I’m working with a team trying to track where permafrost is thawing underneath infrastructure. The surface underneath a lot of runways and railways in arctic areas is really heterogeneous. There are some patches that are frozen solid permafrost, and other patches that are thawed and kind of squishy. If a vibration is sent through the surface, it will travel at different speeds depending on whether the permafrost is solid or thawed. So we’re developing a system using low-cost fiber-optics as sensors to detect vibrations that are traveling through the earth, say, vibrations due to cars driving along a road or airplanes on a runway. We interpret all of those random vibrations and extract coherent signals that help us create a map of the surface underneath the ground. From an ICME perspective, we’ve had to develop fast algorithms to more efficiently process the dense vibration data we are getting from our sensors, because we want to be able to continuously process new data and detect new changes in the permafrost quickly without requiring huge amounts of computing resources. Eventually this process could help manage infrastructure in arctic areas: A technician could see from his or her desktop computer whether the ground is stable in many different locations along a road or runway. If the road is unstable, they will know and be able reinforce the ground before it gives way and the road crumbles.
Story originally published on May 2016
A lot of the work I do is motivated by geoscience examples.