Autonomous Oceanographic Sampling Designs Using Excursion Sets for Multivariate Gaussian random fields
Jo Eidsvik (NTNU)
Thursday 18th June, 2020 14:00-15:00 Zoom
Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime management. Faced with limited resources to understand processes in the water-column, the combination of statistics and autonomous robotics provides new opportunities for experimental designs. In this work we develop methods for efficient spatial sampling applied to the mapping of coastal processes by providing informative descriptions of spatial characteristics of ocean phenomena. Specifically, we define a design criterion based on improved characterization of the uncertainty in the excursions of vector-valued Gaussian random fields, and derive tractable expressions for the expected Bernoulli variance reduction in such a framework. We demonstrate how this criterion can be used to prioritize sampling efforts at locations that are ambiguous, making exploration more effective. We use simulations to study the properties of methods and to compare them with state-of-the-art approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a case study mapping the boundary of a river plume. The results demonstrate the potential of combining statistical methods and robotic platforms to effectively inform and execute data-driven environmental sampling.