Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling
This work addresses the challenge of efficient environmental monitoring for marine science, though it is incremental in combining existing statistical and robotic methods.
The paper tackled the problem of optimizing oceanographic sampling by developing spatial sampling methods to map regions where multiple ocean variables exceed thresholds, using autonomous underwater vehicles. The results demonstrated effective mapping of a river plume boundary through field deployments.
Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime resource management. Faced with limited resources in understanding processes in the water-column, the combination of statistics and autonomous systems provide new opportunities for experimental design. In this work we develop efficient spatial sampling methods for characterizing regions defined by simultaneous exceedances above prescribed thresholds of several responses, with an application focus on mapping coastal ocean phenomena based on temperature and salinity measurements. Specifically, we define a design criterion based on uncertainty in the excursions of vector-valued Gaussian random fields, and derive tractable expressions for the expected integrated 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 and compare properties of the considered approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a 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.