Monitoring Ice Sheets with UAV-borne Ice-penetrating Radar..

Mon, 24 Oct 2022 08:42:26 -0400

Full Title:

Monitoring Ice Sheets with UAV-borne Ice-penetrating Radar Systems and Physics-informed Machine Learning

Date: Wednesday, October 26

Time: 10:00 a.m. - 11:00 a.m. PDT

Registration: https://hai.stanford.edu/events/thomas-teisberg-monitoring-ice-sheets-uav-borne-ice-penetrating-radar-systems-and-physics

Abstract:

The Antarctic Ice Sheet will play a growing role in sea level rise over the next century, but models of sea level contributions from the vast ice sheet carry far larger uncertainty estimates than other major contributing sources. A number of factors contribute to this uncertainty, all of which can be traced back to a sparsity of data. The Antarctic Ice Sheet is nearly 50% larger than the United States in area and holds ice equivalent to over 60 meters of global sea level rise. Satellite observations have turned the surfaces of Earth’s ice sheets into data-rich environments, yet the subsurface environments remain sparsely observed.

Multiple potential positive-feedback processes have been proposed that could dramatically alter our predictions of the future of the ice sheet. These hypotheses are difficult to test because the processes would likely not have occurred in the relatively short direct span of direct observations available to us, but, in many cases, we also lack the observational infrastructure to identify these processes beginning today. Even well-established processes still carry large uncertainties in how they will impact the ice sheet due to the poor spatial and temporal resolution of data available.

Our group explores this problem from two fronts: using robotics to enable more widespread data collection and leveraging data-based approaches to maximize the value of data collected. Uncrewed aerial vehicles (UAVs) carrying ice-penetrating radar instruments hold the potential to dramatically expand sub-surface data collection by reducing the cost, logistical complexity, and safety risks associated with current approaches. At the same time, the scale of the problem is so vast that it is critical to consider how we can optimize the deployment of resources to maximize the value of the collected data. This requires a move towards data-driven approaches to understanding the behavior of the ice sheet