prediction efficacy.
Author
Thomas Chen
(Academy for Mathematics, Science, and Engineering)
